Experimental evidence that evolutionarily diverse assemblages result in higher productivity

Marc W. Cadotte

doi:10.1073/pnas.1301685110

New Research In

Edited by Harold A. Mooney, Stanford University, Stanford, CA, and approved April 22, 2013 (received for review January 28, 2013)

Abstract

There now is ample experimental evidence that speciose assemblages are more productive and provide a greater amount of ecosystem services than depauperate ones. However, these experiments often conclude that there is a higher probability of including complementary species combinations in assemblages with more species and lack a priori prediction about which species combinations maximize function. Here, I report the results of an experiment manipulating the evolutionary relatedness of constituent plant species across a richness gradient. I show that assemblages with distantly related species contributed most to the higher biomass production in multispecies assemblages, through species complementarity. Species produced more biomass than predicted from their monocultures when they were in plots with distantly related species and produced the amount of biomass predicted from monoculture when sown with close relatives. This finding suggests that in the absence of any other information, combining distantly related species in restored or managed landscapes may serve to maximize biomass production and carbon sequestration, thus merging calls to conserve evolutionary history and maximize ecosystem function.

Evidence showing that ecosystem function is positively related to the number of species in an assemblage (1 ⇓⇓⇓⇓⇓–7) has profoundly changed how scientists and policy makers view the potential impact of species extinction (8, 9). However, not all species contribute equally to emergent functioning of an assemblage. How much species uniquely contribute to ecosystem functioning depends on several different aspects of species ecologies, including how variable their population dynamics are (10 ⇓–12) and how susceptible they are to local extinction (13), as well as how much they overlap in their resource requirements (14, 15) and the relative distinctiveness of species in multivariate functional trait space (16, 17).

Measuring the meaningful differences in species’ ecologies, across large numbers of species, has proven difficult. However, the simple assumption that the more time that has passed since two species shared a common ancestor, the higher the probability they have ecologically diverged appears to provide a powerful explanation for how biodiversity affects ecosystem function (15, 18 ⇓–20). Previous analyses of the relationship between plant evolutionary history and biomass production used existing experiments that manipulated species richness. These experiments often were biased toward distantly related species, as experimenters chose to include representatives of disparate lineages (e.g., a grass or a nitrogen fixer); thus, the observed relationship between evolutionary history and productivity may have been driven by the relatively few close relatives used in these experiments (19).

Further, previous analyses of the relationship between phylogenetic diversity (PD) and ecosystem function relied on the untested assumption that distantly related species are more likely than closely related species to exhibit reduced niche overlap. The traditional explanation for richness–productivity relationships is that larger assemblages of species have a greater probability of including species that have low niche overlap and thus can access different resources (1, 21). This complementarity in resource use may be estimated by quantifying the performance of species i in polyculture against the expected performance from its monocultures (22, 23). Polycultures also might appear more productive compared with monocultures via a selection effect—if highly productive species dominate the polyculture, displacing low-productivity species (22). Thus, if distantly related species show reduced resource overlap, then we should observe greater complementarity when they are combined.

Here, I report the results of a biodiversity–ecosystem function experiment that explicitly manipulated the phylogenetic distances separating species in an assemblage. A total of 97 4-m² plots were sown with between 1 and 4 species from a pool of 17 species (see Materials and Methods for details on the experiment), all of which were grown in at least three replicated monocultures. Polycultures belonged to one of the following treatments: constituent species separated by short, medium, or large phylogenetic distances (Fig. 1 A and B). These treatments created differences in plot PD, measured as the total summed phylogenetic branch lengths in an assemblage (19, 24). The experimental design fully crossed richness and phylogenetic distances. Generally, there was a positive correlation between richness and PD in the polycultures (r = 0.51; Fig. 1B), but the phylogenetic treatments maintained a high degree of variation in PD within species richness classes (; ; ).

Fig. 1.

(A) The time-calibrated molecular ML phylogeny of the species used in this experiment. The gray arrowheads indicate example two-species combinations that constitute the phylogenetic treatments. (B) PD across realized richness. Treatments produced a gradient in PD within each richness level.

Results and Discussion

In agreement with other studies manipulating species richness, I found an effect of richness on biomass production [β_Rich = 15.4; 95% confidence interval (CI), 9.20–21.60; Fig. 2A; see Results for parametric statistics). I also analyzed the effect of several different measures of diversity [incorporating relative abundances and/or phylogenetic distances (Materials and Methods)] and found that although several of them predicted biomass production (SI Results), PD was the strongest predictor (β_PD = 0.12; 95% CI, 0.08–0.17; Fig. 2B), as evidenced by model selection using Akaike weights (AW_Richness = 0.01, vs. AW_PD = 0.74), as well as explained variance (R²_Rich = 0.21, vs. R²_PD = 0.28). The effect of PD on biomass production equates to an increase of about 12 g/m² for every 5 My.

Fig. 2.

The relationship between plot biomass production and species richness (A) and PD (B). Lines are estimated from linear regressions, with models shown in the upper left of each plot.

Despite the fact that the original design of this experiment crossed richness and phylogenetic relatedness, these two measures were not orthogonal (e.g., Fig. 1B). Regardless, I used a likelihood ratio test to compare a linear model including both effects, with and without an interaction term, with the PD-only model. The more complex models did not provide a better predictive ability over the PD-only model (PD vs. PD + Rich: X² = 0.653, P = 0.420; PD vs. PD + Rich + PDxRich: X² = 0.184, P = 0.668).

I then estimated complementarity and selection effects following Loreau and Hector (22), and found that complementarity increased with species richness (β_Rich = 8.80; 95% CI, 1.10–16.51; AW < 0.01; Fig. 3A), although the relationship was relatively weak (R² = 0.12). However, complementarity was strongly related to PD, which was selected as the best predictor (β_PD = 0.11; 95% CI, 0.08–0.15; AW = 0.87; R² = 0.50; Fig. 3B). The plots with the greatest PD resulted in the greatest complementarity, whereas plots with close relatives generally resulted in a negligible or negative complementarity effect (Fig. 3B). A negative complementarity effect between close relatives might result from negative interactions that reduce growth in one or both antagonists—such as chemical or physical interference competition (22), or perhaps increased pathogen sharing among close relatives—which has been shown to influence ecosystem function (25).

Fig. 3.

The complementarity effect was significantly related to several diversity measures, including richness (A) but was explained best by PD (B; ●, plots exhibiting transgressive overyielding). The selection effect was predicted best by a phylogenetic measure that quantified the imbalance of abundances across the nodes in the phylogeny (IAC) (C). The single best predictor of the overall biodiversity effect was PD (D).

There was no relationship between either richness or PD and the magnitude of the selection effect (SI Results). Instead, the selection effect was related to measures accounting for the abundances of species, including both phylogenetic metrics—namely a measure of the relative skew of abundances across internal nodes in the phylogeny [imbalance of abundances among clades (IAC), β_IAC = 0.61; 95% CI, 0.34–0.88; R² = 0.35; AW = 0.977; Fig. 3C]—and taxonomic diversity (Shannon index, H′: β_H = −37.0; 95% CI, −62.96 to −11.12; R² = 0.17; AW = 0.008; Simpson index: β_SImp = −58.07; 95% CI, −97.18 to −19.18; R² = 0.18; AW = 0.011). IAC measures the deviation of abundances at internal nodes from a null distribution, where abundances are proportional to the number of descending lineages (26). Thus, high IAC values correspond to an imbalance in abundances in which individual species or clades have disproportionately high abundance. The fact that IAC is a better explanation for the selection effect than either the Shannon or Simpson (nonphylogenetic) index indicates it is more efficacious to include information about the abundances of close relatives than only about individual species.

The sum of the complementarity and the selection effect is the “biodiversity effect” (∆Y), which is the difference between observed and expected polyculture biomass (22). Again, PD was the best predictor of ∆Y [β_PD = 0.14; 95% CI, 0.07–0.21; R² = 0.31; Akaike information criterion (AIC) = 394.26; Fig. 3D], but this was only marginally better than IAC (β_IAC = 0.84; 95% CI, 0.43–1.26; R² = 0.30; AIC = 395.28). Given that these two diversity measures predict different aspects of the biodiversity effect (complementarity vs. selection effect), I looked at a model that included both these variables. PD and IAC were not correlated to each other (r = 0.01). Compared with the single predictor models, the model with both PD and IAC as main effects was a substantially better predictor of ∆Y (AW = 0.99). A model including a PD–IAC interaction was not a significant improvement (AIC_main _effects = 375.95 vs. AIC_interaction = 375.85). Thus, the magnitude of the productivity increase in polycultures is explained, to a large degree (R² = 0.59), by the combination of two independent phylogenetic measures: the total evolutionary history (PD), which underpins observed complementarity, and a measure of the imbalance in relative abundances across the phylogeny (IAC), which predicts selection effects.

I am using the statistical measure of complementarity as a potential measure of niche differences. Certainly, the measure of complementarity used here can reflect stabilizing niche differences, but not necessarily so (14, 27). Species combinations may show statistical complementarity even when they access the same resources or when the community has not reached equilibrium (27). Thus, transgressive overyielding (when polycultures are more productive than the best performing monoculture of the constituent species) supplies more stringent evidence of niche complementarity (27). Almost half the polycultures (48%) showed transgressive overyielding, and these plots also showed higher complementarity (mean complementarity effect, 26.49 with transgressive overyielding and −3.35 for plots not exhibiting overyielding, and a difference of 29.84; 95% CI, 17.63–42.05; Fig. 3B).

The oft-observed relationship between species richness and biomass production has been profoundly important for understanding the potential consequences of species extinction. However, mechanistic explanations largely have been lacking and instead depend on an increased probability of including complementary species in diverse assemblages. The results of many of these biodiversity–ecosystem function experiments have included high variance in biomass production among plots with the same numbers of species. Taken together, this previous work has not been able to provide a method for a priori prediction about which species combinations should result in the highest biomass production. Recent work has shown that phylogenetic measures explain biomass variation better (12, 18). Here, I show that by using evolutionary relationships, one can predict which species combinations should maximize complementarity and thus polyculture biomass production.

It is important to note that phylogenetic relatedness is not an ecological mechanism in itself. Using a measure such as PD necessarily includes several assumptions about the mode and tempo of ecological differentiation. The basic assumption is that phylogenetic distances are linearly related to ecological differences, which is unrealistic, and future work must develop realistic models relating evolutionary time to ecological differences (28, 29). Furthermore, traits ultimately must explain these ecological differences, yet analyses of plant traits have not been able to provide a more powerful explanation of biomass production (16, 17). It might be that the correct traits, or combinations of traits, have not been measured adequately. Alternatively, it might be that the links between traits and ecological differences are sufficiently complex or idiosyncratic, that an integrated measure such as PD performs better. Using phylogenetic distances as an ecological predictor assumes that species have differentiated from one another (proportional to time) but is ambivalent about which trait(s) have evolved or even whether it is the same trait evolving across different clades.

Phylogenetic relationships are an efficacious predictor of ecosystem function and shed light on why previous experiments have found a positive relationship between productivity and species richness. By using phylogenetic relationships, we can predict which species combinations should be the most productive. These findings support calls to protect evolutionarily distinct species (30, 31) and are in line with recent moves toward conservation goals that include the maintenance of ecosystem function. Conservation needs to consider multiple priorities and species, and evolutionary distinctiveness should be an important part setting conservation goals.

Materials and Methods

This experiment was conducted in a formerly grazed old field at the University of Toronto’s Koffler Scientific Reserve, located near King City, Canada (44°02′ N, 79°31′ W). In both fall 2009 and early spring 2010, a 30 × 30-m field was plowed and disked. Within this field, 100 (10 rows of 10) 2 × 2-m plots were evenly spaced, separated by 1 m.

Species and Experimental Treatments.

In May 2010, the plots were seeded with 1, 2, or 4 plant species from a pool of 17 species, including Andropogon gerardii Vitman (Poaceae), Schizachyrium scoparium (Michx.) Nash (Poaceae), Elymus canadensis L. (Poaceae), Elymus trachycaulus (Link) Gould ex Shinners (Poaceae), Asclepias tuberosa L. (Asclepiadaceae), Doellingeria umbellata (Mill.) Nees (Asteraceae), Liatris cylindracea Michx. (Asteraceae), Rudbeckia hirta L. (Asteraceae), Solidago nemoralis Aiton (Asteraceae), Oligoneuron album (Nutt.) G. L. Nesom (Asteraceae), Desmodium canadense (L.) (Fabaceae), Lespedeza capitata Michx. (Fabaceae), Monarda fistulosa L. (Lamiaceae), Pycnanthemum tenuifolium Schrad. (Lamiaceae), Pycnanthemum virginianum (L.) T. Dur & B. D. Jacks. ex B. L. Rob. & Fernald (Lamiaceae), Penstemon digitalis Nutt. ex Sims (Scrophulariaceae), and Penstemon hirsutus (L.) Willd. (Scrophulariaceae). These species are strongly associated with tall grass prairie in Ontario and currently do not occur on the reserve in high abundance. The rationale for their use was to establish a “naïve” community, in which, with a priori knowledge, each species has an equal chance of encountering beneficial or negative interactions (e.g., soil pathogens, herbivores, pollinators). Thus, specific species should not have an advantage because of their recent history at the site. Seeds for these species were obtained from Pterophylla Native Plants and Seeds. Each plot was seeded with 3,000–4,000 seeds (which approximates natural seed fall for 4 m²), evenly divided among the constituent species. After seeding, the soil and seed were compacted using a lawn roller.

After seeding, the plots did not receive any rainfall for 15 d, and I installed a sprinkler system with a gas-powered pump to pull water from four cattle tanks to keep the seeds moist for germination. Also, to suppress weed growth between plots, the interplot space was covered with landscape cloth and pea gravel. Plots were consistently weeded, removing all species not seeded in a plot.

Four species did not germinate in the plots (D. umbellata, L. cylindracea, O. album, and P. digitalis). In late fall 2010, L. cylindracea and O. album were replaced with Solidago altissima L., D. umbellata was replaced with Euthamia graminifolia (L.) Nutt., and P. digitalis was reseeded. All new seedings were done at the same seeding densities as the species they were replacing. Unfortunately, neither E. graminifolia nor the P. digitalis reseeding resulted in sufficient germination. Thus, these two species were dropped from the experiment, and several of the four-species plots became three-species treatments and several of the two-species plots became monocultures.

All species monocultures were replicated three times. The two- and four-species treatments were fully crossed with three PD treatments: (i) small phylogenetic distances, in which plots comprised closely related species; (ii) large phylogenetic distances, in which plot members were distantly related; and (iii) medium phylogenetic distances, in which plots contained moderately distantly related species or a mixture of close and distant relatives (Fig. 1). Because there were fewer possible combinations for the small and large phylogenetic distance treatments, these were replicated seven times each for both the two- and four-species plots. The medium treatments were replicated nine times for both two- and four-species plots.

Sampling.

In August 2012, 0.1 × 1-m quadrats were placed in each plot, 0.25 m away from any plot edge. All above-ground biomass was removed, with stems clipped just above the soil surface. Biomass was sorted into constituent species as well as litter (i.e., dead or dying material not connected to a living stem). All samples were dried in a VWR drying oven (VWR International) at 50 °C for 2 d, then weighed using a Mettler Toledo ML Series precision balance.

Phylogeny.

For each species used in this experiment, I queried GenBank (32) for five commonly sequenced genes: matK, rbcl, ITS1, ITS2, and 5.8s (Table S1). All the species, bar one (S. altissima), had at least one gene represented in GenBank; for the missing species, I used gene sequences from a congeneric relative not included in these experiments (Solidago rigida). I also included two representatives of early diverging angiosperm lineages—Amborella trichopoda and Magnolia grandiflora —as outgroup species. Sequences were aligned using MUSCLE (33), and best-fit maximum likelihood (ML) models of nucleotide substitution for each gene were selected using the Akaike information criterion, as implemented in Modeltest (34, 35).

The ML phylogeny using the PhyML algorithm with a BIONJ starting tree (36, 37) was used to estimate the phylogeny. Nodal support was estimated using approximate likelihood-ratio test (aLRT) scores, which have been shown to correlate with ML bootstrap scores but require much less computational time (37). I then used a semiparametric rate-smoothing method (38) to transform the phylogeny to an ultrametric tree using the chronopl function in the R package ape (39). I iterated this function across a suite of rate-smoothing parameters and found that the parameter value that maximized the likelihood was 1. I then time calibrated the tree by using the chronogram function, with the scale set to 160 My—a conservative estimate for the monocot and dicot split (40, 41). The time-calibrated ultrametric ML tree is shown in Fig. 1.

Statistical Analyses.

The ability of different measures of diversity to explain variation in biomass was compared. These measures of diversity are listed in Table S2. The reason to compare such measures is that different phylogenetic measures are sensitive to different aspects of topology (e.g., imbalance in diversification rates vs. length of terminal branches) (26). Moreover, it is important to compare taxonomic diversity measures that include abundance (i.e., Shannon and Simpson indices) with abundance-weighted phylogenetic measures. I compared linear models of biomass against each of the diversity measures using Akaike weights (42). Although all model comparisons are not shown in the main article, they are shown in Tables S3–S6.

I quantified complementarity and selection effects according to Loreau and Hector (22). Complementarity is defined as the average difference between the observed relative yield of species in polyculture and the expected yield based on biomass production in monoculture. The selection effect measures the covariance between the relative yield in polyculture and monoculture productivity (i.e., whether species that exhibit high productivity in monoculture also have high relative yield in polycultures). The combination of complementarity and selection is the biodiversity effect, or the amount of biomass produced in polycultures above the expectation from the monocultures. To determine whether complementarity, selection, or biodiversity effects were related to diversity, I compared linear models, with the diversity measures as predictors, comparing them using Akaike weights.

Finally, I assessed transgressive overyielding using the log response ratio (3), , where y_i is the biomass of polyculture i and y_m,i is the mean monoculture biomass of the most productive species in polyculture i. Thus, a value of 0 means that the polyculture is as productive as the maximum-performing species. Positive values result from transgressive overyielding.

Acknowledgments

I thank Art Weis and the University of Toronto’s Koffler Scientific Reserve for supporting this work. Many students assisted with this experiment, and their help has been invaluable, especially that of Russell Dinnage, Robin Marushia, Ryan Munroe, and Ciprian Munteanu. Caroline Tucker and two anonymous reviewers provided valuable feedback on an earlier version of this manuscript. I acknowledge generous funding from the Natural Sciences and Engineering Research Council of Canada (386151), the Canada Foundation for Innovation, and the Ontario Research Fund.

Footnotes

↵¹E-mail: mcadotte{at}utsc.utoronto.ca.

Author contributions: M.W.C. designed research, performed research, analyzed data, and wrote the paper.
The author declares no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1301685110/-/DCSupplemental.

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Biological Sciences
Ecology

[1] ↵
Tilman D,
et al.
(2001) Diversity and productivity in a long-term grassland experiment. Science 294(5543):843–845.
OpenUrl Abstract/FREE Full Text

[2] Tilman D,

[3] et al.

[4] ↵
Tilman D,
Wedin D,
Knops J
(1996) Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379:718–720.
OpenUrl CrossRef

[5] Tilman D,

[6] Wedin D,

[7] Knops J

[8] ↵
Cardinale BJ,
et al.
(2006) Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443(7114):989–992.
OpenUrl CrossRef PubMed

[9] Cardinale BJ,

[10] et al.

[11] ↵
Cardinale BJ,
et al.
(2007) Impacts of plant diversity on biomass production increase through time due to complimentary resource use: A meta-analysis of 45 experiments. Proc Natl Acad Sci USA 104(46):18123–18128.
OpenUrl Abstract/FREE Full Text

[12] Cardinale BJ,

[13] et al.

[14] ↵
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Experimental evidence that evolutionarily diverse assemblages result in higher productivity

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Experimental evidence that evolutionarily diverse assemblages result in higher productivity

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