Measuring genetic diversity in ecological studies

Transcription

1 Plant Ecol (2012) 213: DOI /s Measuring genetic diversity in ecological studies Meghan L. Avolio Jeremy M. Beaulieu Eugenia Y. Y. Lo Melinda D. Smith Received: 15 February 2012 / Accepted: 28 May 2012 / Published online: 15 June 2012 Ó Springer Science+Business Media B.V Abstract There is an increasing interest in how genetic diversity may correlate with and influence community and ecosystem properties. Genetic diversity can be defined in multiple ways, and currently lacking in ecology is a consensus on how to measure genetic diversity. Here, we examine two broad classes of genetic diversity: genotype-based and genomebased measures. Genotype-based measures, such as genotypic richness, are more commonly used in ecological studies, and often it is assumed that as genotypic diversity increases, genomic diversity (the number of genetic polymorphisms and/or genomic dissimilarity among individuals) also increases. However, this assumption is rarely assessed. We tested this assumption by investigating correlations between genotype- and genome-based measures of diversity using two plant population genetic datasets: one observational with data collected at Konza Prairie, KS, and the other based on simulated populations with five levels of genotypic richness, a typical design of genetic diversity experiments. We found conflicting results for both datasets; we found a mismatch Electronic supplementary material The online version of this article (doi: /s ) contains supplementary material, which is available to authorized users. M. L. Avolio (&) J. M. Beaulieu E. Y. Y. Lo M. D. Smith Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect St., New Haven, CT 06511, USA meghan.avolio@yale.edu between genotypic and genomic diversity measures for the field data, but not the simulated data. Last, we tested the consequences of this mismatch and found that correlations between genetic diversity and community/ecosystem properties depended on metric used. Ultimately, we argue that genome-based measures should be included in future studies alongside genotypic-based measures because they capture a greater spectrum of genetic differences among individuals. Keywords Community diversity Ecosystem function Genome diversity Phenotype SGDC Introduction The study of genetic diversity within communities has re-emerged as an area of interest in ecology (Weltzin et al. 2003; Vellend and Geber 2005; Hughes et al. 2008; Bailey et al. 2009). Genetic diversity is thought to be ecologically important for a number of reasons, including (1) genetic diversity within a single species may correlate with species diversity both within (Vellend 2005; Vellend and Geber 2005; Vellend 2006; Vellend 2008) and between trophic levels (Wimp et al. 2004; Bangert et al. 2006; Crutsinger et al. 2006; Johnson et al. 2006), (2) in communities dominated by a single species, genetic variation within that species may be as (or more) important as variation between species (Hughes and Stachowicz 2004), and

2 1106 Plant Ecol (2012) 213: (3) genetic diversity may serve as a proxy for a complex multivariate phenotypic space that is often difficult to measure (see review in Hughes et al. 2008). Indeed, for a range of plant species, experimental manipulations of the number of genotypes have been shown to affect population, community, and ecosystem properties (Bailey et al. 2009). The potential importance of genetic diversity within communities can be linked to the theory of the importance of species diversity, whereby a greater number of species is thought to enhance ecosystem functioning. Two mechanisms niche complementarity and selection effect are invoked to explain the positive relationship between diversity and measures of ecosystem function (e.g., productivity; (Huston 1997; Tilman et al. 1997; Loreau and Hector 2001). Species rich communities are predicted to have a larger range of phenotypes and correspondingly occupy greater niche space than species poor communities resulting from niche complementarity (Tilman et al. 1997). Conversely, the selection effect is an artifact of the higher species richness, where more diverse plots have a greater probability of containing a species whose phenotype is driving the relationship between species richness and ecosystem function (Huston 1997; Tilman et al. 1997). Correspondingly, within a population, a species with greater genetic diversity presumably contains genotypes that capture a greater range of trait space when compared with populations with lower genetic diversity (Hughes et al. 2008). As a consequence, communities containing species with high genetic diversity, particularly if they are the dominant species, are expected to have increased ecosystem functioning than those with lower genetic diversity (Crutsinger et al. 2006). As this field of study continues to grow, it is important to consider how ecologists define genetic diversity, as it is a complex term with a range of definitions depending on the discipline and the measurement employed. For this paper, we follow Hughes et al. (2008) and define genetic diversity as any measure that quantifies the magnitude of genetic variability within a population. Genetic variability has two major components: (1) genotype-based, meaning the number of distinct clones or genotypes, and (2) genome-based, meaning the amount of genetic polymorphisms and/or genomic (dis)similarity among individuals within a population (Reusch et al. 2005; Arnaud-Haond et al. 2010). An example of how these two measures of genetic diversity differ is found in clonal lineages where evolutionary processes such as somatic mutation, facultative sexual recombination, gene flow, and drift create novel genetic material (Ellstrand and Roose 1987; Widen et al. 1994; McLellan et al. 1997; Gornall 1999; Reusch and Bostrom 2011) causing individuals within a clone to differ genetically. In this case, genotypic richness does not necessarily capture the full range of genetic variation within a population. Recent genetic diversity studies have primarily used clonal plants as their study system, where the number of genotypes genotypic richness is the employed measure of genetic diversity (Hughes et al. 2008). These studies often use the terms genotypic richness and genetic diversity interchangeably (Weltzin et al. 2003; Schweitzer et al. 2005; Vellend 2005; Jump et al. 2006; Vellend 2006; Crutsinger et al. 2008; Ehlers et al. 2008; Munzbergova et al. 2009; Vellend et al. 2010; Kotowska et al. 2010), and generally do not consider the other component of genetic diversity genome dissimilarity. The underlying and rarely acknowledged assumption of such terminology is that as the number of genotypes increases, there is a corresponding increase in genome diversity, and thus the two measures are positively correlated (Hughes and Stachowicz 2004; Reusch et al. 2005; Arnaud-Haond et al. 2010). By this logic, a population with low genotypic richness (e.g., three genotypes) is presumed to have low genomic diversity, and vice versa (Fig. 1, bold arrow). However, without knowledge of the relationships among the co-occurring genotypes, there exists the equally plausible alternative of high genomic dissimilarity, despite low genotype richness (Fig. 1; dashed arrow). Ultimately genetic diversity is a proxy for phenotypic diversity, thus understanding how genotype- and genome-based measures of genetic diversity are correlated is important because of the predictions for how they relate to phenotypic diversity (Fig. 1). Studies have found high genome diversity correlates with high phenotype diversity (Leinonen et al. 2007; Jousset et al. 2011), however, it is less clear how genotype-based measures of diversity correspond to trait diversity (although see (Johnson et al. 2006). The differences between genotype- and genomebased measures of genetic diversity can be likened to the differences between species richness and phylogenetic measures of community diversity (Faith 1992; Webb et al. 2002). For example, phylogenetic

3 Plant Ecol (2012) 213: Fig. 1 Example of the potential mismatch between genotypeand genome-based measures of genetic diversity. A plot with a genotypic richness of three is easily thought to have low genomic diversity, as shown on the left (solid arrow). However, the genotypes could be more distantly related to one another and, therefore, actually represent high genomic diversity, as shown on the right (dashed arrow). Further, genotypes that are closely related to one-another may have more similar phenotypes, while genotypes that are more distantly related to one another might occupy a greater amount of trait space diversity has been shown to be a better predictor of community biomass than species richness (Cadotte et al. 2008; Cadotte et al. 2009). The thought is that phylogenetic history or shared ancestry may be a more dynamic way of describing ecological differentiation than number of species alone. Here, time separating any two species is proportional to the amount of evolutionary and genetic divergence, and presumably trait differentiation, between species (Felsenstein 1985). The same could be said at the intraspecific level, assuming that the number of genomic differences separating two lineages is proportional to the number of trait differences between lineages. There is a clear need to test the assumption that genotype- and genome-based measures of genetic diversity behave similarly, and determine whether these two measures of genetic diversity correlate similarly with community diversity and ecosystem function. Our first goal was to test for a potential mismatch between genotype- and genome-based measures of genetic diversity. We did this using an observational dataset based on AFLP data collected from naturally co-occurring individuals of a C 4 grass species, Andropogon gerardii, in a field study. Our second goal was to address whether the same potential for a mismatch exists in an experimental manipulation of genotypes, as is typically performed in experiments (see Bailey et al. 2009). To do this, we used a randomly generated AFLP dataset to simulate experimental populations with different levels of genotypic richness. We recognize that there are many ways besides the AFLP technique with which molecular data can be generated; however, assessing how different molecular markers may affect behavior of the different measures of genetic diversity is beyond the scope of this paper. We predict that there will not be a strong correlation between genotype- and genome-measures of genetic diversity for both the observational and experimental datasets. Last, using our field dataset, we investigate whether there are consequences of using only genotype- or genomebased measures of diversity when assessing correlations between genetic diversity and plant community and ecosystem properties. Methods and materials Observational data Study species We used A. gerardii as our study species to address these questions. We chose A. gerardii because of its ecological importance; A. gerardii is a widespread dominant tallgrass species that affects community and ecosystem processes (Smith and Knapp 2003). In addition, previous work investigating the genetic structure of A. gerardii at the plant neighborhood scale (the scale at which individuals compete for resources) found that there was considerable diversity, with the potential for the genetic diversity of this species to affect community and ecosystem processes (Avolio et al. 2011; Chang and Smith 2012). A. gerardii is a long-lived (Lauenroth and Adler 2008) self-incompatible polyploid (n = 6x, 7x, 8x, or 9x; Keeler 2004), and the degree of ploidy can vary within

4 1108 Plant Ecol (2012) 213: a population (Keeler 2004). A. gerardii predominantly reproduces vegetatively through belowground buds (Benson and Hartnett 2006). As these clones grow they sever their root connections (Weaver 1954; Benson and Hartnett 2006) but remain genetically identical. Both ploidy level and reproductive system could influence the genetic architecture of individuals and thus the results of the genetic markers. For instance, polyploid individuals may reveal higher heterozygosity than diploids because of genome multiplications and/or higher mutation rates of excess genome copies (Song et al. 1995; Dubcovsky and Dvorak 2007). In contrast, asexual species may reveal higher homozygosity than a sexual species because of the lack of recombination. In both cases, populations may naturally deviate from the Hardy Weinberg equilibrium and the usual advantage of using co-dominant over dominant markers does not necessarily hold. Given A. gerardii is a polyploid species and predominantly reproduces asexually, AFLPs are a suitable marker to assess genetic variation (Meudt and Clarke 2007). Data collection The observational AFLP dataset was based on a total of 480 individuals of A. gerardii sampled in 2007 from m plots (n = 40 per plot). The plots were located in intact, annually burned tallgrass prairie at the Konza Prairie Biological Station, which is located in northeastern Kansas. For the genotype-based measures of genetic diversity, we used the clonal distance model implemented in GenoDive V.20b17 (Meirmans and Van Tienderen 2004) to assign individuals to genotypes following the methods of (Avolio et al. 2011) where 103 polymorphic AFLP loci were generated using six primer pairs. The clonal distance model assumes clonal reproduction and calculates distances based on the number of mutations taken to transform an individual of one genotype into another genotype. Based on this analysis, we then calculated the different measures of genetic diversity for each plot (see below). See Avolio et al. (in revision), for more details. We also determined species richness and productivity of each plot for which genetic diversity was measured. Percent aerial cover (nearest 1 %) was estimated for each species separately within four 1-m 2 quadrats per plot in June and August Maximum cover estimates for each species were averaged and used to calculate plant community richness, evenness, and Shannon s diversity. Aboveground biomass was clipped at ground level from four 0.1 m 2 subplots randomly located within each m plot in late August 2008 (after the species composition measurements), dried at 60 C for [48 h, and then weighed. Because the site is burned each spring, this provides a direct measure of aboveground net primary production (ANPP). Simulated data For the simulated dataset, we created a binary matrix that resembles an AFLP presence and absence dataset. This matrix consisted of 500 genotypes (rows) and 125 loci (columns) roughly the same number of loci in our observational dataset, where each locus was randomly assigned as present or absent for each genotype based on a bimodal distribution. We randomly chose 50 out of the 500 genotypes as our source pool of genotypes to create the simulated populations; these 50 genotypes drawn from a larger sample had more pronounced differences in genetic structure than a direct simulation of 50 genotypes. We used our source pool of genotypes to calculate Dice dissimilarity pair-wise distances and investigate how these genotypes were related to one another. We assessed the genetic relatedness of the 50 genotypes using non-metric multidimensional scaling using the vegan package (Oksanen et al. 2012) in R (R Core Development Team). Once the genetic structure of these 50 genotypes were known, we created two subset pools of 16 genotypes, A and B, from the source pool, where genotypes were either (1) more closely related, with less genetic structure among genotypes (A), or (2) randomly chosen, with a greater genetic structure among genotypes (B; Panel 1, Fig. 2). We used these two subset pools of genotypes to see if the genetic structure or relatedness among genotypes would affect the correlation between genotypic richness and genomic diversity. For both pools A and B, we created replicate populations with five genotypic richness levels, 1, 2, 4, 8, and 16 genotypes, where replicates of each level had 16 individuals. Three replicates were simulated for each level except for the 1 and 16 genotype treatments, as the measures of genetic diversity were not repeatable. From the 16-genotype pools, genotypes were randomly assigned to each

5 Plant Ecol (2012) 213: Fig. 2 Results from the simulated dataset. Genomic relatedness between all 50 simulated genotypes (represented by a circle) in non-metric multidimensional scaling space. The 16 genotypes (open circles) used to create populations with different levels of genetic diversity were more similar to one another and had less genetic variability as indicated by clustering of genotypes (open circles) for Simulation A than Simulation B (see text for details). Black circles refer to the remaining pool of genotypes from which the 16 genotypes were drawn. For both simulations, A and B, the genome-based measures of genetic diversity (allelic richness) correlated significantly with genotype-based measure of genetic diversity (right graphs). Similar relationships were found for percent polymorphic loci (data not shown) replicate. For each simulated replicate, we calculated the different genotype- and genome-based measures of genetic diversity (see below). Measures of genetic diversity Genotype-based measures of genetic diversity partition individuals into discrete groups based on genomic similarity among individuals. In contrast, genomebased measures describe the range of genomic variation among individuals without assigning them to user-defined genotype identities. In this regard, the genome-based measures are continuous descriptors of genetic variation. In ecological studies, generally two genotype-based measures of diversity are employed: genotypic richness and genotypic diversity (Arnaud- Haond et al. 2007). Genotypic richness is a measure of the number of genotypes, whereas genotypic diversity is calculated by drawing upon diversity indices used in community ecology taking into account both the richness and evenness of genotypes. In contrast, many methods are employed to calculate differences between genomes of individuals in ecological studies. These methods often fall into two broad categories: allelic-based and distance-based. At the allelic level, genetic diversity measures the proportion of unique alleles per locus among individuals within a population (or a plot). These measures

6 1110 Plant Ecol (2012) 213: included allelic richness, expected heterozygosity (gene diversity; Nei 1978), and percentage of polymorphic loci. Distance-based methods are used to measure genomic similarity or dissimilarity between individuals for binary data (Rogers 1972; Legendre and Legendre 1998; Kosman and Leonard 2005). This is analogous to phylogenetic methods developed for community ecology (e.g., (Faith 1992; Webb 2000), which considers the average time separating all cooccurring species within a given community, to reflect genomic differences among species. It remains unclear how best to analyze dominant or co-dominant marker data for polyploids (Kosman and Leonard 2005). The potential disomic, tetrasomic, or higher levels of inheritance of co-dominant loci in polyploids makes the allele dosage difficult to determine and thus complicates the analysis of genetic variation among individuals. Hence, converting bands at co-dominant loci to dominant ones (i.e., the presence or absence of a band) is one way to measure variation among polyploid individuals, largely comparable to AFLP data. Here, we followed the convention of converting the AFLP data into a binary presence/absence matrix of independent loci (Yang et al. 2006; Kloda et al. 2008; Gonzalez-Perez et al. 2009). For the genome-based measures of genetic diversity, we calculated allelic richness using POPGENE V1.32 (Yeh and Boyle 1999) and percent polymorphic loci using AFLP-surv (Vekemans 2002). Because the biology of A. gerardii violated some of the assumptions used to calculate expected heterozygosity, we did not include it in our analyses. We used a mean pairwise distance (MPD) metric as our distance-based measure of genetic diversity. MPD is the average dice dissimilarity observed between all pair-wise combinations of co-occurring individuals found within a given treatment (i.e., plot, geographic region, etc.). This is a useful metric because it describes the difference between all co-occurring individuals relative to the overall pool of genotypes from which each individual could have been drawn from. For the simulated data we did not use MPD to calculate diversity because for plots containing only two individuals, MPD is based off only one comparison. We used R (R Core Development Team) to calculate pair-wise Dice dissimilarities using the package vegan (Oksanen et al. 2012) and to calculate MPD using picante (Kembel et al. 2009). Statistical analyses Pair-wise Pearson s correlations of the different genotype- and genome-based diversity measures were performed separately for each dataset using SAS (V.9.1.3, Cary, N.C.). Significance was set at p B Results Using the observational dataset, we found that all of the genome-based measures of genetic diversity (allelic richness, % polymorphic loci, and MPD) were positively correlated with one another and the genotype-based measures (genotypic richness and genotypic diversity) were positively correlated with one another (Fig. 3; Supplementary Material Table A1). However, only MPD correlated with genotypic richness and genotypic diversity. This demonstrates that a mismatch does exist between genotypic richness, the measure of genetic diversity most commonly used in ecological studies, and some of the commonly used genome-based measures of diversity in population genetic studies. Using the simulated dataset that experimentally manipulated genotypic richness from 1 to 16 genotypes we found a correlation between genotypic richness and genome-based measures of genetic diversity. For both simulations where individuals were either closely related to one another (A) or randomly chosen (B), allelic richness correlated with genotypic richness (r = 0.89, p = 0.01; r = 0.81, p \ 0.01 respectively; Fig. 2). However, for both simulations, up to 85 % of the maximum allelic richness was reached with four genotypes, and increasing the number of genotypes to 8 and 16 resulted in only a slight increase in allelic diversity. Similar patterns were observed for % polymorphic loci (data not shown). While there is an increase in genome diversity from 1 to 2 or more genotypes, the effect of more genotypes on allelic richness quickly saturates after two genotypes. To address the implications of this mismatch between genotypic richness and genome-based measure of genetic diversity, we investigated how both genotypic richness and MPD correlated with community (species diversity) and ecosystem (aboveground net primary productivity; ANPP) properties with our

7 Plant Ecol (2012) 213: Fig. 3 Results from the AFLP dataset for the observational field study of the C 4 grass, Andropogon gerardii. Correlations between genotype-based (genotypic richness and diversity) and genomebased (allelic richness, percent polymorphic loci and MPD, or mean pair-wise dissimilarity) measures of genetic diversity are shown. Plots with black data points indicate significance at p B 0.05, while those with gray data points do not show a significant correlation field AFLP dataset. We found that genotypic richness did not correlate with either plant community diversity or ANPP, whereas MPD (Fig. 4) allelic richness, and % polymorphic loci were correlated with ANPP but not community diversity. Discussion The relationship between genetic diversity and ecological properties such as community diversity and productivity is an area of increasing interest to Fig. 4 Correlations between MPD or genotypic richness and species diversity or aboveground productivity (ANPP, see text for details). Plots with regression lines indicate significance at p B Each plot is numbered to enable comparisons between the genotypic richness of a plot and its genomic diversity, as measured by allelic richness. Data modified from Avolio et al. (In Revision)

8 1112 Plant Ecol (2012) 213: ecologists, where ultimately, genetic diversity is a proxy for phenotypic diversity. We have shown that there exists either a mismatch or a quickly saturating relationship between genotype- and genome-based measures of genetic diversity employed by ecologists, for both an observational and manipulated experiment (Figs. 2 and 3). Understanding the relationship between genotype- and genome-based measures is important because genotype-based measures of genetic diversity do not necessarily relate information on phenotypic variation within a population (Fig. 1). In contrast, allelic- and distance-based measures of genetic diversity, which capture genomic variation among individuals, have the potential to be more representative of phenotypic differences compared to genotype-based measures. A survey of 36 ecological studies on the effects of genetic diversity found that the majority of studies (67 %) use genotype-based measures of genetic diversity (Supplementary Material Table A2). Although 22 % of the studies did not use molecular methods to determine genotype, the majority of these studies had the ability to calculate genome-based measures of diversity in addition to genotype-based measures, but chose not to. Thus, there is a great potential in this field to apply genome-based measures of genetic diversity in addition to genotypebased measures for comparison. Such studies would then provide deeper insight into the relationships between different measures of genetic diversity and phenotypic trait diversity in the population. Linking genetic diversity to phenotypic diversity remains difficult, and thus genetic diversity is generally used as a proxy, particularly in ecological studies. With genotype-based measures, if each genotype represents a novel phenotype, then it is presumed that a population with more genotypes occupies greater phenotypic trait space than a population with fewer phenotypes. However, without any knowledge of the genomic relatedness between any two genotypes, it is unclear how number of genotypes relates to the trait space they occupy (Fig. 1). This problem is often overcome in experimental manipulations by drawing genotypes from different populations and then growing them together, assuming that geographically distant genotypes are more likely to be phenotypically and genetically distinct (e.g. (Crutsinger et al. 2006; Johnson et al. 2006). However, the range of phenotypic and genotypic traits in these artificially composed populations may be much greater than the range that occurs naturally within a single population. It remains to be seen how results of these experiments relate to natural plant communities; the effects of genotypic diversity in these experiments may be greater than the effects of genotypic diversity that would be observed naturally. In studies that focus on genotypes within a population and capture a realistic range of phenotypic trait diversity, it is important to have some understanding of genome relatedness among genotypes to make predictions about phenotypic similarity. Genome-based measures of genetic diversity also assume that when there is greater genome dissimilarity among individuals, there are more phenotypes. With allelic-based measures, higher values are found in populations containing a greater number of alleles for existing loci. Thus, there is the possibility of a greater number of allelic combinations through frequent gene mixing and/or faster gene mutation, and consequently a greater number of unique phenotypes. For distance-based measures, having greater genome dissimilarity between individuals demonstrates that the individuals in the population have more distinct genomes, and presumably exhibit more distinct phenotypes (Fig. 1). While some studies have shown correlations between genome-based measures of diversity and phenotypic diversity (Merila and Crnokrak 2001; Reed and Frankham 2001; Cmokrak and Merila 2002; Leinonen et al. 2007; Jousset et al. 2011), additional studies are needed to better understand the relationships between measures of genotypic and genomic diversity. Jousset et al. (2011) postulated that genomic dissimilarity among individuals better predicted complimentary uptake of resources than did genotypic richness because it correlated well with phenotypic dissimilarity. Interestingly, they found that genomic dissimilarity was better at predicting ecosystem functioning in complex environments than in homogenous environments, because in homogenous environments niche complementarity among genotypes was not as important. These different aspects of genetic diversity have the potential to lead to contradicting correlations when assessing relationships between genetic diversity and community and ecosystem properties. In the survey of ecological studies on genetic diversity, there was no clear pattern in how different measures of intraspecific genetic diversity correlated with community and ecosystem properties (Supplementary

9 Plant Ecol (2012) 213: Material Table A2). While the range of study organisms and systems used may help explain the discrepancy between studies (Vellend and Geber 2005, Hughes et al. 2008), our results suggest that the metric used to calculate genetic diversity could equally influence whether or not a pattern was observed. The variety of ways genetic diversity is measured also affects the ability to compare results across studies. For instance, although there is a correlation between genotype-and genome-based measures in manipulative experiments, in studies that only manipulate genotype richness the range of genomic variation is unknown. Thus, comparing the results of manipulative experiments to field studies in a biologically meaningful way is not feasible because the measures of genetic diversity are so different. It is impossible to compare a genotype richness of 12 in one study to an allelic richness of 1.85 in another. As this field develops it will be important for studies to use similar measures of genetic diversity, in order for the results of the many studies to be more comparable. Using data from the observational study, we found that the metric used to assess genetic diversity affected whether community and ecosystem properties correlated with genetic diversity. Our data was collected from plots located in an intact prairie population where differences among genotypes are probably small, thus genotypic richness may not accurately reflect phenotypic differences. Greater MPD, on the other hand, suggests that there could be more gene mixing and/or mutational changes among individuals in the population. These processes may generate novel or unique alleles within the local gene pool and potentially contribute to greater phenotypic diversity within the population. However, such information could be hidden or mistaken when categorizing genetic variation into genotypes. For example, plot 12 in Fig. 4 has low genotypic richness but high MPD, possibly explaining why using different measures of genetic diversity can give contradicting results. This example demonstrates that the correlations found between genetic diversity and community and ecosystem properties may be highly dependent on the measure of genetic diversity used. Arnaud-Haond et al. (2010) found conflicting relationships of allelic richness and genotypic richness with the stability of seagrass communities. Allelic richness was negatively correlated with shoot mortality, whereas genotypic richness was positively correlated with net population growth. Similarly, Hughes and Stachowicz (2009) found that genotypic richness and genotypic diversity but not expected heterozygosity correlated with seagrass shoot density; and heterozygosity but not genotypic richness and diversity correlated with epiphyte biomass. In another example, Jousset et al. (2011) found that in constructed bacterial populations, genotypic richness and genomic dissimilarly had opposing effects of ecosystem functioning. Thus, had we, or any of these studies, focused solely on one genotypeor genome-based measure of genetic diversity, the results and conclusions of the studies would have been different and arguably incomplete. Collectively, our results emphasize the need to incorporate both measures of genetic diversity in future studies assessing relationships between intraspecific diversity and ecological parameters, such as community diversity and productivity. Future experiments manipulating genomic dissimilarity among individuals have the ability to create a more nuanced range of diversity values than the discrete genotypic richness design typically used in biodiversity-ecosystem function experiments, and may consequently be better able to test for complementarity effects. Ultimately, we argue that future experiments need to consider manipulating both aspects of diversity to have a more complete understanding of the importance of genetic diversity for ecological processes. Acknowledgments This research was supported by the USDA CSREES Ecosystem Studies Program, and the US Department of Energy s Program for Ecosystem Research (#DE-FG02-04ER63892) to MDS. We thank Erem Kazancıoğlu for his assistance with the simulations. We also want to thank M. Vellend, J. Oliver, D. Post, C. Chang, J. Weis, T. Hanley, two anonymous reviewers and members of D. Post s and M. Smith s laboratories for their insightful comments and discussion of the manuscript. References Arnaud-Haond S, Duarte CM, Alberto F, Serrao EA (2007) Standardizing methods to address clonality in population studies. Mol Ecol 16: Arnaud-Haond S, Marba N, Diaz-Almela E, Serrao EA, Duarte CM (2010) Comparative analysis of stability-genetic diversity in seagrass (Posidonia oceanica) meadows yields unexpected results. Estuar Coasts 33: Avolio ML, Chang CC, Smith MD (2011) Assessing fine-scale genotypic structure of a dominant species in native grasslands. Am Midl Nat 165:

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