Oikos 118: 1703Á1711, 2009 doi: 10.1111/j.1600-0706.2009.17585.x, # 2009 The Authors. Journal compilation # 2009 Oikos Subject Editor: Daniel Gruner. Accepted 24 April 2009
Species richness enhances both algal biomass and rates of oxygen production in aquatic microcosms
Lillian D. Power and Bradley J. Cardinale
L. D. Power and B. J. Cardinale ([email protected]), Dept of Ecology, Evolution and Marine Biology, Univ. of California-Santa Barbara, Santa Barbara, CA 93106, USA. LDP also at: Biomathematics Graduate Program, North Carolina State Univ., Raleigh, NC 27695, USA.
Accelerating rates of species extinction have generated much recent interest in understanding how biodiversity affects the functioning of ecosystems. Experiments to date have shown communities composed of fewer species generally capture a smaller fraction of available resources, and achieve lower standing stock biomass than more diverse communities. However, it is uncertain how changes in biodiversity and the resulting alterations in biomass affect the rates of important ecological processes like primary production, which regulates fluxes of CO2 and O2 between the biotic and abiotic components of the environment. Here we show that species richness influences not only the standing stock biomass of primary producers, but also rates of gross primary production measured by changes in O2 concentrations in aquatic systems. We manipulated the richness of five widespread species of algae in laboratory microcosms and then quantified how richness impacts algal biomass, rates of gross primary production (GPP), and the ratio of production to respiration. Algal biomass increased by a factor of 1.82 for each level of species richness, and GPP by a factor of 1.20, for each additional species. Production to respiration ratios increased about 10% for each additional species, indicating that systems with more species were increasingly autotrophic Á that is, they produced more O2 than they consumed, and accumulated CO2 faster than they released it. These trends were driven by two highly productive species that became co-dominant in species rich polycultures at the expense of other taxa. Our experiment suggests that changes in biodiversity may influence not only the rates at which O2 and CO2 are produced and released in ecosystems, but also the total amount of carbon that is sequestered and stored as biomass.
Over the past several decades, more than 150 experiments certain communities, such as terrestrial grasslands that are have manipulated the number of species of bacteria, fungi, dominated by annual plants, it is often assumed that the plants and animals in a variety of terrestrial and aquatic amount of aboveground biomass accrued over a single ecosystems to examine how biodiversity affects the effi- growing season is a reasonable proxy for rates of primary ciency by which communities capture limited resources and production. convert those into new biomass (Hooper et al. 2005, However, several authors have suggested that long-term Balvanera et al. 2006). Recent meta-analyses of these accumulation of standing biomass might not respond to experiments have shown that diverse communities are, on diversity in the same way as instantaneous rates of average, twice as efficient at capturing limited resources and production (Stocker et al. 1999, Petchey 2003, Balvanera achieve roughly twice the biomass as communities that et al. 2006). For example, Schulze (1996) argued that the are highly reduced in diversity (Cardinale et al. 2006, fluxes of CO2 that are driven by primary production should Stachowicz et al. 2007). While there appears to be be independent of plant diversity because these fluxes are considerable generality in how species richness impacts instead regulated by environmental constraints after plant the standing stock biomass achieved by a group of cover exceeds a relatively low critical value. In contrast, organisms, it is much less certain how species diversity Korner (2004) argued that plant species richness should affects the rates of ecological processes like primary stimulate photosynthesis and the rate at which plant production. This is because studies have only rarely communities sequester carbon from their environment; quantified rates of photosynthesis, or the fluxes of O2 or however, this might not translate into long-term carbon CO2 into/out of plants. Rather, most experiments have storage due to increased turnover of biomass. These simply examined how the initial number of species placed contrasting perspectives are not easily resolved by ecological in some experimental unit (field plot, greenhouse pot, etc.) theory, much of which predicts that species diversity will impacts standing biomass (mass area 1 or volume 1) after increase standing stock biomass and, in turn, the fraction of some period of growth (usually months or years). For available carbon, nutrients, and other inorganic resources
1703 that are stored in tissue (Tilman et al. 1997, Loreau 1998a, both standing stock biomass, as well as rates of primary Cardinale et al. 2004, Ives et al. 2005). However, these production that control fluxes of O2. models tend to focus on communities that have achieved an equilibrium biomass where gains and losses of carbon are balanced (i.e. zero net change). As such, they offer few Methods predictions about how species diversity might influence instantaneous fluxes of carbon between biotic and abiotic Focal species components of the environment (but see Cardinale et al. 2004, Weis et al. 2007 for mathematical explorations of the We used five species of freshwater algae that are common in of the effects of diversity on short-term changes in standing North American phytoplankton communities (Graham and stocks for non-equilibrium communities). Wilcox 2000). These included two species of diatoms Á Unfortunately, empirical work has also failed to provide Achnanthidium minutissimum (Ac), Navicula cryptocephalus any consistent answer to how species richness impacts the (Na), and three chlorophycean green algae Á Chlorella stocks verses fluxes of energy and matter. Balvanera et al. vulgaris (Ch), Scenedesmus quadricauda (Sc) and Selenastrum (2006), who analyzed biodiversity studies performed with minutum (Se). All species were acquired from commercial primary producers, decomposers and higher consumers, culture collections, two from Carolina Biological Supply found no difference in the magnitude of diversity effects on (Ch, Sc), and three from the culture collection at the Univ. long-term accumulation of standing stocks verses short- of Texas at Austin (Ac, Na, Se). Aside from being abundant term rates of carbon flux. Schmid et al. (2009), who and common in lakes, these taxa were chosen because all combined the data of Balvanera et al. (2006) with those grow well under laboratory conditions using common from the meta-analysis of Cardinale et al. (2006), came to a growth media, and are morphologically diverse, making different conclusion, stating ‘‘Our analyses clearly showed them easy to distinguish from one another. that there were differences between stocks and rates and that, in fact, biodiversity influenced stocks more strongly and more positively than rates.’’ Srivastava et al. (2009), Experimental design who performed a meta-analysis of studies focusing more The experimental units for this study were 105 sterilized narrowly on experiments performed with detritivores glass jars filled with 120 ml of Chu algal growth media. Jars feeding on clearly defined carbon sources (usually leaf were randomly assigned to one of 21 treatments represent- litter), came to an even different conclusion. Their results ing each of the five species grown alone in monoculture, five revealed no evidence that detritivore diversity influences the combinations of 2, 3 and 4 species selected randomly from standing stock biomass of carbon resources; yet, there was all possible combinations, and all five species grown strong evidence that detritivore diversity increased the rate together in full polyculture. Species were inoculated at the at which carbon was sequestered from these resources. start of the experiment according to a replacement-series Of course, one reason these meta-analyses have poten- design where total algal density was held constant at tially come to differing conclusions is that they have 5000 cells ml 1 across all levels of species richness, S; compared biodiversity effects on stocks and fluxes that thus, the number of cells of any single species added to an were typically measured for different response variables experimental unit was 5000 1/S. Each species monocul- across different groups of organisms. It is fairly rare to find ture was replicated in nine jars, each two through four experiments that have manipulated the diversity of a focal species polyculture was replicated in three jars, and the full group of organisms and then simultaneously measured the five species polyculture was replicated in 15 jars (for 105 effect of diversity on both stocks and fluxes of a focal jars total). After inoculation, experimental units were placed material. However, aquatic ecologists have recently started upright at randomly selected positions on a shaker table, to weigh in on this debate by using model systems of algae and exposed to cool white fluorescent lights that were set to in which it is tractable to measure both the accrual of an 18:6 hour light dark cycle inside a growth chamber biomass as well as the instantaneous rates of photosynthesis where temperature was maintained at 178C over the course (Bruno et al. 2005, 2006, Griffin et al. 2009). These of the experiment. systems allow one to investigate how species diversity influences both the fluxes of O2 and CO2 between the abiotic and biotic components of the environment, as well Sampling as the total carbon that is accumulated and stored as biomass. On each of days 13, 18 and 32 of the experiment we In this paper we ask whether changes in the species randomly selected three replicates of each species mono- richness of freshwater primary producers have similar, or culture, one replicate of each 2Á4 species polyculture, and qualitatively different impacts on standing stock biomass five replicates of each full species polyculture and destruc- verses rates of primary production. We manipulated the tively sampled these experimental units to measure rates of richness of five common species of freshwater algae in primary production and standing algal biomass (35 jars per laboratory microcosms and then measured concentrations date). Primary production was measured using the light/ of chlorophyll-a (a measure of algal biomass) and rates of dark bottle incubation technique that characterizes rates of gross primary production (via changes in O2 concentra- phytoplankton production via changes in the concentration tions) over a period spanning roughly 8Á10 generations of of dissolved O2 (Wetzel 1983, Bott 1996). We began by growth. Our results suggest that, under the simplified measuring initial levels of O2 in the jars using a YSI model conditions of this experiment, species richness influences 556 probe. Jars were then sealed with air-tight stoppers,
1704 covered with tin foil to prevent exposure to light, and set We continued with our analyses by using general linear back on the rotating shaker table for a 1 h incubation. At models (GLMs) to test whether each of the four response the end of the incubation, we again measured levels of variables varied as a function of algal species richness, the dissolved oxygen to assess the amount of respiration, R, as day of measurement, and/or the richness day interaction. the rate at which O2 was consumed in the dark. Jars were Note that, although we did measure the response variables then sealed a second time, placed back on the shaker table, through time, this was done by destructively sampling and incubated for a second 1 h period while exposed to cool different experimental units. Thus, the data could not be white fluorescent lights. Dissolved oxygen concentrations analyzed as a repeated measures model. Instead, the day of were measured at the end of the light incubation to assess measurement was included as a categorical variable in the net primary production, NPP, as the rate of O2 accumula- models, while richness was considered a continuous vari- tion in the light. Gross primary production was then able. These GLMs essentially characterize the ‘net’ effect of calculated as GPP RNPP. species richness when values are averaged across all species After measuring primary production, we took 1 ml sub- and species combinations for a given level of richness. In samples of algae from each jar and preserved them in some scenarios, researchers may be less interested in the net Lugol’s solution for later determination of algal densities effect of diversity, and more interested to know whether a (described in the next paragraph). The remaining contents diverse community is any more productive than its single of the jar were filtered onto a GFF filter, which was placed most productive species (a phenomenon called ‘transgres- in a 10 ml Falcon tube with 90% EtOH and stored for 48 h sive overyielding’, Hector et al. 2002). There have been a in a freezer to lyse algal cells and extract photopigments. number of test statistics used to detect transgressive over- Samples were then analyzed spectrophotometrically using yielding in previous studies, some more conservative and the trichromatic method to quantify the concentrations of some less so (Loreau 1998b, Hector et al. 2002, Hooper chlorophyll-a, which is a widely used estimate of algal and Dukes 2004). A simple metric that we have used biomass (Steinman and Lamberti 1996, Wetzel and Likens previously (Cardinale et al. 2006, 2007) is the log response 2000). ratio LRtrans ln(yip¯ =yimˆ ) where /yimˆ is the mean To determine algal densities in each experimental unit, for all replicates of the species that attains the highest value we counted cells on a hemacytometer using an compound in monoculture, and yip¯ is the mean for all replicates in microscope. Mean cell biovolume for each species was polyculture. This unitless metric quantifies the proportional determined by measuring the dimensions of ]100 cells difference between the mean value of the most species rich from the original laboratory cultures, and fitting species polyculture and that of the taxon having the highest mean dimensions to a geometric shape (Hillebrand et al. 1999). value of y in monoculture mˆ : If LRtrans is significantly Mean cell biovolume was then multiplied by cell density greater than zero, this indicates that diverse polycultures to obtain population-level estimates of biovolume in the achieve a higher response than even the single most microcosms. Time constraints prevented us from perform- productive species in the community. ing counts and biovolume estimates on all 105 experimental Although is not possible to separate the individual units, so we focused on obtaining data for each of species contributions of different species to total chlorophyll-a or monocultures as well as the full 5-species polyculture. These GPP measured in a polyculture, it is possible to use were chosen because they are the treatments needed for estimates of species-specific biovolumes to assess the relative calculating metrics of relative yield, and for quantifying the ‘yields’ (as total biovolume) of a species when grown alone underlying cause of diversity effects. versus in polyculture. We used biovolumes to calculate the proportional deviation, Di, of the biovolume for each species i in polyculture from its expected value as Data analyses Oi Ei Di Our analyses focused on how four dependent variables Á (1) Ei 1 algal biomass (mg chlorophyll-a l ), (2) rates of gross where Oi is the observed biovolume of species i in a 1 1 primary production (mg O2 produced l h ), (3) mass- polyculture, and the expected value Ei is simply 1/5th of the specific productivity (GPP mg chlorophyll-a 1), and (4) observed biovolume in monoculture, which represents the the ratio of primary production to respiration (GPP/R) initial proportion for species inoculated in a 5-species Á responded to manipulations of algal species richness, S. polyculture (Loreau 1998b). A positive value of Di indicates We began by modeling each of the dependent variables as a that a species produces more biomass in polyculture than function of S using four common mathematical expressions would be expected from its initial relative biovolume in the that have been used to describe the effects of species richness community. In contrast, negative values of Di indicate that on ecological processes. These included linear, log, power, a species achieves less biovolume in polyculture than would and MichaelisÁMenton hyperbolic functions. Parameter be expected. Thus, Di essentially measures the degree of estimates for each function were generated by maximum over or under-yielding by any single species when placed likelihood. The Akaike information criterion (AIC) was together with other species. then used to calculate the likelihood that each function was Lastly, we used the statistical method developed by Loreau the best fit to the data, which were then expressed as Akaike and Hector (2001) and later improved by Fox (2005), to weights to give a relative weight of evidence for a function partition the net effect of diversity on biovolumes into three (readers not familiar with model selection using likelihood statistically additive components: ‘dominance effects’ (DE), may want to see Johnson and Omland 2004 for a ‘trait-dependent complementarity’ (TDC) and ‘trait inde- summary). pendent complementarity’ (TIC). Details on how these
1705 metrics are calculated are provided in the original references (Loreau and Hector 2001, Fox 2005). Here we simply describe what each metric represents biologically. DE quantifies that portion of the net diversity effect that occurs when a species comes to competitive dominance in poly- culture at the expense of other species (that is, other species decline in their relative yields). This is equivalent to what most researchers think about when they refer to the ‘selection’, ‘sampling’ or ‘selection-probability’ effect of diversity, which can be either positive or negative depending on whether the competitive dominant has high or low productivity (Huston 1997, Loreau 2000). TDC occurs when a species that achieves high biomass in monoculture performs even better than expected in mixture, but this does not come at the expense of other species. Positive values of TDC might occur if, for example, the growth of a highly productive species is facilitated in mixture by a species with low monoculture biomass, but not vice versa. TIC occurs when species in mixture perform yield more than expected, but this is both independent of their monoculture biomasses and not at the expense of other species. Positive values of TIC indicate that interspecific effects on biomass are smaller than intraspecific effects. One way this can happen is when species exhibit niche differentiation. Thus, we partitioned the net changes in species biovolumes between monocultures and the full 5-species polycultures to assess whether diversity effects Figure 1. (A) The biomass of each algal species in monoculture, were most due to DE, TDC or TIC. and of the full 5-species polyculture measured over the 32 day duration of the experiment, and (B) the relative abundance of each species in the 5-species polyculture on the same dates. Each data point is the mean of n 3 replicate microcosms of the mono- Results cultures, or five replicates for the full species polyculture 91 SEM.
Analyses of the time-series data suggest that the 32-day duration of the experiment was sufficient for algal biomass richness and the four response variables could not be and the relative abundance of species to reach constant distinguished from linear, and treat them as such in analyses values. We found no significant difference in the biomass that follow. of any of the five species growing alone in monoculture Total algal biomass in the microcosms increased as a when each was compared among days 13, 18 and 32 of the function of algal species richness (Fig. 2A). The effects of experiment during measurement (ANOVA, all p0.10, algal richness on biomass were constant through time, as Fig. 1A). Likewise, there was no difference in the biomass of evidenced by the lack of any interaction between species the full 5-species polyculture on the three dates of richness and the day of measurement (Table 2A). When we measurement (ANOVA, p 0.68). Species composition eliminated the richness day interaction, parameter esti- in the polyculture also appeared to be stable over the three mates of a GLM suggested that algal biomass increased by a dates of measurement with green algal species dominating. 0.26 Chlorella vulgaris represented a constant 46% of cell mean 10 (1.82 ) for each additional species in the densities in polyculture, and Scenedesmus quadricauda microcosms (Fig. 2A). Species richness explained one- dominated 3593 (mean of 3 dates9SD). The diatoms fourth of all variation in biomass, with the remaining being Achnanthidium minutissimum and Navicula cryptocephalus largely explained by variation among the different species or both went extinct by the second date of measurement (Fig. species combinations. Indeed, when we took the residuals 1B). Collectively, these trends suggest that the experiment from the regression in Fig. 2A, differences among the ran long enough to achieve a stable biomass and species species or species combinations explained 81% of the composition. residual variation (ANOVA, F20,83 18.15, pB0.01, We found no evidence that any particular function was a R2 0.81). Although the net effect of species richness on superior fit to the data (Table 1). Although the hyperbolic biomass was positive, diverse communities produced no function had the lowest AIC for three out of four response more or less biomass than the species achieving the highest variables, it was not a significantly better fit than any of the value in monoculture. The log response ratio LRtrans was other options. Indeed, all of the AICs were within 1.0 of not different from zero (mean995% CI 0.0690.08). each other, and the Akiake weights suggested that each This indicates that the 5-species polycultures produced the function was equally likely. These results suggest that there same biomass as C. vulgaris, which was the largest of the five was no qualitative difference in how algal species richness species in cell biovolume, and which achieved the highest influenced levels of algal biomass verses rates of primary amounts of chlorophyll in monoculture when averaged over production. For the sake of simplicity and presentation, we the three dates of measurement (Fig. 2A). hereafter assume that the relationships between species
1706 Table 1. A comparison of how well different functions (linear, log, power, and the MichaelisÁMenten hyperbolic) describe the relationship between algal species richness S and each response variable measured in this study: (A) algal biomass, (B) gross primary production (GPP), (C) the ratio of production to respiration, and (D) biomass specific production. For each function, parameters were estimated using maximum likelihood. The Akaike information criterion (AIC) was then used to calculate the likelihood that each model mi was the best fit to the data, L(mijy), among the four models. The likelihood was expressed as an Akaike weight, wi, which gives a relative weight of evidence for a model (higher values are more probable explanations of the data).
a b AIC DAIC L(mijy) wi
1 (A) y Algal biomass log10(mg chlorophyll-a l ) linear, y abS 1.94 0.26 159.47 0.60 0.74 0.21 log, y ab log(S) 2.13 0.63 159.01 0.14 0.93 0.26 power, y aSˆb 2.15 0.24 159.1 0.23 0.89 0.25 hyperbolic, y aS/(bS) 3.48 0.64 158.87 0.00 1.00 0.28 1 1 (B) y GPP (mg O2 l h ) linear, y abS 0.60 0.12 113.61 0.33 0.85 0.24 log, y ab log(S) 0.80 0.21 113.83 0.55 0.76 0.21 power, y aSˆb 0.68 0.34 113.4 0.12 0.94 0.27 hyperbolic, y aS/(bS) 1.39 1.07 113.28 0.00 1.00 0.28 (C) y Production:respiration linear, y abS 1.25 0.13 139.25 0.00 1.00 0.28 log, y ab log(S) 1.37 0.20 139.5 0.25 0.88 0.25 power, y aSˆb 1.36 0.14 139.47 0.22 0.90 0.25 hyperbolic, y aS/(bS) 1.72 0.25 139.65 0.40 0.82 0.23 (D) y Biomass specific production 1 log10(GPP mg chlorophyll-a min) linear, y abS 2 0.01 147.85 0.03 0.99 0.25 log, y ab log(S) 2 0.05 147.84 0.02 0.99 0.25 power, y aSˆb 2 0.02 147.84 0.02 0.99 0.25 hyperbolic, y aS/(bS) 2.11 0.06 147.82 0.00 1.00 0.25
In addition to algal biomass increasing as a function of removed, parameter estimates suggest that rates of GPP by 1 1 species richness, rates of gross primary production, GPP, algae increased by a mean 0.12 mg O2 l h (1.20 ) increased with algal richness (Fig. 2B). Effects of richness on for each additional species. Richness explained just 13% of primary productivity were consistent through time with no all variation in gross production. Of the residual variation, significant interaction between richness and the day of and additional 67% could be explained by differences measurement (Table 2B). Thus, when the interaction was among species or species combinations (ANOVA, F20,83
Figure 2. The effects of algal species richness on (A) algal biomass, (B) rates of gross primary production, (C) biomass specific rates of production, and (D) the ratio of productivity to respiration (P:R) in the microcosms. Each of the open circles represents data from one of 105 experimental units. The mean value achieved by each species in monoculture is also noted [Achnanthidium minutissimum (Ac), Navicula cryptocephalus (Na), Chlorella vulgaris (Ch), Scenedesmus quadricauda (Sc) and Selenastrum minutum (Se)]. Solid lines give the linear regression of y b0b1 S with parameter estimates and model fit shown. The insets in each panel show the mean value995% confidence intervals for LRtrans, which gives the proportional difference between the full 5-species polyculture and the species that achieves the highest value in monoculture.
1707 Table 2. Results of general linear models that examined how (A) algal biomass, (B) gross primary production, (C) the ratio of production to respiration, and (D) biomass specific production vary as a function of algal species richness, day of the experiment, and the richness day interaction.
DF Mean-square F-ratio p-value
1 (A) Algal biomass log10(mg chlorophyll-a l ) richness 1 10.40 26.87 B0.01 day 2 0.25 0.65 0.53 richness day 2 0.15 0.38 0.69 error 98 0.39 1 1 (B) GPP (mg O2 l h ) richness 1 3.65 14.13 B0.01 day 2 0.08 0.32 0.73 richness day 2 0.02 0.09 0.91 error 98 0.26 (C) Production:respiration richness 1 4.22 12.20 B0.01 day 2 0.44 1.26 0.29 richness day 2 0.04 0.11 0.90 error 98 0.35 (D) Biomass specific production (GPP mg chlorophyll-a 1) richness 1 0.77 2.25 0.14 day 2 0.04 0.12 0.88 richness day 2 0.05 0.15 0.87 error 98 0.34
8.41, pB0.01, R2 0.67). We found no significant trend the second and third dates of measurement (days 18 and 32), relating biomass specific rates of production to algal species the only metric that was significantly different from zero was richness (Fig. 2C, Table 2D), which suggests that any net DE, which suggests that diversity effects were driven by changes in GPP were driven primarily by changes in algal biomass. Note, however, that LRtrans was not different from zero for GPP (mean995% CI 0.0591.11, Fig. 2B). This indicates that the full species polycultures achieved the same rates of productivity as S. quadricauda, which was the most productive species in monoculture. Although S. quadricauda did not necessarily achieve the highest levels of biomass in monoculture (Fig. 1A), this taxon did have the highest rates of biomass specific production among the five species (Fig. 2C) and, therefore, achieved the highest GPP over the three dates of measurement. Our data also suggest that algal species richness influenced the ratio of production to respiration in the microcosms. Consistent across all dates of measurement, results of a GLM indicate that P:R ratios increased by 0.13 for each additional species (Fig. 2D, Table 2C). This amounts to roughly a 10% increase per species. It is worth noting that richness explained a fairly small 10% of all variation in P:R ratios (differences among species and species combinations explained an additional 45% of the 2 residual variation, ANOVA, F20,83 3.29, pB0.01, R 0.45). Nevertheless, these results suggest that algal richness had some influence on the degree of autotrophy in the microcosms, increasing the rate at which carbon was accumulating relative to the rate at which it was being released. Based on measurements of biovolumes in the mono- and 5-species polycultures, metrics of additive partitioning suggest that the diversity effect on the first day of measurements (day 13) was driven by a combination of Figure 3. (A) the net effect of algal species richness on biovolumes mechanisms. Dominance effects (DE) and trait-independent broken down into three additive effects: the dominance effect (DE), trait-dependent complementarity (TDE) and trait-indepen- complementarity (TIC) were both greater than zero, on dent complementarity (TIC). (B) the relative yield of each species average, while trait-dependent complementarity (TDC) was in the five species polyculutre measured on three different dates of less than zero. However, none of these metrics were the study. Each data point represents the mean91 SEM on n 5 significantly different from zero on day 13 (Fig. 3A). On replicate polycultures.
1708 highly productive species coming to competitive dominance deposition of nutrients that cause eutrophication (Tilman in polycultures at the expense of other taxa. Two species et al. 1996, Reich et al. 2001, Fridley 2002), or the were potentially responsible for the dominance effect. resulting changes in oxygen concentrations that stem from Scenedesmus quadricauda yielded 148% and C. vulgaris eutrophication in aquatic habitats (Dodds 2006). 90% more biovolume than would be expected based on The results of our experiment are partly consistent with their growth over the course of the experiment in mono- the results of other studies that have manipulated the culture. In contrast, S. minutum attained 43% less biovo- diversity of aquatic primary producers. For example, there lume than would be expected from its proportional change appears to be growing evidence that algal species richness is in monoculture, and both of the diatoms (A. minutissimum at least associated with, if not a direct cause of, higher algal and N. cryptocephalus) went extinct in the 5-species poly- biomass in both freshwater and marine environments cultures despite having positive growth in monoculture (Naeem et al. 2000, Fox 2004, Bruno et al. 2005, 2006, (Fig. 1). This suggests that S. quadricauda and/or C. vulgaris Weis et al. 2007, 2008, Ptacnik et al. 2008, Griffin et al. were superior competitors that reduced the densities of the 2009). Our results lend support to this conclusion. As was other species, and that they were likely responsible for the true in our study, researchers have often found that algal effects of species richness on biomass (and possibly GPP) in diversity impacts biomass production through the influence more diverse polycultures. of single-species that come to dominate the response of polycultures (Bruno et al. 2005, 2006, Weis et al. 2007, 2008). Such results contrast with findings in terrestrial plant Discussion communities where roughly two-thirds of diversity effects can be explained by different plant species using resources We have shown that the richness of five common species of in ways that are complementary in space or time (Spehn algae influences the standing stock biomass, rates of primary et al. 2005, Cardinale et al. 2007, Fargione et al. 2007). production, and the ratio of production to respiration in One of our reviewers suggested that the difference in assemblages of freshwater primary producers. We found mechanisms that tend to dominate in aquatic verses that total algal biomass increased by a factor of 1.82 for terrestrial experiments likely reflects real differences in each additional species added to the microcosms. Gross biology among the focal species Á for example, because primary production, measured as the amount of O2 algae have no roots and generally live on hard substratum, produced, increased by a factor of 1.20 with a corre- certain mechanisms by which plants diversity can impact sponding 10% increase in P:R ratios per additional species. production (i.e. below ground competition for nutrients) Higher biomass and productivity in the polycultures are not plausible. Alternatively, we suspect that the appeared to stem from co-dominance by two algal species difference in mechanisms might reflect the overly simplified that also achieved high biomass in monoculture (Scenedes- conditions that are typical of many aquatic experiments. In mus quadracada and Chlorella vulgaris), one of which these experiments, producers tend to be grown in small also had the highest rate of biomass specific production experimental units that, by comparison to natural habitats, (S. quadracada). Our results suggest that species diversity of are homogeneous in space and time (Cardinale et al. 2009). primary producers simultaneously influences both the Homogeneity can constrain opportunities for resource standing stock biomass and rate of oxygen produced by a partitioning and limit the expression of complementarity community of algae Á at least, under the simplified among species, thus negating the potential for diversity conditions of these laboratory conditions. Because of the effects (Dimitrakopoulos and Schmid 2004, Tylianakis 1:1 exchange ratio of O2 for CO2 in the process of primary et al. 2008, Cardinale et al. 2009). To the extent that production, our results also indicate that species richness this latter explanation is correct, then it is important to increases both the rate at which CO2 is removed from keep in mind that our experiment might not reflect water, and the total amount of carbon accumulated and trends in more natural environments, and it may be stored as biomass. important for future experiments to specifically incorporate Our study contributes to an important, ongoing debate the forms of heterogeneity thought to influence algal about how changes in species diversity are likely to alter the coexistence. functioning of ecosystems. Although there is now unequi- Our results also partly contrast with those of other vocal evidence that the richness of species in numerous experiments performed with aquatic primary producers. For trophic groups influences the standing stock biomass of example, Bruno et al. (2005, 2006) and Griffin et al. (2009) those groups (Balvanera et al. 2006, Cardinale et al. 2006, reported on a sequence of experiments in which they 2007, Stachowicz et al. 2007), it is much less clear how manipulated the number of species of marine macroalgae in species richness influences rates of important ecological both mesocosm and field experiments in various locations processes, such as the rate at which communities assimilate around the world (North Carolina, Jamaica and the United or release CO2,O2, or nutrients from/to the abiotic Kingdom). Although these authors frequently documented environment (Petchey 2003, Korner 2004, Srivastava effects of algal diversity on standing biomass, they found no et al. 2009, Schmid et al. 2009). Understanding how effects of diversity on rates of photosynthesis. In contrast, richness affects rates of processes (as opposed to just we found significant and somewhat strong effects of standing stocks) is essential if we are to predict whether diversity on rates of photosynthesis that could not be modern changes in biodiversity will enhance or ameliorate qualitatively distinguished from the effects of diversity on ecosystem response to other forms of environmental biomass. We think it is too early to tell, and there are too change, such as increases in atmospheric levels of CO2 few studies to say, whether our results or those of other (Niklaus et al. 2001, Reich et al. 2001), widespread authors represent the generality. Nevertheless, our work
1709 serves as a case study that suggests it is possible that loss of Hillebrand, H. et al. 1999. Biovolume calculation for pelagic and species diversity from aquatic ecosystems could reduce both benthic microalgae. Á J. Phycol. 35: 403Á424. the accumulation and storage of carbon in biomass, as well Hooper, D. U. and Dukes, J. S. 2004. Overyielding among plant functional groups in a long-term experiment. Á Ecol. Lett. 7: as the rates at which O2 (and thus, CO2) are exchanged with water and, ultimately, the atmosphere. This possibility 95Á105. deserves attention in future studies. Hooper, D. U. et al. 2005. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Á Ecol. Monogr. 75: 3Á35. Acknowledgments Á We thank Jarrett Byrnes, Ian Carroll and Huston, M. A. 1997. Hidden treatments in ecological experi- Daniel Gruner for comments that helped improve this manuscript. ments: re-evaluating the ecosystem function of biodiversity. This project was completed as part of an undergraduate honors Á Oecologia 110: 449Á460. project at the Univ. of California-Santa Barbara. Funding for this Ives, A. R. et al. 2005. A synthesis of subdisciplines: predatorÁprey project was provided, in part, by an NSF grant to BJC (DEB- interactions, and biodiversity and ecosystem functioning. 0842009). Á Ecol. Lett. 8: 102Á116. Johnson, J. B. and Omland, K. S. 2004. Model selection in ecology and evolution. Á Trends Ecol. Evol. 19: 101Á108. References Korner, C. 2004. Through enhanced tree dynamics carbon dioxide enrichment may cause tropical forests to lose carbon. Á Philos. Balvanera, P. et al. 2006. Quantifying the evidence for biodiversity Trans. R. Soc. Lond. B 359: 493Á498. effects on ecosystem functioning and services. Á Ecol. Lett. 9: Loreau, M. 1998a. Biodiversity and ecosystem functioning: a 1146Á1156. mechanistic model. Á Proc. Natl Acad. Sci. USA 95: 5632Á Bott, T. L. 1996. Primary production and community respiration. 5636. Á In: Hauer, F. R. and Lamberti, G. A. (eds), Methods in Loreau, M. 1998b. Separating sampling and other effects in stream ecology. Academic Press, pp. 533Á556. biodiversity experiments. Á Oikos 82: 600Á602. Bruno, J. F. et al. 2005. Effects of macroalgal species identity and Loreau, M. 2000. Biodiversity and ecosystem functioning: recent richness on primary production in benthic marine commu- theoretical advances. Á Oikos 91: 3Á17. nities. Á Ecol. Lett. 8: 1165Á1174. Loreau, M. and Hector, A. 2001. Partitioning selection and Bruno, J. F. et al. 2006. Partitioning the effects of algal species complementarity in biodiversity experiments. Á Nature 412: identity and richness on benthic marine primary production. 72Á76. Á Oikos 115: 170Á178. Naeem, S. et al. 2000. ProducerÁdecomposer co-dependency Cardinale, B. J. et al. 2004. Effects of species diversity on the influences biodiversity effects. Á Nature 403: 762Á764. primary productivity of ecosystems: extending our spatial and Niklaus, P. A. et al. 2001. A long-term field study on biodiversity x temporal scales of inference. Á Oikos 104: 437Á450. elevated CO2 interactions in grassland. Á Ecol. Monogr. 71: Cardinale, B. J. et al. 2006. Effects of biodiversity on the 341Á356. functioning of trophic groups and ecosystems. Á Nature 443: Petchey, O. L. 2003. Integrating methods that investigate how 989Á992. complementarity influences ecosystem functioning. Á Oikos Cardinale, B. J. et al. 2007. Impacts of plant diversity on biomass 101: 323Á330. production increase through time due to complementary Ptacnik, R. et al. 2008. Diversity predicts stability and resource use resource use: a meta-analysis. Á Proc. Natl Acad. Sci. 104: efficiency in natural phytoplankton communities. Á Proc. Natl 18123Á18128. Acad. Sci. USA 105: 5134Á5138. Cardinale, B. J. et al. 2009. Towards a food-web perspective on Reich, P. B. et al. 2001. Plant diversity enhances ecosystem biodiversity and ecosystem functioning. Á In: Naeem, S. responses to elevated CO2 and nitrogen deposition. Á Nature et al. (eds), Biodiversity, ecosystem functioning and human 410: 809Á812. wellbeing. Oxford Univ. Press. Schmid, B. et al. 2009. Consequences of species loss for ecosystem Dimitrakopoulos, P. G. and Schmid, B. 2004. Biodiversity effects functioning: a meta-analysis of data from biodiversity experi- Á Á increase linearly with biotope space. Ecol. Lett. 7: 574 583. ments. Á In: Naeem, S. et al. (eds), Biodiversity, ecosystem Dodds, W. K. 2006. Eutrophication and trophic state in rivers and functioning and human wellbeing. Oxford Univ. Press. streams. Á Limnol. Oceanogr. 51: 671Á680. Schulze, E. D. 1996. Relations between biodiversity and ecosytem Fargione, J. et al. 2007. From selection to complementarity: shifts fluxes of water vapour and carbon dioxide. Á In: di Castri, F. in the causes of biodiversity-productivity relationships in a and Younes, F. (eds), Biodiversity, science and development: long-term biodiversity experiment. Á Proc. R. Soc. Lond. B Á 274: 871Á876. towards a new partnership. CAB Int., pp. 162 164. Fox, J. W. 2004. Effects of algal and herbivore diversity on the Spehn, E. M. et al. 2005. Ecosystem effects of biodiversity partitioning of biomass within and among trophic levels. manipulations in European grasslands. Á Ecol. Monogr. 75: Á Ecology 85: 549Á559. 37Á63. Fox, J. W. 2005. Interpreting the ‘selection effect’ of biodiversity Srivastava, D. S. et al. 2009. Diversity has stronger topÁdown than on ecosystem function. Á Ecol. Lett. 8: 846Á856. bottomÁup effects on decomposition. Á Ecology 90: 1073Á Fridley, J. D. 2002. Resource availability dominates and alters the 1083. relationship between species diversity and ecosystem produc- Stachowicz, J. et al. 2007. Understanding the effects of marine tivity in experimental plant communities. Á Oecologia 132: biodiversity on communities and ecosystems. Á Annu. Rev. 271Á277. Ecol. Evol. Syst. 38: 739Á766. Graham, L. E. and Wilcox, L. W. 2000. Algae. Á Prentice Hall. Steinman, A. D. and Lamberti, G. A. 1996. Biomass and pigments Griffin, J. N. et al. 2009. Functional diversity predicts overyielding of benthic algae. Á In: Hauer, F. R. and Lamberti, G. A. (eds), effect of species combination on primary productivity. Á Oikos Methods in stream ecology. Academic Press, pp. 295Á311. 118: 37Á44. Stocker, R. et al. 1999. A field study of the effects of elevated CO2 Hector, A. et al. 2002. Overyielding in grassland communities: and plant species diversity on ecosystem-level gas exchange testing the sampling effect hypothesis with replicated biodi- in a planted calcareous grassland. Á Global Change Biol. 5: versity experiments. Á Ecol. Lett. 5: 502Á511. 95Á105.
1710 Tilman, D. et al. 1996. Productivity and sustainability influenced Weis, J. J. et al. 2007. Effects of species diversity on community by biodiversity in grassland ecosystems. Á Nature 379: 718Á biomass production change over the course of succession. 720. Á Ecology 88: 929Á939. Tilman, D. et al. 1997. Plant diversity and ecosystem productivity: Weis, J. J. et al. 2008. Effects of algal species richness on the theoretical considerations. Á Proc. Natl Acad. Sci. USA 94: production of community biomass in homogeneous verses 1857Á1861. heterogeneous environments. Á PLoS One 3: e2825. Tylianakis, J. M. et al. 2008. Resource heterogeneity moderates Wetzel, R. G. 1983. Limnology. Á Saunders College Publishing. the biodiversityÁfunction relationship in real world ecosys- Wetzel, R. G. and Likens, G. E. 2000. Limnological analyses. tems. Á PLoS Biol. 6: 947Á956. Á Springer.
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