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NATURE ECOLOGY & EVOLUTION 1, 0168 (2017) | DOI: 10.1038/s41559-017-0168 | www.nature.com/natecolevol 1

PERSPECTIVEPUBLISHED: 22 JUNE 2017 | VOLUME: 1 | ARTICLE NUMBER: 0168

Biodiversity is causally linked to ecosystem functioning: changes in the number of species in a community generally result in changes in a range of functions1–5. Functioning increases with

species richness due to niche complementarity, positive interac-tions, or the presence of certain influential species. The relationship between richness and functioning can generally be described using some version of a positive but decelerating function6,7, such that the relationship is steep at low richness, and levels off quickly as rich-ness increases further.

Because species differ in their contributions to different func-tions, it has been proposed that biodiversity should be more impor-tant—and that the relationship should be less saturating—when multiple functions are considered. This idea, often referred to as multifunctionality8, was originally proposed in a study on seagrass, where it was observed that a diverse assemblage of small marine grazers simultaneously maximized multiple functions9. Positive biodiversity–multifunctionality relationships were later shown for many different systems8,10–23, and a recent meta-analysis sug-gests that a positive biodiversity–multifunctionality relationship is general24.

How biodiversity influences ecosystem multifunctionalityBecause all species are to some extent functionally unique, high biodiversity may provide for a high variety of functions. If species specialize in different functions, all species are needed to perform all of the functions. As a simple example, most well-functioning ecosystems include primary production, primary and secondary consumption, decomposition and nitrogen fixation (to name a few). If each function is provided by a different species, all are needed to perform all functions simultaneously. [Au: Can this sentence be deleted? It is almost a carbon copy of the second sentence in this paragraph.]

In addition to providing for more functions, increasing bio-diversity is proposed to increase the level of multifunctionality. More importantly, it is hypothesized that the importance of bio-diversity increases with the number of functions considered. It is this hypothesis that suggests that, as we increase the number of functions that we study, the slope between biodiversity and mul-tifunctionality becomes steeper. The literature on biodiversity and

Revisiting the biodiversity–ecosystem multifunctionality relationshipLars Gamfeldt†* and Fabian Roger†*

A recent and prominent claim for the value of biodiversity is its importance for sustaining multiple ecosystem functions. The general idea is intuitively appealing: since all species are to some extent unique, each will be important for a different set of functions. Therefore, as more functions are considered, a greater diversity of species is necessary to sustain all functions simultaneously. However, we show here that the relationship between biodiversity and ecosystem functioning does not change with the number of functions considered. Biodiversity affects the level of multifunctionality via non-additive effects on individ-ual functions, and the effect on multifunctionality equals the average effect on single functions. These insights run counter to the messages provided in the literature. In the light of our simulations, we present limitations and pitfalls with current methods used to study biodiversity–multifunctionality, which together provide a perspective for future studies.

ecosystem multifunctionality has mainly focused on this ‘biodi-versity begets the level of multifunctionality’ hypothesis (for exam-ple, refs 8,10,12,24–26), and, with few exceptions22,27,28, all papers on the importance of biodiversity for multifunctionality study sets of func-tions shared by all focal species. We should point out that the case in which certain functions are unique to particular species is a special case within the broader ‘biodiversity begets the level of multifunc-tionality’ hypothesis. There is no conceptual difference between a species that performs a given function at a very low level and a spe-cies that does not perform the function at all. They are both part of the same continuum. A value of zero will simply decrease the aver-age level of multifunctionality slightly more than a low value.

While the assumption that the slope between biodiversity and multifunctionality becomes steeper with the number of functions is not always mentioned explicitly, it is still an implicit assumption in many papers. It is common for articles to include variations of state-ments like “the impact of diversity is stronger when multiple func-tions are considered together,” or “more species are needed to sustain multiple functions than any single function,” or “studies focusing on individual functions will underestimate levels of biodiversity required for multifunctionality” (for example, refs 3,8,10,11,24,25,29,30). The truth of such statements requires that the slope of the diversity–ecosystem function relationship changes as we move from single to multiple functions.

The importance of biodiversity for multifunctionality is grounded on a verbal framework that makes intuitive sense. Species have trade-offs in terms of allocating resources to growth, reproduc-tion and survival, so no single species can maximize all ecosystem functions9,31,32. A variety of organisms are hence required for an eco-system to simultaneously sustain multiple functions. However, this intuitive knowledge, blended with vague terminology, has painted a hazy picture of a causal relationship between biodiversity and mul-tifunctionality. We argue here that it is time scientists abandon the idea that the effect on multifunctionality is larger than the average effect on single functions, and that the importance of biodiversity is dependent on the number of functions considered. We show that, contrary to common belief, increasing the number of functions considered does not by itself change the nature of the relationship between biodiversity and ecosystem multifunctionality.

Department of Marine Sciences, University of Gothenburg, Box 461, SE-405 30 Göteborg, Sweden. †These authors contributed equally to this work. *e-mail: [email protected]; [email protected]

© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

SUPPLEMENTARY INFORMATIONVOLUME: 1 | ARTICLE NUMBER: 0168

NATURE ECOLOGY & EVOLUTION | DOI: 10.1038/s41559-017-0168 | www.nature.com/natecolevol 1

http://dx.doi.org/10.1038/s41559-017-0168

1

Supplementary information, Gamfeldt and Roger 2017. Revisiting the biodiversity-

ecosystem multifunctionality relationship.

Figure S1 | The observed increase in slopes with the number of functions is a 5

mathematical artefact. It may seem from Fig. 2b that the pattern of increasing slopes with

increasing number of functions provides insight into what can be described as a purely

mathematical, but relevant, effect. But as we show with simulations, this is rather a

mathematical artefact. Let us consider a hypothetical example with 10 species (A-J) and 9

functions (F1-F9) as shown in the left panel. All functions are perfectly and positively 10

correlated, meaning that for each species the contribution is the same for all functions. This is

equivalent to studying a single function. The mid panel shows how the number of functions ≥

threshold changes with species richness (as in Fig. 1b). Red circles are for 5 functions, blue

are for 9 functions. Even though we are essentially not analysing multifunctionality,

regardless of how many functions we add or subtract, the right panel shows that the 15

magnitude of the slopes increases with the number of functions (as in Fig. 2b). Shaded areas

represent the 95% CI of the slope estimates (n = 1023). Given a shift in slopes regardless of

the fact that we are studying a single function, it is not warranted to use Fig. 2b to infer any

insight into the consequences of studying few or many functions. Note that the same

argument could be made for the number of species considered. 20

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SUPPLEMENTARY INFORMATION

http://dx.doi.org/10.1038/s41559-017-0168

2

Figure S2 | The effect of choice of standardisation on the result of the multi-threshold

approach. a, The expected result for the multi-threshold approach for a simulated diversity

experiment with 15 species and 9 functions. Function values drawn from a normal 25

distribution with a mean of 30 and a standard deviation of 6. We show the results of using two

different methods to standardise raw function values, either between 0 and 1 (blue line) or by

the maximum (red line). Both standardisation techniques are common in the literature but the

choice has a large impact on the resulting slope pattern. Note that standardisation by the

maximum shows the highest positive slopes for the same range of threshold levels for which 30

the standardisation between 0 and 1 shows the highest negative slopes. The shaded areas

represent the 95% CI of the slope estimates (n = 32767); the simulation was fully replicated

(all possible species composition at each richness level). b, The distribution of the raw

function values. c and d, The distribution of the function values after the two different

standardisations, which results in the two distinct slope patterns shown in a. 35

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http://dx.doi.org/10.1038/s41559-017-0168

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http://dx.doi.org/10.1038/s41559-017-0168

4

Figure S3 | Same figure as Fig. 1, here highlighting the maximum values used in quantile

regression on the 100% quantile. The maximum function values at each richness level are 40

indicated in black. The relationship between biodiversity (species richness) and

multifunctionality for a scenario with no species interactions, a-d, and with complementarity

for functions 1 and 6, e-h. Relationships are shown for individual functions (a, e), average

function value (b, f) and for the number of functions above or equal to each of nine thresholds

(c, g). Also presented is how the slope of the relationship between biodiversity and number of 45

functions above threshold changes with threshold level (d, h). Lighter blue colours in c and g

represent higher point densities. The regression line is fitted to all points before rounding (n =

32767). In the plots d and h, the shaded area represents the 95% CI of the slope estimates.




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