gamfeldt et al 2008

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CONCEPTS & SYNTHESISEMPHASIZING NEW IDEAS TO STIMULATE RESEARCH IN ECOLOGY

Ecology, 89(5), 2008, pp. 12231231 2008 by the Ecological Society of America

MULTIPLE FUNCTIONS INCREASE THE IMPORTANCE OF BIODIVERSITYFOR OVERALL ECOSYSTEM FUNCTIONING

LARS GAMFELDT,1,2,3 HELMUT HILLEBRAND,2 AND PER R. JONSSON1

1Department of Marine Ecology, Goteborg University, Tjarno Marine Biological Laboratory, SE-452 96 Stromstad, Sweden2Institute for Botany, Aquatic Ecology, University of Cologne, Gyrhofstrasse 15, D-50931 Koln, Germany

Abstract. Biodiversity is proposed to be important for the rate of ecosystem functions.Most biodiversityecosystem function studies, however, consider only one response variable ata time, and even when multiple variables are examined they are analyzed separately. Thismeans that a very important aspect of biodiversity is overlooked: the possibility for differentspecies to carry out different functions at any one time. We propose a conceptual model toexplore the effects of species loss on overall ecosystem functioning, where overall functioningis defined as the joint effect of many ecosystem functions. We show that, due tomultifunctional complementarity among species, overall functioning is more susceptible tospecies loss than are single functions. Modeled relationships between species richness andoverall ecosystem functioning using five empirical data sets on monocultures reflected therange of effects of species loss on multiple functions predicted by the model. Furthermore, anexploration of the correlations across functions and the degree of redundancy within functionsrevealed that multifunctional redundancy was generally lower than single-function redun-dancy in these empirical data sets. We suggest that by shifting the focus to the variety offunctions maintained by a diversity of species, the full importance of biodiversity for thefunctioning of ecosystems can be uncovered. Our results are thus important for conservationand management of biota and ecosystem services.

Key words: bacteria; biodiversity; ecosystem functioning; multifunctionality; multiple functions; plants;redundancy; seagrass.

INTRODUCTION

The richness of species, functional groups, or geno-

types is an important aspect of biodiversity that governs

the magnitude and efficiency of ecosystem processes and

properties (Chapin et al. 1997). Additionally, biodiver-

sity is essential for providing goods and services to

human society and thus also has economic values.

Process rates in ecosystems, properties of ecosystems,

and goods and services derived from ecosystems haveoften been summarized as ecosystem functions. The

hypothesis that biodiversity is important for ecosystem

functions seems to have good general support (Hooper

et al. 2005, Balvanera et al. 2006). Many studies,

however, conclude that individual species are as efficient

performers of certain ecosystem processes as a more

diverse mix of species (e.g., Aarssen 1997, Huston 1997,

Wardle et al. 1997, Bruno et al. 2005, Hooper et al. 2005,

Cardinale et al. 2006). In fact, empirical studies show

that one or a few key species can dominate individual

ecosystem processes (e.g., Paine 2002, Bellwood et al.

2003, Solan et al. 2004). In addition, a small range of

species can often carry out the same individual

ecological process, resulting in a high degree of

ecological redundancy in natural and experimental

species assemblages (Walker 1992, Naeem 1998, but

see Loreau 2004).

Conceptually, as species richness increases, so does

the lowest possible level of any single ecosystem

function (Fig. 1a). Depending on the redundancy across

species for the function of interest, this increase can be

steep at high redundancy (solid curve) or flat at low

redundancy (gray curve). It is possible, however, that

high-performing species are present at all levels of

species richness, and hence system states above the solid

curve are hypothetically possible (gray area).

Within the extensive biodiversityecosystem function

(BEF) research over the past decade, the majority of

Manuscript received 18 December 2006; revised 22 August2007; accepted 11 September 2007. Corresponding Editor: J. B.Yavitt.

3 E-mail: [email protected]

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studies has used a single-function perspective, i.e.,

addressed the consequence of species loss on single

process rates or properties. The use of single response

variables as proxies for ecosystem functioning may

ignore other important ecosystem processes (Rosenfeld

2002a). Equating single functions with overall function-

ing can be highly misleading, especially if BEF research

ultimately aims to provide knowledge and advice to

management and conservation. Rather, individual func-tions and processes may be better viewed as components

of ecosystem functioning writ large. In this paper we

therefore propose that overall ecosystem functioning is

the joint effect of multiple ecosystem functions. We

believe that this is a valuable definition, which will aid in

the interpretation of the ecological consequences of

biodiversity loss.

The role of altered biodiversity for multifunctional

ecosystem functioning has so far only been considered in

a quantitative way in the recent study by Hector and

Bagchi (2007). They found that ecosystem multifunc-

tionality in experimental grasslands does require greater

numbers of species than do single functions. Otherwise,

when multiple functions have been examined (e.g.,

Tilman et al. 1997, Duffy et al. 2001, Downing 2005,

Spehn et al. 2005), they have not been considered

collectively. Duffy et al. (2003) discussed qualitatively

the role of individual species for different functions in

seagrass beds, and concluded that even when single

grazer species are most important for individual

ecosystem responses (i.e., there are sampling rather than

complementarity effects, see Loreau and Hector [2001]),

only mixtures of these grazers maximize multiple

ecosystem responses simultaneously. Furthermore, dif-

ferent species of marine macroalgae appear to differ in

their efficiency in using limiting resources, so that total

nitrogen use is higher in diverse assemblages than

predicted based on the uptake rates of the component

species (Bracken and Stachowicz 2006). As stated by

Rosenfeld (2002b), species are more likely to show

nonoverlapping functions in a multi-dimensional than in

a one-dimensional functional space. Accordingly, Petch-

ey and Gaston (2002) showed that species becomeincreasingly unique when many functions are added to a

multivariate index of functional diversity.

To examine the importance of diversity for the overall

functioning of ecosystems, it is important to analyze

quantitatively the effect of species loss on multiple

functions. We hypothesized that as several functions are

considered jointly, the importance of biodiversity will

become apparent, even though single functions do not

depend on a mixture of different species or groups. This

should hold true for different ecosystem types, sets of

organisms (or genotypes, functional groups, etc.), and

functions. To explore the importance of biodiversity for

multiple ecosystem functioning, we first present a simple

conceptual model. We then compare the outcome of this

model with independent empirical data sets, for which

we derive estimates for the consequences of species loss

on single and multiple ecosystem functions.

CONCEPTUAL MODEL

Overall functioning is the joint effect of multiple

constituent functions considered in any one system, and

cannot be expressed as the average of those functions.

Our conceptual model rests on the logical but important

premise that a decline in one function cannot be

FIG. 1. (a) The effects of species loss for hypothetical communities. The black line represents maximum loss in function if eachspecies lost is the most efficient species of the ones remaining in the community (as we move from high to low species richness). Thedarker gray area above this line represents all possible scenarios of species loss. The arrow and the gray line indicate that the impactof species loss on ecosystem function changes with changing redundancy. (b) The graph shows conceptually how the probability ofsustaining overall functioning depends on both the number of functions and the degree of multifunctional redundancy across

species. With some functional specialization, multiple functions will always be more susceptible to species loss than single functions(dashed line). This susceptibility will increase with decreasing redundancy. At the extreme case where all species can carry out onlyone function, the probability of sustaining overall functioning will be zero until S n, where the probability will instantaneouslyrise to a certain level. If all species play the exact same role for all functions, functioning will equal the response of one function.

LARS GAMFELDT ET AL.1224 Ecology, Vol. 89, No. 5


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compensated by an increase in another function. For

example, decreased primary productivity cannot be

compensated by an increase in nutrient uptake. We

therefore must define some level of each function that we

find acceptable. When one function drops beneath this

level, overall ecosystem functioning is no longer

sustained.

As an example, let us consider a system with Snumber of species and N number of functions, and look

at the probability of this system of sustaining overall

functioning in the face of random species loss. For any

one function, the probability of sustaining this function

increases with species richness due to sampling effects

(Aarsen 1997, Cardinale et al. 2006), and will asymp-

totically approach a maximum value, which we set as 1

(cf. Fig. 1a). The sampling effect means that the

probability of sampling functionally important species

increases with increasing number of species. At low

species richness, the probability of sustaining a function

will lie somewhere between 0 and 1 because there is a

certain probability of sampling those species that are

efficient for the function.

For multiple functions the probability also approach-

es 1 at high species richness, where enough species are

included that together perform all necessary functions,

i.e., ensure overall ecosystem functioning. As for single

functions, the response of overall functioning to species

loss depends on the level of redundancy (Fig. 1b).

However, multifunctional redundancy implies a positive

covariance of species traits across functions, i.e., the

ability of species to perform different functions has to be

positively correlated. In one extreme scenario, each

species is equally important for each function, in which

case functions will be perfectly correlated across species,

and the effect of species loss on overall ecosystem

functioning is then the same as for each single function

(Fig. 1b). At the other end, specialization among species

results in uncorrelated results or even negative correla-

tions. An extreme case is when each species can carry

out only one function and contributes nothing to the

other functions. Under such conditions fewer than n

number of species will have zero probability of

sustaining overall ecosystem functioning (Fig. 1b). This

scenario leads to little redundancy across functions,

which is potentially unrelated to redundancy within

functions.For real-world species assemblages, levels of redun-

dancy across multiple functions will fall somewhere

between these two extreme scenarios. Even if different

species may be equally important for one function,

redundancy across functions can be low, as different

species may sustain different functions. Therefore, the

important message from this conceptual model is that a

system is more likely to lose overall ecosystem function-

ing if species traits do not covary and different functions

reside on different species. At the same time, the

probability of losing ecosystem functioning increases

with the number of different functions considered (Fig.

1b). The arrows in Fig. 1b indicate how both the degree

of redundancy and the number of functions interact to

influence the probability of sustaining overall ecosystem

functioning in the face of biodiversity loss. We tested

this conceptual model using empirical data sets to (1)

test how the perceived effects of species loss might

change if more than one function is considered at the

same time, and (2) show how redundancy levels withinand across functions may be distributed for different

types of organisms and functions. Note that the

empirical data sets, although they already measure a

variety of functions, all cover only a subset of the

important processes and properties in ecosystems.

Accordingly, this subset of functions can be defined as

joint ecosystem functioning.

ANALYSIS OF EMPIRICAL DATA

Data sets

A survey of the literature revealed only a few studies

with information on multiple functions for a range ofmonocultures. There are indeed more published studies

that investigate multiple functions (e.g., Tilman et al.

1997, Spehn et al. 2005). It is, however, not possible

from those papers to extract information on species-

specific data for the functional performances of individ-

ual monocultures. Other studies (e.g., Gamfeldt et al.

2005) consider only low levels (23) of species richness

and functions. To examine our conceptual model, we

used five empirical data sets (referred to as the Plant

study, the Bacteria study, and Seagrass studies IIII; for

details see Table 1). From the two plant studies by

Palmborg et al. (2005) and Viketoft et al. (2005), which

are based on the same experiment, we collectedinformation on three functions from each study on the

same 12 plant species (Plants). In Jiang (2007) we found

three functions for four bacteria species (Bacteria).

From Duffy et al. (2001) we extracted information on

three grazer species and three functions (Seagrass I), and

from Duffy et al. (2005) on four grazers and four

functions. Duffy et al. (2005) included two different

scenarios for the effects of grazers (with and without a

predatory crab), which we refer to as separate experi-

ments in our study (without crabs [Seagrass II] and with

crabs [Seagrass III]). All three seagrass studies actually

presented more functions and response variables, but

some variables did not fit the broad definition of theword function, and some were indirect effects of other

functions. All functions examined in our analyses fit well

within the listed definitions of ecosystem functions and

processes in Giller et al. (2004).

Rationale

We based our analyses on monoculture data only and

provide a simple analysis of consequences of species loss,

which can be viewed as a best-case scenario, as we did

not include a variety of factors that might deteriorate

consequences of biodiversity loss. First, we constructed

mixtures from the monocultures in each data set and

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equalled the level of any function in a mixture with the

level of the best constituent species in monoculture.Using data from monocultures to address multispecies

performance is conservative, since it completely disre-

gards complementarity effects (enhanced performance in

mixes of species due to niche partitioning or facilitation),

and suggests that only selection effects are driving BEF.

We thus adopt the view that multispecies assemblages

outperform the average but not the best monoculture,

which emerged from a recent study comprising meta-

analysis across systems, trophic levels, and functions

(Cardinale et al. 2006).

Second, we use a 0.5 threshold for assessing a single

ecosystem function to be performed sufficiently within

an ecosystem. Thus, if the mixture contained a speciesperforming an ecosystem function at half the maximum

strength, we considered this to be sufficient for the

community to sustain that particular function. In the

absence of a general criterion for when ecologists

consider an ecosystem function at risk, we used a rather

low threshold, which in its analogy to EC50 (concentra-

tion giving 50% of maximum effect in ecotoxicology)

reflects another important outcome of the Cardinale et

al. (2006) meta-analysis: they found that for any single

function, on average one species was needed to keep a

certain process at ;60% of its maximum rate. Using 0.5

as a threshold thus maximizes the probability that in our

calculations, only one species will be needed for a single

function, and that calculated effects of species loss will

appear only on the level of joint ecosystem functioning.

The consequences of species loss on joint ecosystem

functioning would appear much more dramatic if a

threshold of 0.75 or 0.9 had been used.

Third, we do not consider temporal changes in the

environment. The number of species needed to sustain a

single function (and consequently joint ecosystem

functioning) will be much higher in temporally fluctu-

ating or unstable ecosystems, as different species might

perform a single function at different times.

Finally, the model further assumes that simply the

presence of a species performing an ecosystem functionat half the maximum strength is sufficient for the

community to sustain that particular function. Thus, we

make no assumptions on minimum densities required to

sustain ecosystem processes. Different extinction sce-

narios and types of density dependence may further

influence the exact relationship between diversity and

multiple ecosystem functioning, but probably less so

than for single functions (e.g., Solan et al. 2004). The

most common species may be least susceptible to

extinction and most important for one of the functions.

But other functions may be strongly affected by less

common species, and some processes may in fact be only

weakly coupled to overall density (e.g., predation andpollination). By not making any specific assumptions

about density dependence we emphasize the general

effect of biodiversity when considering multiple func-

tions collectively.

Calculation of consequences of species loss

We analyzed the effect of species on multiple

ecosystem functions by randomly deleting 1 to S 1

species from the species pool and calculating the

probability that the reduced community was still able

to perform all single functions considered at the 0.5

threshold. These calculations were based on the species-

specific information from the monocultures within the

five data sets. For all mixtures of species, each function

was assigned the function value of the best-performing

monoculture (as described in Rationale). For each data

set we performed three calculation steps.

The first step was to transform the multiple target

functions to a common scale. For each single function

we defined the proposed effect of diversity when

diversity enhances the function (e.g., grazer richness

increases grazer productivity and decreases algal bio-

mass [Cardinale et al. 2006]). For each function we

identified the best-performing monoculture and assigned

TABLE 1. Functions and correlations from the five empirical studies analyzed in the study.

StudyNo.

speciesSpecies

typeNo.

functions FunctionsRange of pairwise

function correlations

Plants (Palmborg et al. 2005,Viketoft et al. 2005)

12 plants 6 biomass production 0.80 to 0.69NO3 extractionNH4 extractionnematode plant feeder production

nematode fungal feeder productionnematode bacterial feeder production

Bacteria (Jiang 2007) 4 bacteria 3 biomass production 0.07 to 0.63consumer biomass productiondecomposition

Seagrass I (Duffy et al. 2001) 3 grazers 3 biomass production 0.84 to 0.87algae grazingeelgrass grazing

Seagrass II and III(Duffy et al. 2005)

4 grazers 4 biomass production no crabs:0.62 to 0.95macroalgae grazing with crabs: 0.55 to 0.94sediment algae grazingepiphyte production



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the process rate of this function a value of 1. The other

monoculture values were then expressed as ratios to the

best-performing species. If this ratio was negative we

assigned it a value of zero. The truncation at zero for

negative values prevented the artificial increase of low

function values when normalizing to the maximum

function value.

In a second step, we randomly deleted species fromthe assemblage and calculated the effects of species loss

on each single function, by analyzing if there was at least

one species remaining with a performance value .0.5,

which we had defined as a sufficient function level (see

Rationale).

In a final, third step, we considered whether joint

ecosystem functioning was sustained in the reduced

community for any combination of 1 to Nfunctions. We

considered this maintenance of joint ecosystem func-

tioning to be achieved when for each ecosystem function

at least one species was present in the remaining

community performing above the threshold of 0.5. The

probability of sustaining joint ecosystem functioning

was then calculated for each combination of remaining

species richness and number of functions considered.

We simulated random species loss for one to several

ecosystem functions using a simple computer sampling

routine. Sampling of each combination of species

richness and number of ecosystem functions was

repeated 10000 times (the maximum number of

combinations was 18 480). From the repeated samples

we calculated the probability of sustaining joint

ecosystem functioning for each combination of species

loss and number of ecosystem functions. All simulations

were performed with MATLAB 7 (MathWorks 2007)

for the Apple Mac OS X.

Calculation of redundancy

We also calculated relative estimates of redundancy

for each data set, both within functions and across

functions. We used pairwise correlations for all func-

tions across all species to calculate across-function

(multifunctional) redundancy for each data set. A

correlation for any two functions across all species tells

us something about how much two functions covary,

i.e., how the relative species contributions are distribut-

ed. These values range from1 to 1, where 1 means that

the functions correlate perfectly, 0 means that there is nocorrelation, and1 means that there is a perfect negative

correlation between functions. Low or inversely corre-

lated functions imply that species are functionally

complementary, i.e., several species are important for

ecosystem functioning, and that species might have

trade-offs for different functions. The mean of the

correlation coefficients between all possible combina-

tions of functions is employed as the redundancy (for

this study) across functions. The correlation coefficients

do not, however, tell us anything about the magnitude of

the function values for each function. We therefore also

calculated a measure of redundancy within each

function by taking the mean performance of all species

when the best species was removed. This redundancy

varies between 0 and 1, where 1 depicts perfect

replacement (i.e., the absence of the best-performing

species can be fully compensated), and 0 the absence of

redundancy (i.e., only the best species is able to perform

this function). Then we compared estimates of redun-

dancy across and within functions for all data sets to

explore whether redundancy across functions is less

probable than within functions.

EMPIRICAL DATA: RESULTS

The analyses of the empirical data sets captured the

range of patterns found in the conceptual model (Fig.

1b). Plants showed quite low multifunctional redundan-

cy among the 12 plant species, and considering all six

functions, made joint ecosystem functioning highly

susceptible to species loss (Fig. 2a). Bacteria showed

higher multifunctional redundancy, as there was an

effect of the number of functions only at low speciesrichness (Fig. 2b).

The analyses of the grazerseagrass data sets also

revealed a range in susceptibility of joint ecosystem

functioning to species loss. The importance of diversity

increased with the number of functions considered in the

three data sets (Fig. 3). All three data sets showed high

risk of losing joint functioning if only one or two species

were removed.

The redundancy within functions ranged from 0.19 to

0.71, indicating a wide spread in the ability to sustain

single functions when species are lost (Fig. 4: x axis).

However, across functions, much lower levels of

FIG. 2. The probability of sustaining joint functioning withchanging species richness for (a) plants with 12 species and N16 functions, and for (b) bacteria with 4 species and N 13functions.



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redundancy were observed, as the means of the pairwise

correlation coefficients between functions were close to 0

for most data sets (Fig. 4: y axis). Thus, there was a

higher probability that species extinctions affected joint

ecosystem functioning if more than one function was

considered.

DISCUSSION

Our calculations on empirical results showed clearly

that joint ecosystem functioning (i.e., the sustaining of

multiple functions) is more sensitive to species loss than

are single functions. An important message from these

analyses is that even though the performance of

individual functions can often be explained by thepresence of one or a few functionally dominant species

and is relatively insensitive to initial species loss, joint

functioning proved to be generally susceptible to the loss

of species. This defines a new type of complementarity

for species: complementarity across multiple functions.

To ensure overall functioning in the face of biodiversity

loss, it is thus important to consider the degree of

multifunctional redundancy.

Interestingly, the patterns in the empirical data sets

spanned the ranges of species lossfunctioning relation-

ships covered in the conceptual model (Fig. 1b). The

results from Plants (Fig. 2a) mirrored the patterns of a

multifunctionally nonredundant community (species arecomplementary in terms of which functions they are

important for), and the results from Bacteria (Fig. 2b)

mirrored those of a multifunctionally redundant assem-

blage (most species are important for all functions). The

Seagrass data also revealed that considering multiple

functions made the species assemblages more susceptible

to diversity loss (Fig. 3).

This general pattern was also shown by the compar-

ison of redundancy within and across functions, where

redundancy across functions was generally low regard-

less of the degree of redundancy for single functions

(Fig. 4). Our simple approach to measuring redundancy

does not intend to give a general absolute measure for

the consequences of species loss, but it allows a

standardized comparison of relative redundancy across

data sets. We consider it a strength of our analysis that

we used different data sets, which had not initially been

collected to quantitatively test predictions on multifunc-

tional effects of diversity loss. Across the organisms and

ecosystems used in these data sets, we arrived at a

general conclusion about the importance of biodiversity

FIG. 3. The probability of sustaining joint functioning withchanging species richness for (a) Seagrass I, (b) Seagrass II,and (c) Seagrass III grazer studies (Duffy et al. 2001, 2005).The number of functions (N) ranges from 3 to 4. The patternis identical for scenarios with one and two functions in

Seagrass I.

FIG. 4. The means of the redundancy valueswithin single functions plotted against the meansof the correlation coefficients across multiplefunctions. Error bars are61 variance. Studies inthe graph are from left to right: Seagrass III,Plants, Seagrass I, Seagrass II, and Bacteria. SeeAnalysis of empirical data: Calculation of redun-dancy.



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in sustaining overall ecosystem functioning. Our model

and analyses confirmed the observation in Duffy et al.

(2003) that even where sampling can explain individual

ecosystem responses, only more diverse assemblages

maximized multiple ecosystem responses simultaneous-

ly. If our observed patterns should prove to be general,

they have important consequences for how the effects of

biodiversity should be interpreted.The first attempts to quantify the importance of

biodiversity for multiple functions (Hector and Bagchi

2007, and the present study) reach the same conclusion:

plant species richness is more important for multi-

functionality than for single functions. Hector and

Bagchi (2007) analyzed a data set from the BIODEPTH

project (Hector et al. 1999). Similar to our study, they

based their analyses on presence/absence, but Hector

and Bagchi (2007) used backward-deletion multiple

regressions and the Akaike Information Criterion to

find the most parsimonious set of species that influences

each function. They thus used information on species

mixtures to predict the number of species required as

number of ecosystem functions increase (based on set

theory). Our model is based on the simpler, but more

general, concept of how combinations of monocultures

may sustain an increasing number of functions, and our

empirical analyses included a broader set of organisms

and functions. The combined results of both studies

suggest that effects of diversity on multiple ecosystem

functioning may be general.

The lack of a multifunctional perspective on BEF has

skewed the debate about the importance of species

richness and species identity. Many studies show

idiosyncratic effects of diversity on ecosystem functions

(Wardle et al. 1997, Emmerson et al. 2001), whereas

others show that diverse assemblages do not perform

significantly better than the best monoculture (Cardinale

et al. 2006). Our results suggest that focusing on

individual functions can often be highly misleading,

because a high level of a single function does not equal

overall ecosystem functioning. In contrast, it is possible

that the mixture performs worse than the best mono-

culture for each individual function (unifunctional

underyielding), but still experiences multifunctional

overyielding because the identity of the best monocul-

ture species switches between functions. Redundancy is

thus less likely to be present when multiple functions areconsidered. As pointed out by Kareiva et al. (2007),

important ecosystem services may often be subject to

trade-offs, i.e., an increase in one service (e.g., biomass

production) may be accompanied by a decrease in

another (e.g., resistance to disease). Indeed, low

functional redundancy is confirmed in natural marine

systems (Micheli and Halpern 2005), suggesting that the

functioning of ecosystems can be tightly linked to

changes in biodiversity. Likewise, Heemsbergen et al.

(2004) show that between-species functional differences

over four traits explain positive net diversity effects for

two individual ecosystem processes in soil microcosms.

Additionally, the width of single functions addressed in

BEF studies tends to be very narrow, often based on

production within one trophic level and consumption of

the next lower level (Cardinale et al. 2006). Giller et al.

(2004) strongly advocate that additional important

process rates in ecosystems should be analyzed within

the BEF framework.

None of the published BEF studies that have to datemeasured multiple functions have looked at natural

ecosystems and nonrandom species loss. Future exper-

iments should aim at filling this gap so that the results of

our analyses can be compared to alternative scenarios.

The fact that published studies have used experimentally

constructed communities does not mean, however, that

their studies on random species loss does not have any

relevance to natural systems. Researchers have often

chosen common species from the natural systems that

their assembled communities are supposed to mimic,

and the results from marine BEF experiments (e.g.,

Duffy et al 2003) appear to scale up to patterns found at

regional and global scales (Worm et al. 2006). We

believe that the results of our simple model, and the fact

that it is supported by the analyses of independent

empirical data, are robust in terms of different types of

extinction scenarios and types of density dependence,

since it includes no assumptions about these factors.

Within the field of BEF, the terms ecosystem function

and functioning are often used as synonyms; the result is

some confusion regarding the meaning of these terms.

With an attempt to explicitly define ecosystem functions

as single processes that together constitute overall

ecosystem functioning we believe that our interpretation

of the effects of biodiversity change will be facilitated.

Even though touched upon in earlier studies (Rosenfeld

2002a, b, Duffy 2003, Bracken and Stachowicz 2006),

the explicit and quantitative point that joint functioning

is more sensitive to species loss than are single ecosystem

functions, has only been brought forward twice (Hector

and Bagchi 2007, the present study). Turning to a

multifunctional perspective can move us away from the

traditional focus on sampling effects of dominant

species. In terms of resilience (the ability of a system

to return to its original state following a perturbation), a

multifunctional perspective provides new insights into

the way species can buffer for environmental fluctua-

tions and changes, as well as for perturbations andstresses. Earlier work has pointed out the importance of

biodiversity for single functions in fluctuating environ-

ments (Yachi and Loreau 1999), the role of phenotypic

trade-offs for the dynamics of functional group proper-

ties (Norberg et al. 2001), and the concept of response

diversity (Elmqvist et al. 2003). However, a focus on

multiple functions calls for further concerns. In the face

of external pressure on many ecosystems, ensuring

resilience requires management of a broader range of

species and traits than previously acknowledged.

Our results are also relevant to the discussion about

the goods and services derived by humans from



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ecosystems. Naeem (1998) discussed the concepts of

ecosystem failure and reliability and proposed: From

an ecosystem perspective, an ecosystem fails when it

ceases to provide the services and goods demanded of

it. If, from a management perspective, we define a

range of functions (or services) that we intend to

preserve, any amount of species loss resulting in a

significant lowering of any one of those functions couldbe considered an ecosystem failure. Therefore, we

propose that ensuring multiple functions could be equal

to sustaining ecosystem integrity. By defining a range

and level of functions that are essential from the

perspective of ecosystem services and environmental

management, we can start to evaluate the effects of

biodiversity loss for real systems. Even though arbi-

trarily chosen, a 50% decrease in functioning could be

viewed as a level at which the ecosystem can no longer

provide the necessary ecosystem functions. There is little

discussion in the literature about what effect levels of

reduced ecosystem function could be viewed as ecolog-

ically significant, and hence we had not much informa-

tion to base our threshold levels on. In addition to the

EC50 index analogue, decreases in ecosystem function in

the range of 50% in the lowest compared to the highest

diversity treatments seem to be quite common in the

literature on biodiversity effects on ecosystem function-

ing (BEF). Average diversity effect sizes for both

terrestrial and aquatic ecosystems are;0.5 (measured as

loge[response ratios]), which corresponds to ;40%

reduction in process rates for biomass production and

resource use (see Cardinale et al. 2006: Fig. 1).

Our model and analyses are only a small step to show

that species richness may affect the overall functioning

of ecosystems. For future analyses, several issues need to

be addressed when deciding how to jointly analyze

multiple functions, and there is a need for more studies

that look explicitly at multiple functions for realistic

ranges of diversity. Moreover, the potential consequenc-

es for ecosystem management have to be fully devel-

oped. We propose four emergent research tasks. First,

functions have to be defined more properly. Some

functions may be viewed as subparts of other functions;

for example, nutrient cycling is one part of the many

processes that determine primary productivity. A second

task related to ecosystem management is the weighting

of functions. Should the degradation of specific chem-icals be viewed on a par with primary productivity, and

is pollination more, less, or equally important as

herbivory? Third, from a management perspective,

how many functions constitute ecosystem functioning?

Cardinale et al. (2006) consider biomass production at

the focal trophic level and resource use efficiency, but

this definition neglects indirect effects (e.g., nutrient

regeneration by consumers) or rates of mutualism (N2fixation, pollination). Finally, we need theoretical and

empirical studies on how different extinction scenarios

and types of density dependence affect the relationship

between biodiversity and multiple functions. An effec-

tive analytical framework should be able to address all

of these issues. By moving from a unifunctional to a

multifunctional approach, there will be a shift in the way

the effects of biodiversity loss will be interpreted, and

this will aid management and conservation of biological

values, as well as increase the understanding of the

benefits of protecting biodiversity for our future.

ACKNOWLEDGMENTS

We thank J. Havenhand, B. Matthiessen, J. Bengtsson, andM. Gamfeldt for stimulating discussions and valuable com-ments. Three anonymous reviewers provided comments thatstrengthened the synthesis and discussion. We are grateful forsupport from the Swedish Environmental Protection Agencythrough the MARBIPP program (to L. Gamfeldt), FORMASthrough contract 215/2006-2096 (to P. R. Jonsson), the SwedishResearch Council through contract 621-2002-4770 (to P. R.Jonsson), the Helge and Ax:son Johnson Foundation, theWa hlstro ms Foundation, the Colliander Foundation, theAdlerbertska Foundation, the Ebba and Sven SchwartzFoundation, and the Hasselblad Foundation.

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