Supporting InformationCadotte et al. 10.1073/pnas.0805962105Supporting TextSummary of Experiments Included in Analyses. Our data wereobtained from 29 independent experiments that have beenreported in 11 separate publications (Table S1). The multipleexperiments reported within publications correspond to identi-cal manipulations of diversity performed at different geographiclocations (1), in different-sized experimental units (2), or indiffering nutrient regimes (3–6). The number of plots or potsused in single experiments ranged from 30 to 360, and thenumber of species used in the most diverse polyculture rangedfrom 4 to 32 (Table S1). The individual experiments varied inmean levels of phylogenetic diversity (PDC) of species in a plotand in both standardized measures of biomass productionLRmean and LRmax (for detailed descriptions of these measures,see Materials and Methods) (Fig. S4). Differences in PDC valuesreflect the different strategies experimenters used in selectingspecies assemblages, with some studies randomly selecting spe-cies from a known community type as opposed to those thatpurposefully selected species from different functional groups.

Comparison of Different Phylogenetic Methods. We used five meth-ods to construct phylogenies: maximum likelihood (ML), MLwith Ultrametric rate smoothing (MLU), Bayesian inferences(BI), BI with Ultrametric rate smoothing (BIU), and an angio-sperm supertree (ST). Although PDC estimates from all methodsproduced superior models explaining the log ratio of biomassproduction, there were only minor differences among individualmethods (all P � 0.0001). BIU was the best model (Akaikeweight � 0.485), followed, in order, by MLU, ML, BI, and ST(Akaike weight � 0.255, 0.146, 0.085, and 0.028, respectively).

Removal of Commonly Used Species. One potential concern aboutcombining data from numerous experimental units across 29experiments is that some species maybe more widely used thanothers and therefore disproportionately affect the results. Spe-cies were highly variable in the number of experimental units inwhich they were included (Fig. S5), ranging from 30 speciesincluded in �20 experimental units each to a single species,Achillea millefolium, found in �500 units. We examined theeffect of the commonly used species on our results by removing

the experimental units associated with these species. We createdn data subsets corresponding to the n species found in �10% ofplots, where all plots containing common species ni were re-moved. We reran the statistical analyses and compared theseresults with the full dataset to determine whether species ni hada disproportionate effect on the results.

In the full model, PDC was by far the likeliest best modelexplaining the data (Table S2). Even though we removed eachof the species in �10% of the experimental units, the generalresults did not change, and PDC was always much more likely tobe the best model rather than either the number of species orfunctional groups (Akaike weight �0.999). Therefore, in no casedid the phylogenetic distance added by a commonly used speciesalter our results.

Testing Transgressive Overyielding Patterns. Although we havefocused on polyculture biomass production relative to meanproduction of the monocultures exclusively, we also examined analternative metric relating polyculture production to the domi-nant producing monoculture, termed ‘‘transgressive overyield-ing’’ (7, 8). In the main article, community productivity wasstandardized using the proportional difference between biomassproduction (y) of a polyculture (p) and the mean of those speciesin monoculture (m� ) in experiment i, using the log response ratioLRmax � ln(yip/yim� ). Alternatively, we standardized polycultureproduction to maximum producing species in monoculture (m̂)in experiment i, using the log response ratio LRmax � ln(yip/yiˆm).Transgressive overyielding refers to LRmax � 0.

As has been published elsewhere (9, 10), the metaanalysis ofthese experiments reveals that the majority of polyculture plots(71.5%) show nontransgressive overyielding (LRmean � 0, butLRmax � 0; see Fig. S6). At the same time, a minority ofpolycultures (33.6%) do exhibit transgressive overyielding(LRmean � 0 and LRmax � 0), which allows examination ofwhether the magnitude of LRmax is a function of evolutionaryhistory. We could find no evidence that LRmax was related tochanges in PDC (nor any other aspect of diversity; Table S3 andFig. S6). Thus, our analyses suggest that evolutionary historyhelps explain the net effect of diversity, but not the phenomenaof transgressive overyielding.

1. Spehn EM, et al. (2005) Ecosystem effects of biodiversity manipulations in Europeangrasslands. Ecol Monogr 75:37–63.

2. Dimitrakopoulos PG, Schmid B (2004) Biodiversity effects increase linearly with bio-trope space. Ecol Lett 7:574–583.

3. Fridley JD (2002) Resource availability dominates and alters the relationship betweenspecies diversity and ecosystem productivity in experimental plant communities. Oe-cologia 132:271–277.

4. Fridley JD (2003) Diversity effects on production in different light and fertility envi-ronments: An experiment with communities of annual plants. J Ecol 91:396–406.

5. Lanta V, Leps J (2006) Effect of functional group richness and species richness inmanipulated productivity–diversity studies: A glasshouse pot experiment. Acta Oe-colog Int J Ecol 29:85–96.

6. Reich PB, et al. (2001) Plant diversity enhances ecosystem responses to elevated CO2

and nitrogen deposition. Nature 410:809–812.7. Hector A, Bazeley-White E, Loreau M, Otway S, Schmid B (2002) Overyielding in

grassland communities: Testing the sampling effect hypothesis with replicated biodi-versity experiments. Ecol Lett 5:502–511.

8. Loreau M (1998) Separating sampling and other effects in biodiversity experiments.Oikos 82:600–602.

9. Cardinale BJ, et al. (2006) Effects of biodiversity on the functioning of trophic groupsand ecosystems. Nature 443:989–992.

10. Cardinale BJ, et al. (2007) Impacts of plant diversity on biomass production increasethrough time due to complimentary resource use: A meta-analysis of 45 experiments.Proc Natl Acad Sci USA 104:18123–18128.

11. Dukes JS (2001) Productivity and complementarity in grassland microcosms of varyingdiversity. Oikos 94:468–480.

12. Naeem S, Tjossem SF, Byers D, Bristow C, Li SB (1999) Plant neighborhood diversity andproduction. Ecoscience 6:355–365.

13. Naeem S, Hakansson K, Lawton JH, Crawley MJ, Thompson LJ (1996) Biodiversityand plant productivity in a model assemblage of plant species. Oikos 76:259 –264.

14. Tilman D, Wedin D, Knops J (1996) Productivity and sustainability influenced bybiodiversity in grassland ecosystems. Nature 379:718–720.

15. Tilman D, et al. (1997) The influence of functional diversity and composition onecosystem processes. Science 277:1300–1302.

16. Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends EcolEvol 19:101–108.

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 1 of 10

Fig. S1. Molecular phylogeny of the 143 angiosperms used in this metaanalysis plus three outgroup species estimated from Bayesian analyses.

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 2 of 10

Fig. S2. Scatter plots of plot-level PDC estimates for different phylogenetic methods (BI, phylogeny based on Bayesian inference; ML, maximum likelihood; BIU,Bayesian inference with Ultrametric rate smoothing; MLU, maximum likelihood with ultrametric rate smoothing; and ST, angiosperm supertree). All correlationswere highly significant (r � 0.912, P � 0.01).

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 3 of 10

0.0

0.2

0.4

0.6

0.8

1.0

Number of functional groups

Mea

n P

D

1 2 3 4 5

Fig. S3. Mean plot PDC is positively correlated with the number of functional groups in that plot. Each line represents a different experiment.

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 4 of 10

●

●●

●●

●●

●

●

0.02.0

4.06.0

8.00.1

2.1ytisrevid citenegolyhp tolP

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●●

●

●●

●

●

4−

2−

02

)naem( oitar goL

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

6−

4−

2−

02

Experiment

)xam( oitar goL

Fig. S4. Boxplots of PDC, LRmean, and LRmax for each experiment.

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 5 of 10

Number of plots

seiceps fo rebmu

N

0 100 200 300 400 500

05

0151

0252

03

Achillea millefolium

Fig. S5. Histogram of the number of experimental units in which each species occurred.

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 6 of 10

●

●

●●●

●

●●

●

●●

●

●

●

●●

●●

●

●

●

●

●

●

●

●●

●

●●

●●●

●

●

●

●●

●

●●

●

●●

●

●

●

●●●●

●●●

●

●

●

●●

●

●

● ●

●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●●

●

●●●

●

●

●

●

●●

●

●

●●●

●

●

●●

●

●●

●

●

●

●●●

●●

●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●

●●

●●

●

●

●

●

●●●●●●●

●

●●●

●●

●●●●●●●●

●

●

●

●

●●

●●●●

●●

●

●

●

●●

●●●●●●●

●

●●

●●

●●●

●●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●

●●

●●

●●

●

●●

●

●

●

●

●

●●

●

●

●●●

●

●

●●

●

●●

●

●●

●

●

●●

●

●●

●

●

●

●

●

●

●

●●

●●●

●

●

●

●●

●

●●

●●

●●

●

●

●●

●●

●●●●●●●●

●

●

●

●

●●

●●●

●

●

●

●

●

●●

●

●●

●

●

●●

●

●●

●

●

●●

●

●●

●

●

●

●●

●

●

●

●

●●●●

●

●●●

●●

●

●

●

●●●

●

●

●

●

●

●

●

●

●●●

●

●

●●●

●

●

●

●●●●

●

●

●●

●

●

●

●●

●

●

●

●●●

●●

●●

●

●

●

●

●●

●●

●●●

●

●

●

●

●

●

●

●●

●

●●●

●

●

●●●●●●●●

●

●

●

●●●

●●

●

●

●●

●

●

●

●

●

●

●

●●●●

●

●●

●

●

●

●●

●

●

●

●●

●

●

●

●●

●●●

●

●

●●●

●●

●●

●●●

●

●●

●

●●●

●

●●

●

●

●

●●

●●●●

●

●●

●

●●

●●●●●●

●●

●

●

●

●●●●●

●●●

●

●

●●

●●●

●●●

●

●

●●

●●●●

●

●

●

●● ●●

●●

●

●

●●●

●●

●●●

●●●●

●

●●

●

●●●

●

●

●

●

●

●●

●

●

●

●●●●

●●●

●●

●

●

●

●

●

●

●●●●

●

●

●

●●

●

●

●

●●

●

●● ●

●●●●

●●

●

●●

●

●

●●

●●●●●●●

●

●●●●●●●

●●●

●●

●

●

●●●●●●

●

●

●

●●●●●●

●

●

●

●

●●●

●

●●●●●●

●

●●●●●

●●

●●

●

●

●

●●●●●

●

●

●

●●

●

●●●

●●

●

●●●●

●

●

●●●●●●●●

●

●

●

●

●

●●

●

●

●

●●●●●

●

●●●

●

●

●●●●

●●●

●●●

●

●

●●●●

●●

●

●●

●

●●●

●

●

●

●

●●

●

●

●●●

●

●●●

●

●●

●

●

●●●

●●

●

●

●

●●

●●

●●●●●●

●

●

●

●●●●●●●●●

●

●

●●●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●●●●

●

●

●

●

●●

●●●

●●

●

●

●●

●●●

●

●●

●

●●

●

●

●●●●●●

●

●

●

●●

●●●●

●●

●

●

●● ●

●●

●●

●

●

●●●

●

●

●

●

●

●●

●

●●

●

●

●

●

●●●●

●

●●●●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●●●

●●

●●●

●●●●●●

●●●

●●

●●

●

●●

●

●

●●●●●

●

●

●

●●

●●●●●●●

●

●●

●

●

●●●

●●●

●

●●●●

●●

●

●

●●● ●

●●

●●

●●

●

●●

●

●●

●

●

●●●●●●

●

●●●●●

●●

●●

●

●

●●

●

●

●

●

●

●

●●●

●●

●●

●

●

●● ●●

●●

●●

●

●

●

●● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

● ●●

●

●

●

●

●

●

●

●

●

●

●●●●

●●●

●●

●

●

●

●

●●

●

●

●●

●●

●

● ●●

●

●

●●●

●●

●

●

●

●●

●

●

●

●

●

●

●●

●●

●

●

●●●●●●●

●

●

●

●

●

●

●

●●●●●

●

●●

●●

●

●●

●

●●

●

●

●●

●●●●●●●●

●●●

●

●

●●

●●

●

●

●●

●

●

●●

●

●

●

●

●

●

●●●

●●

●

●

●

●

●

●

●●

●●●

●

●

●

●

●●●●

●●●●

●

●

●

●●

●●

●

●

●

2 4 6 8 10 12 14 16

3−

2−

1−

01

2

Number of species

)xam( oitar goL

●

●

●●●

●

●●

●

●●

●

●

●

●●

●●

●

●

●

●

●

●

●

●●

●

●●

●●●

●

●

●

●●

●

●●

●

●●

●

●

●

●●●●

●●●

●

●

●

●●

●

●

●●

●

●

●

●●●

●

●

●

●

● ●

●

●

●

●

●●

●

●●●

●

●

●

●

●●

●

●

●●●

●

●

●●

●

●●

●

●

●

●●●

●●

●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●

●●

●●

●

●

●

●

●●●●●●●

●

●●●

●●

●●●●●●●●

●

●

●

●

●●

●●●●

●●

●

●

●

●●

●●●●●●●

●

●●

●●

●●●

●●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●●

●

●

●●

●

●

●

●

●

●●

●●

●●

●

●●

●

●

●

●

●

●●

●

●

●●●

●

●

●●

●

●●

●

●●

●

●

●●

●

●●

●

●

●

●

●

●

●

●●

●●●

●

●

●

●●

●

●●

●●

●●

●

●

●●

●●

●●●●●●●●

●

●

●

●

●●

●●●

●

●

●

●

●

●●

●

●●

●

●

●●

●

●●

●

●

●●

●

●●

●

●

●

●●

●

●

●

●

●●●●

●

●●●

●●

●

●

●

●●●

●

●

●

●

●

●

●

●

●● ●

●

●

●●●

●

●

●

●●●●

●

●

●●

●

●

●

●●

●

●

●

●●●

●●

●●

●

●

●

●

●●

●●

●●●

●

●

●

●

●

●

●

●●

●

●●●

●

●

●●●●●●●●

●

●

●

●●●

●●

●

●

●●

●

●

●

●

●

●

●

● ●●●

●

●●

●

●

●

●●

●

●

●

● ●

●

●

●

●●

●●●

●

●

●●●

●●

●●

●● ●

●

●●

●

●●●

●

●●

●

●

●

●●

●●●●

●

●●

●

●●

●●●●

●●

●●

●

●

●

●●●●●

●●●

●

●

●●

●●●

●●●

●

●

●●

●● ●●

●

●

●

●● ● ●

●●

●

●

●●●

●●●●●

●●●●

●

●●

●

●● ●

●

●

●

●

●

●●

●

●

●

●●●

●

●●●

●●

●

●

●

●

●

●

●●●●

●

●

●

●●

●

●

●

●●

●

●● ●

●●●●

●●

●

●●

●

●

●●

●● ●●

●●

●

●

●●●● ●●●

●●●

●●

●

●

● ●● ●●●

●

●

●

●●●

●●●

●

●

●

●

●●

●

●

●●● ●●●

●

●●●●●

●●

●●

●

●

●

●●●●●

●

●

●

●●

●

●●●

● ●

●

●●

●●

●

●

●● ●●●

● ●●

●

●

●

●

●

●●

●

●

●

●●●●●

●

●●●

●

●

●●

● ●

●●●

●●●

●

●

●●●●

●●

●

●●

●

●●●

●

●

●

●

●●

●

●

●●●

●

●●●

●

●●

●

●

●●●

●●

●

●

●

●●

●●

●●●●● ●

●

●

●

●●●●●●●●●

●

●

●●●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●●●●

●

●

●

●

●●

●●●

●●

●

●

●●

●●

●

●

●●

●

●●

●

●

●●●●●●

●

●

●

●●

●●●●

●●

●

●

●●●

●●

●●

●

●

●●●

●

●

●

●

●

●●

●

●●

●

●

●

●

●●●●

●

●●●●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●●●

●●

●●●

●●●●●●

●●●

●●

● ●

●

●●

●

●

●●●●●

●

●

●

●●

●●●●●●●

●

●●

●

●

●● ●

●●●

●

●●●●

●●

●

●

●●● ●

●●

●●

●●

●

●●

●

●●

●

●

●●●●●●

●

●●●●●

●●

● ●

●

●

●●

●

●

●

●

●

●

●●●

●●

● ●

●

●

●● ●●

●●

●●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

● ●●

●

●

●

●

●

●

●

●

●

●

●● ●●

●●●

●●

●

●

●

●

●●

●

●

●●

●●

●

● ●●

●

●

●●●

●●

●

●

●

●●

●

●

●

●

●

●

●●

●●

●

●

●●●●● ●●

●

●

●

●

●

●

●

●●

●●●

●

● ●

●●

●

●●

●

●●

●

●

●●

●●●●●●●●

●●●

●

●

●●

● ●

●

●

●●

●

●

●●

●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

● ●●●

●●●●

●

●

●

●●

●●

●

●

●

1 2 3 4 5

3−

2−

1−

01

2

Number of functional groups

)xam( oitar g oL

●

●

● ●●

●

●●

●

●●

●

●

●

●●

●●

●

●

●

●

●

●

●

● ●

●

●●

●●●

●

●

●

●●

●

●●

●

●●

●

●

●

●●

●●

●●

●

●

●

●

●●

●

●

● ●

●

●

●

●●

●

●

●

●

●

● ●

●

●

●

●

●●

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

●●

●

●●

●

●

●

●●●

● ●

●

●

●

●

●

●● ●

●

●

●●

●

●

●

●

●

●●

●●

●

●

●

●

●●● ●●

●●

●

●●●

●●

●●●

●●●● ●

●

●

●

●

● ●

●● ●

●

● ●

●

●

●

●●

●●●

●●●

●

●

● ●

●●

● ●●

●●

●

●

●

●

● ●●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●●

●

●

● ●

●

●

●

●

●

●●

●●

●●

●

● ●

●

●

●

●

●

●●

●

●

●● ●

●

●

●●

●

●●

●

●●

●

●

●●

●

●●

●

●

●

●

●

●

●

●●

●●●

●

●

●

●●

●

●●

●●

●●

●

●

●●

● ●

●● ●●●

●●●

●

●

●

●

●●

● ●●

●

●

●

●

●

●●

●

●●

●

●

●●

●

●●

●

●

●●

●

●●

●

●

●

●●

●

●

●

●

●● ●●

●

●●●

●●

●

●

●

●●●

●

●

●

●

●

●

●

●

●● ●

●

●

●●●

●

●

●

●●●●

●

●

●●

●

●

●

●●

●

●

●

●●●

●●

●●

●

●

●

●

● ●

● ●

●● ●

●

●

●

●

●

●

●

●●

●

●●●

●

●

●●●●●●●●

●

●

●

●●●

●●

●

●

●●

●

●

●

●

●

●

●

● ●●●

●

●●

●

●

●

●●

●

●

●

● ●

●

●

●

●●

●● ●

●

●

●●●

●●

●●

●● ●

●

●●

●

● ●●

●

●●

●

●

●

●●

● ●●●

●

●●

●

● ●

●●●●

●●

●●

●

●

●

●●●● ●

●● ●

●

●

●●

● ●●

●●●

●

●

●●

●● ●●

●

●

●

●● ●●

●●

●

●

●●●

●●●

●●

●●●

●

●

●●

●

●●●

●

●

●

●

●

●●

●

●

●

●●●

●

●●●

●●

●

●

●

●

●

●

●●●●

●

●

●

●●

●

●

●

●●

●

●● ●

●●●●

●●

●

●●

●

●

●●

●●●●●

●●

●

●●●●●●●

●●●

●●

●

●

●●●●●●

●

●

●

●●●●●●

●

●

●

●

●●

●

●

●●

●●● ●

●

●●●●●

●●

●●

●

●

●

●●●●●

●

●

●

●●

●

●●●

● ●

●

●●

●●

●

●

●●●●●●●●

●

●

●

●

●

●●

●

●

●

●●●● ●

●

●●●

●

●

●●

● ●

● ●●

●●●

●

●

●●● ●

●●

●

●●

●

● ●●

●

●

●

●

●●

●

●

●●●

●

●● ●

●

●●

●

●

●●

●

●●

●

●

●

●●

●●

●●●●● ●

●

●

●

●●●●●●●●●

●

●

●●●

●

●

●

●

●

●

●●

● ●

●

●

●

●

●

●● ●

●

●

●

●

●

● ●

●●●

●●

●

●

●●

●●

●

●

●●

●

●●

●

●

●●●●●●

●

●

●

●●

●●

●●

●●

●

●

●● ●

●●

●●

●

●

●●●

●

●

●

●

●

●●

●

●●

●

●

●

●

●●●●

●

●●●●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●

●

●

●●●

●●

●●●

●●●●●●

●●●

●●

● ●

●

●●

●

●

●●

●●●

●

●

●

●●

●●●●●●●

●

●●

●

●

●● ●

●●●

●

●●

●●

●●

●

●

●●● ●

●●

●●

●●

●

●●

●

●●

●

●

●●●●●●

●

●●●●●

●●

●●

●

●

●●

●

●

●

●

●

●

●●●

●●

● ●

●

●

●● ●●

●●

●●

●

●

●

●● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

● ● ●

●

●

●

●

●

●

●

●

●

●

●● ●●

●●●

●●

●

●

●

●

●●

●

●

●●

●●

●

● ●●

●

●

●●●

●●

●

●

●

●●

●

●

●

●

●

●

● ●

●●

●

●

●●●●●●●

●

●

●

●

●

●

●

●●

●●

●

●

● ●

●●

●

●●

●

●●

●

●

●●

●●●

●●● ●●

●●●

●

●

●●

● ●

●

●

●●

●

●

●●

●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●● ●●

●●

●●

●

●

●

●●

●●

●

●

●

0.0 0.2 0.4 0.6 0.8 1.0 1.2

3−

2−

1−

01

2

Phylogenetic diversity (BI)

)xam( oitar goL

a b c

Fig. S6. Relationship between LRmax and plant species richness (a); number of plant functional groups (b); and PDC estimated from Bayesian inference analysis(c). None of these relationships was significant (see Table S3).

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 7 of 10

Table S1. Summary of the studies included in our analyses with the number of experimental units and species levels used

Study (ref. no) ExperimentTotal no. plots,monocultures

No. specieslevels, max

No. polyculturesused in analyses

Dimitrakopoulos andSchmid 2004 (2)

Large pot size 30 (10) 3 (6) 20

Dimitrakopoulos andSchmid 2004 (2)

Medium pot size 30 (10) 3 (6) 20

Dimitrakopoulos andSchmid 2004 (2)

Small pot size 30 (10) 3 (6) 20

Dukes 2001 (11) NA* 36 (24) 4 (16) 9Fridley 2002 (3) Amb. nut. 111 (54) 3 (9) 50Fridley 2002 (3) High nut. 110 (53) 3 (9) 49Fridley 2002 (3) Low nut. 111 (54) 3 (9) 47Fridley 2003 (4) High nut./ High light 63 (21) 3 (6) 42Fridley 2003 (4) High nut./ low light 63 (21) 3 (6) 42Fridley 2003 (4) Low nut./ High light 63 (21) 3 (6) 42Fridley 2003 (4) Low nut./ low light 63 (21) 3 (6) 42Lanta and Leps 2006 (5) High nut. 89 (31) 5 (16) 58Lanta and Leps 2006 (5) Low nut. 89 (31) 5 (16) 58Naeem et al. 1999 (12) NA 360 (30) 4 (4) 330Naeem et al. 1996 (13) NA 181 (64) 6 (16) 101Reich and Tilman 2001 (6) N amb/ CO2 amb 58 (16) 4 (16) 42Reich and Tilman 2001 (6) N elevated/ CO2 amb 58 (16) 4 (16) 42Reich and Tilman 2001 (6) N amb/CO2 elevated 58 (16) 4 (16) 42Reich and Tilman 2001 (6) N elevated/ CO2 elevated 58 (16) 4 (16) 42Spehn et al. 2005 (1) Germany 60 (20) 5 (16) 4Spehn et al. 2005 (1) Greece 52 (14) 5 (18) 4Spehn et al. 2005 (1) Ireland 70 (20) 5 (8) 46Spehn et al. 2005 (1) Portugal 41 (18) 4 (14) 6Spehn et al. 2005 (1) Sheffield 54 (24) 5 (12) 30Spehn et al. 2005 (1) Silwood 66 (22) 5 (11) 30Spehn et al. 2005 (1) Sweden 58 (24) 5 (12) 34Spehn et al. 2005 (1) Switzerland 64 (20) 5 (32) 14Tilman et al. 1996 (14) NA 117 (20) 7 (24) 5Tilman et al. 1997 (15) NA 148 (20) 11 (18) 47

For a select subset of studies, we had to exclude some of the polycultures from the analyses (see Material and Methods for an explanation of why). Thus, thenumber of polycultures used in our analyses is not always equal to the total number of polycultures planted (number of plots � monocultures).*NA, not applicable.

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 8 of 10

Table S2. Statistical models comparing the full data set (analogous to Table 1 Model A) with subsets with plots removed thatcontained each of the 14 commonly used species (i.e., those found in more than 10% of the experimental units) for three measuresof diversity

Species removed Variable F df P value Log likelihood AIC Akaike weight

All species included Spp 90.443 1,262 �0.0001 �1057.728 2,123.456 2.26 � 10�5

PDc 107.55 1,262 �0.0001 �1047.030 2,102.060 �1.0FG 65.469 1,262 �0.0001 �1067.760 2,143.521 9.93 � 10�10

Achillea millefolium Spp 41.638 790 �0.0001 �878.895 1,765.789 3.22 � 10�4

PDc 53.044 790 �0.0001 �870.856 1,749.712 0.999FG 38.523 790 �0.0001 �878.866 1,765.732 3.32 � 10�4

Bouteloua gracilis Spp 64.320 1,015 �0.0001 �898.555 1,805.111 8.21 � 10�6

PDc 84.150 1,015 �0.0001 �886.845 1,781.690 �1.0FG 42.575 1,015 �0.0001 �907.387 1,822.774 1.20 � 10�9

Amaranthus hypochondriacus Spp 72.720 1,050 �0.0001 �863.673 1,735.346 1.51 � 10�5

PDc 89.790 1,050 �0.0001 �852.570 1,713.140 �1.0FG 61.379 1,050 �0.0001 �867.068 1,742.136 5.05 � 10�7

Calendula officinalis Spp 76.901 1,081 �0.0001 �871.708 1,751.417 5.79 � 10�7

PDc 101.17 1,081 �0.0001 �857.346 1,722.692 �1.0FG 67.834 1,081 �0.0001 �874.000 1,756.001 5.85 � 10�8

Fagopyrum esculentum Spp 67.758 1,083 �0.0001 �872.605 1,753.209 8.15 � 10�5

PDc 81.265 1,083 �0.0001 �863.190 1,734.379 �1.0FG 64.514 1,083 �0.0001 �872.232 1,752.463 1.18 � 10�4

Borago officinalis Spp 83.272 1,084 �0.0001 �871.796 1,751.592 6.75 � 10�8

PDc 112.20 1,084 �0.0001 �855.286 1,718.571 �1.0FG 71.451 1,084 �0.0001 �875.266 1,758.532 2.10 � 10�9

Satureja hortensis Spp 77.225 1,085 �0.0001 �836.020 1,680.039 1.81 � 10�5

PDc 94.683 1,085 �0.0001 �825.098 1,658.196 �1.0FG 63.830 1,085 �0.0001 �840.625 1,689.250 1.81 � 10�7

Linum perenne Spp 74.277 1,088 �0.0001 �893.934 1,795.868 3.74 � 10�5

PDc 89.329 1,088 �0.0001 �883.739 1,775.478 �1.0FG 65.025 1,088 �0.0001 �896.219 1,800.438 3.80 � 10�6

Panicum virgatum Spp 80.938 1,100 �0.0001 �942.805 1,893.610 1.25 � 10�3

PDc 89.211 1,100 �0.0001 �936.119 1,880.238 0.999FG 50.019 1,100 �0.0001 �955.487 1,918.975 3.87 � 10�9

Lupinus perennis Spp 69.187 1,105 �0.0001 �931.557 1,871.113 5.03 � 10�6

PDc 90.180 1,105 �0.0001 �919.356 1,846.712 �1.0FG 44.532 1,105 �0.0001 �942.383 1,892.767 9.98 � 10�11

Vicia villosa Spp 82.419 1,107 �0.0001 �955.358 1,918.716 6.90 � 10�5

PDc 97.055 1,107 �0.0001 �945.777 1,899.554 �1.0FG 54.274 1,107 �0.0001 �966.777 1,941.553 7.59 � 10�10

Rudbeckia hirta Spp 80.833 1,109 �0.0001 �978.304 1,964.609 2.40 � 10�4

PDc 92.674 1,109 �0.0001 �969.972 1,947.944 �1.0FG 48.734 1,109 �0.0001 �991.598 1,991.196 4.05 � 10�10

Astragalus canadensis Spp 83.676 1,111 �0.0001 �955.069 1,918.139 5.38 � 10�6

PDc 103.937 1,111 �0.0001 �942.937 1,893.874 �1.0FG 56.510 1,111 �0.0001 �966.052 1,940.104 9.15 � 10�11

Lotus corniculatus Spp 73.769 1,120 �0.0001 �966.644 1,941.288 1.15 � 10�3

PDc 82.706 1,120 �0.0001 �959.865 1,927.729 0.999FG 46.333 1,120 �0.0001 �977.988 1,963.977 1.34 � 10�8

Spp is the number of species in an experimental unit; FG is the number of functional groups, and PDC is phylogenetic diversity. Results are for single-variable,fixed-effect models with experiment (Exp) included as a random effect. Akaike weights can be interpreted as the probability that model i is the best fit to theobserved data among a set of models (16).

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 9 of 10

Table S3. Results of linear mixed effects models predicting the log response ratio of biomass production to three measures ofbiodiversity

Models and variables F df P value Log likelihood AIC Akaike weight

A: Single-variable models, with Exp as a random effectSpp 0.453 1,262 0.501 �1088.843 2,185.686 0.049FG 1.726 1,262 0.189 �1086.664 2,181.329 0.433PDC 0.132 1,262 0.717 �1086.485 2,180.969 0.518

B: Multivariable models, with Exp as a random effectSpp � PDC I � Spp*PDC �1092.147 2,196.295

Spp 0.377 1,260 0.539PDC 0.132 1,260 0.716Spp*PDC 2.234 1,260 0.135

C: Multivariable models, with FG as a random effect nested in ExpSpp � PDc � Spp*PDc �1092.097 2,198.195

Spp 0.496 1,220 0.481PDC 0.152 1,220 0.697Spp*PDC 2.001 1,220 0.157

Spp is the number of species in an experimental unit; FG is the number of functional groups, and PDC is phylogenetic diversity. Results in Model A are forsingle-variable, fixed-effect models with experiment (Exp) included as a random effect. Model B is a model with Spp and PDC and their interaction with the Exprandom effect. Model C is the two-variable model with FG as a random effect nested within Exp. Akaike weights can be interpreted as the probability that modeli is the best fit to the observed data among a set of models (16).

Cadotte et al. www.pnas.org/cgi/content/short/0805962105 10 of 10