Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis

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1 Journal of Ecology 2012, 100, doi: 1111/j x Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis Yu Zhang 1, Han Y. H. Chen 1 * and Peter B. Reich 2,3 1 Faculty of Natural Resources Management, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada; 2 Department of Forest Resources, University of Minnesota, 115 Green Hall, 1530 Cleveland Ave. N., St. Paul, MN , USA; and 3 Hawkesbury Institute for the Environment, University of Western Sydney, Locked Bag 1797, Penrith NSW 2751, Australia Summary 1. Although there is ample support for positive species richness productivity relationships in planted grassland experiments, a recent 48-site study found no diversity productivity relationship (DPR) in herbaceous communities. Thus, debate persists about diversity effects in natural versus planted systems. Additionally, current knowledge is weak regarding the influence of evenness on the DPRs, how DPRs are affected by the variation in life-history traits among constituent species in polycultures and how DPRs differ among biomes. The impacts of these factors on DPRs in forest ecosystems are even more poorly understood. 2. We performed a meta-analysis of 54 studies to reconcile DPRs in forest ecosystems. We quantified the net diversity effect as log effect size [ln(es)], the log ratio of the productivity in polycultures to the average of those in monocultures within the same type of mixture, site condition and stand age of each study. The first use of a boosted regression tree model in meta-analysis, a useful method to partition the effects of multiple predictors rather than relying on vote-counting of individual studies, unveiled the relative influences of individual predictors. 3. Global average ln(es) was 128, indicating 23.7% higher productivity in polycultures than monocultures. The final model explained 21% of the variation in ln(es). The predictors that substantially accounted for the explained variation included evenness (34%), heterogeneity of shade tolerance (29%), richness (13%) and stand age (15%). In contrast, heterogeneity of nitrogen fixation and growth habits, biome and stand origin (naturally established versus planted) contributed negligibly (each 4%). Log effect size strongly increased with evenness from 0.6 to 1 and with richness from 2 to 6. Furthermore, it was higher with heterogeneity of shade tolerance and generally increased with stand age. 4. Synthesis. Our analysis is, to our knowledge, the first to demonstrate the critical role of species evenness, richness and the importance of contrasting traits in defining net diversity effects in forest polycultures. While testing the specific mechanisms is beyond the scope of our analysis, our results should motivate future studies to link richness, evenness, contrasting traits and life-history stage to the mechanisms that are expected to produce positive net biodiversity effects such as niche differentiation, facilitation and reduced Janzen Connell effects. Key-words: biomes, boosted regression trees, net diversity effect, plant development and life-history traits, productivity, species evenness, stand origins Introduction The diversity productivity relationship (DPR) has received considerable attention during the past two decades, largely because of the continuous loss of biodiversity (Hooper et al. 2005). Numerous empirical experiments, mostly in temperate *Correspondence author. hchen1@lakeheadu.ca grasslands, have demonstrated positive DPRs, also defined as net biodiversity effect, i.e. polycultures have higher biomass production than the average production of monocultures (overyielding) (e.g. Tilman, Wedin & Knops 1996; Loreau & Hector 2001; Cardinale et al. 2007; Isbell, Polley & Wilsey 2009). The niche complementarity hypothesis, i.e. positive diversity effect is due to increased resource use and nutrient retention via niche differentiation or partitioning and Ó 2012 The Authors. Journal of Ecology Ó 2012 British Ecological Society

2 Diversity and productivity relationships 743 interspecific facilitation (Tilman 1999; Loreau et al. 2001; Hooper et al. 2005), has been the cornerstone of DPR studies. However, it is rare for DPR studies to directly demonstrate the link between the net biodiversity effect in polycultures and ecological mechanisms (Cardinale 2011). Additionally, there is a lack of evidence for a positive DPR in naturally assembled herbaceous communities (Adler et al. 2011). Research of DPRs is lagging behind in forest ecosystems due to the longevity and size of trees and the complexity of forest ecosystems (Leuschner, Jungkunst & Fleck 2009). Previous empirical DPR studies in forest ecosystems have reported positive (e.g. MacPherson, Lieffers & Blenis 2001; Garber & Maguire 2004; Amoroso & Turnblom 2006; Pretzsch & Schutze 2009; Brassard et al. 2011), insignificant or even negative (e.g. Edgar & Burk 2001; Chen & Klinka 2003; Vila et al. 2003; Cavard et al. 2010) effects of species diversity on productivity. Possible causes of the observed contrasting DPRs may include: choice of diversity indices, failing to consider variation in life-history traits such as contrasting shade tolerance, growth rate and nitrogen (N) fixation among constituent species in polycultures, stand origins (experimental plantations versus naturally established stands) and variation in stand age. However, little research has been conducted to determine the potential influences of these factors on DPRs. The measure of species diversity in DPR studies is still debated (Hillebrand & Matthiessen 2009; Hillebrand & Cardinale 2010). Most DPR studies have chosen species richness as the measure of species diversity to define and interpret DPRs. However, richness alone cannot fully represent species diversity (Bock, Jones & Bock 2007) in relation to ecosystem functioning because it ignores the influence of species evenness (relative abundance) on interspecific interactions (Kirwan et al. 2007; Hillebrand, Bennett & Cadotte 2008; Turnbull & Hector 2010). The lack of understanding of species evenness in DPRs is presumably limited by traditional experimental and statistical methods (Isbell, Polley & Wilsey 2009). We hypothesize that both richness and evenness influence the diversity benefits on productivity. The trait-based approach has elicited much recent interest for predicting changes in community composition and ecosystem functioning in response to the presence of competitors along environmental niche axes (Hillebrand & Matthiessen 2009) because of the potential causal link between species traits to niche occupancy and partitioning (Silvertown 2004). For example, polycultures with different life-history traits among constituent species can increase the spatial niche occupancy of asite(coomeset al. 2009; Brassard et al. 2011). Thus, we hypothesize that variation in the DPRs (i.e. positive, neutral or negative DPRs) in forest ecosystems is attributable in large part to the presence or absence of life-history variation such as contrasting shade tolerance, growth rate and nitrogen (N) fixation among constituent species in polycultures. Stand origin may also be an important factor influencing DPRs in forest ecosystems because experiments under controlled homogeneous environments, while allowing for a mechanistic understanding of DPRs, may not reflect processes in heterogeneous natural environments (e.g. Lepsˇ 2004; Grace et al. 2007). The discrepancy between the outcomes of DPR studies conducted in experimental plantations versus naturally established stands may be more pronounced in forest ecosystems because of the long-term dynamics associated with tree establishment, competition and mortality (Wardle, Walker & Bardgett 2004; Hart & Chen 2008). However, the opposite view argues that the benefits of species diversity in natural environments have been underestimated (Duffy 2009) because resource heterogeneity and the extended time frame associated with observational studies arguably increase the realized effects of niche complementarity (Stachowicz et al. 2008). To reconcile these divergent views, we hypothesize that natural stands may exhibit greater diversity benefits on productivity than experimental plantations. We also hypothesize that diversity effects increase with stand age since species complementarity tends to increase with time (Cardinale et al. 2007). Furthermore, we hypothesize that DPR patterns may differ among biomes. For example, a recent analysis demonstrates that the positive biodiversity effects are more apparent in the more stressful environment of the boreal biome than in the temperate biome (Paquette & Messier 2011). Here, we attempted to reconcile DPRs in forest ecosystems at a global scale using a meta-analysis of 54 DPR studies. We specifically examined how the effect size, a ratio of the aboveground productivity in polycultures to the average of those in monocultures, responded to changes in species richness, evenness, the extent of life-history trait variation, stand origin, stand age and biome. Materials and methods DATA COLLECTION We conducted an extensive literature search for studies of DPRs in forest ecosystems using the ISI Web of Science, Forest Science Database and Google Scholar. Different combinations of key words such as basal area, volume, biomass, productivity, forest, tree, species richness, plantation, diversity, biodiversity, pure, mixed species, single species, boreal, temperate and tropical were used for the search. We included studies that met the following criteria: (i) studies were published in reputable peer-reviewed journals; (ii) studies were implemented purposely to isolate the effect of tree species diversity from other factors such as soil conditions and topographic features of sampling plots; resulting in the use of 54 studies (published papers) in the meta-analysis (see Appendix S1 in Supporting Information). We extracted above-ground productivity measurements of live trees, climate, geographical location, species diversity and lifehistory traits from the original papers. For studies with multiple sampling dates, the latest data of productivity and species diversity measurements were used in the analysis. When an original study reported results graphically, we used SigmaScan Ò Pro version 5 (Systat Software Inc., Point Richmond, CA, USA) to digitally extract data from figures. A meta-analysis weighing both the variances and replication sizes from original studies is preferred. However, when the main goal of a meta-analysis is to generalize the commonality and differences between original studies, a drastic removal of studies would reduce the power and generality of the meta-analysis (Hillebrand & Cardinale 2010). Since neither variance nor replication size were available

3 744 Y. Zhang, H. Y. H. Chen & P. B. Reich in 29 of the 54 studies, we used an un-weighted meta-analysis to avoid a large loss of information. We used both species richness and evenness as measures of diversity from the original studies (four of the 54 studies) or calculated these metrics based on the species proportions by stand basal area (41 studies). When basal area was not reported in the original study, we used stem density (four studies) or crown cover (one study) to calculate richness and evenness. For the remaining four studies, evenness could not be determined, and it was treated as a missing value in our data set. Species evenness was estimated using J index (Pielou 1969) as the ratio of the observed Shannon s diversity of a stand to its maximum value with the same number of species: J 0 ¼ H 0 PS 1 i¼1 H 0 ¼ S lnð1 S Þ lnðsþ eqn 1 where H is the observed Shannon s index, and S is the species richness. We adopted a trait-based approach to the analysis (Hillebrand & Matthiessen 2009) by using the extent of life-history variation, selected a priori, as a measure of trait dissimilarity. Life-history variation in our study was qualified as discrete variables, because the traits we were interested in were qualitatively classified in the literature. Similar to our metric, life-history variation, qualified as non-objectively chosen a priori, has been found to be one of the best predictors at explaining productivity in grassland experiments (Cadotte et al. 2009; Marquard et al. 2009). The classification of presence absence of life-history variation was based on the explicit description in the original papers. When descriptions of interspecific differences in lifehistory traits were not presented in the original paper, we obtained these life-history traits from the USDA plant data base ( usda.gov/java/). This approach is practical because trait information on individual species is usually available for plants (Statzner, Bonada & Doledec 2007). The presence of life-history variation within a stand was defined apriorias the presence or absence of the following three contrasting traits among the dominant and co-dominant tree species within a stand: shade tolerant versus shade intolerant, fast versus slow growing and N fixation versus non-fixation. Stand origin and stand age were determined based on the site description in the original studies. However, stand age was not available for four studies, and it was treated as a missing value for these four studies. Biomes were identified as boreal, temperate or tropical. DATA ANALYSIS Effect size (ES) was calculated as a standardized measure of productivity across studies, using a response ratio: ES ij ¼ P ij eqn 2 M i where ES (i = 1, 2,... 54; j=1, 2,... n, n is the number of observations in each original study) is effect size of the jth observation in the ith study, P ij is the observed productivity of the jth observation in the ith study and M is the mean productivity of all monocultures within each study. When a study reported multiple types of mixtures, site conditions or stand ages, the M was calculated separately for each mixture type, site condition and stand age. Monocultures in most studies were pure single species stands. In studies where pure single species stands were not available (nine of the 54 studies), consistent with most observational studies (e.g. Brassard et al. 2011), monocultures were defined by the single species comprising 80% of stand basal area. For studies that examined the DPRs across different stand ages and site conditions, ES was calculated by using stands with the same age and site condition. The productivity measures were chosen in order of preference from biomass, volume and basal area from original studies when multiple measurements were reported as surrogates of above-ground productivity. As a recommended practice in meta-analysis (Hedges, Gurevitch & Curtis 1999), controls, i.e. monocultures, were not included in analysis, resulting in a total of n = 319 for analysis. To partition independent influences of species richness, species evenness and life-history traits, stand origin, stand age and biome on ES, which was transformed by natural logarithm, we used boosted regression trees (BRT). BRT is an advanced form of machine learning method based on classification and regression trees and is ideal for complex data with unidentified distributions (De ath 2007). Furthermore, BRT can accommodate missing values in predictors (De ath 2007; Elith, Leathwick & Hastie 2008). In BRT, multiple trees are fitted and combined in a forward stage-wise procedure to predict the response of the dependent variable to multiple predictors (De ath 2007). There are four input settings for BRT models: loss function, learning rate, tree complexity, bagging fraction and folds of cross-validation (De ath 2007). Gaussian error structure was chosen for the loss function because of the nature of our response variable (Ridgeway 2010). The learning rate regulates the number of trees fitted. In general, a low learning rate (and a large number of trees) enables BRT to generate highly complex response functions. A fast learning rate requires fewer trees but is subject to more noise from the bagging and a lack of smoothness in the response functions. The tree complexity, the number of splits in each tree, indicates the level of interactions in BRT, i.e. a value of three permits up to three-way interactions. The bagging fraction introduces randomness into BRT to reduce overfitting by a random selection of a portion of the data for model training and validation. The cross-validation specifies the number of times to randomly divide the data for model fitting and validation. The monotonic constraint on continuous variables may further reduce overfitting and the filtering of data noise, resulting in a simpler model. We fitted 36 BRT models with the combinations of the following settings: learning rates of 5, 1, 05 and 01, bag fractions of 0.40, 0.50 and 0.60, 5-, 8- and 10-fold cross-validations and a tree complexity of 4 to account for potential higher order interactions (De ath 2007). These values were chosen to find the optimal settings based on the empirical rules recommended for ecological modelling (De ath 2007; Elith, Leathwick & Hastie 2008). Among the fitted models, the best model had a cross-validation deviance, i.e. prediction error (PE), of 01 [±one stand error = 14] from the learning rate of 05, the bag fraction of 0.5 and 10-fold cross-validation. The consequent models were all fitted with these optimal settings. Further BRTs were fitted as follows: (i) since N fixation, growth habit and stand origin showed weak relative influences (<3%) in the best model, the three predictors were dropped from the model, which increased PE to by only 03 (to 04±11), suggesting negligible loss in the simplified model; (ii) fitting the BRT comprising main effects without interactions increased the PE to 11 (±11), indicating that interactions were existent but negligible; (iii) applying monotonic constraints to richness, evenness and stand age yielded a PE of 13 (±12). Because the models with and without monotonic constraints show similar results (Figs 1 and 2), our interpretation focused on the model with monotonic constraints. To interpret BRT results, we examined the relative influence of predictors by partitioning the total variation explained by each predictor in percentage. In the boosted model, the relative influence was the

4 Diversity and productivity relationships 745 Relative influence (%) Richness Evenness Shade tolerance N-fixation Monotonic Non-monotonic Growth habit Age Biome Stand origin Fig. 1. Results from boosted regression tree (BRT) analysis showing the relative contributions of predictors in percentage on natural logtransformed effect size. Bars in black are values for the BRT model with monotonic constraints on richness, evenness and stand age, and those in grey for the model without the monotonic constraints. n = 294 for evenness, 283 for stand age and 319 for remaining predictors. average of all trees in a BRT model (Friedman & Meulman 2003). We interpreted the response of ln(es) to predictors from the partial dependency plots, which illustrates the predicted effect of the individual predictor on the response variable while accounting for the average effects of other variables (De ath 2007; Elith, Leathwick & Hastie 2008). All BRT analyses were carried out in R using the gbm package (Ridgeway 2010) and supplemental functions (De ath 2007; Elith, Leathwick & Hastie 2008). Results The global average of ln(es) was 128, i.e. ES = 1.237, indicating that polycultures on average had 23.7% higher productivity than monocultures. The BRT model explained 21% of the variation in ln(es). Species evenness and richness contributed 34% and 13% of the explained variation, respectively (Fig. 1). ln(es) increased with richness from 2 to 6, then plateaued with richness 6 (Fig. 2a). When evenness was <0.60, it had no effect on ln(es). However, ln(es) increased markedly with evenness from 0.6 to 1 (Fig. 2b). The extent of life-history variation had a total of 35% relative influence on ln(es): 29% from shade tolerance, 4% from N fixation and 2% from growth habit, respectively (Fig. 1). The presence of contrasting shade tolerance in polycultures Predicted ln(es) Predicted ln(es) (a) (d) Richness Absence Presence Contrasting shade tolerance Evenness (b) (e) Absence Presence Contrasting N-fixation (c) Stand age (years) (f) Absence Presence Contrasting growth habit (g) (h) Predicted ln(es) Bo Te Tr Biome N P Stand origin Fig. 2. The predicted natural log-transformed effect size [ln(es)] in relation to predictors. (a) species richness, (b) evenness, (c) stand age (years), (d) presence or absence of contrasting shade tolerance, (e) presence or absence of contrasting nitrogen fixation, (f) presence or absence of contrasting growth habit, (g) biome (bo = boreal, te = temperate and tr = tropical) and (h) stand origin (N = natural stand, P = plantation). n = 294 for evenness, 283 for stand age and 319 for remaining predictors. The responses of ln(es) with monotonic constraints on richness, evenness and stand age are shown in black and without the constraints in grey.

5 746 Y. Zhang, H. Y. H. Chen & P. B. Reich had a predicted ln(es) value of 6, while the absence of contrasting shade tolerance had a ln(es) value of -3 (Fig. 2d). The predicted ln(es) was 32 and 03 with the presence and absence of contrasting N fixation traits, and 19 and 0 with the presence and absence of contrasting growth habits, respectively (Fig. 2e,f). Stand age had a 15% relative influence on ln(es) (Fig. 1). With increasing stand age, ln(es) generally increased, showing two steps of increase: a very modest one between 1.6 and 20 and a sharp, large one between 65 and 75 years of age (Fig. 2c). Biome and stand origin had a 1.4% and 1.2% relative influence on ln(es), respectively (Fig. 1), indicating that neither biome nor stand origin is important for explaining the variation in observed diversity effects. The predicted ln(es) of biomes was 1, 1 and 2 for boreal, temperate and tropical forest ecosystems, respectively (Fig. 2g). The predicted ln(es) of stand origin was 1 and 2 for natural stands and plantations, respectively (Fig. 2h). Discussion This meta-analysis is, to our knowledge, the first to reveal the distinct productivity responses to species richness, evenness, heterogeneity of life-history traits and stand age in forest ecosystems at the global scale. Our results demonstrate that polycultures are generally more productive than monocultures, and our findings offer new insight to the ever-evolving debate surrounding DPR studies. First, the importance of evenness as a central component of species diversity to drive DPRs is supported, provoking further investigations to unveil the underlying mechanisms. Second, our findings highlight the important role of contrasting traits on productivity in forest communities, including natural ones, extending findings shown previously in heavily manipulated grassland experiments (e.g. Cadotte et al. 2009). Third, our results are, to some extent, consistent with the findings of a previous meta-analysis, which showed net diversity effects on productivity are generally positive and increase over time in various types of plant communities (Cardinale et al. 2007). Fourth, the expected greater diversity effect from the enhanced expression of niche differentiation in observational studies due to higher intrinsic resource and spatial heterogeneity was not found, suggesting that the potential bias in diversity experiments in controlled environments may be overestimated (Weis, Madrigal & Cardinale 2008). Lastly, the limited influence on ln(es) from biomes rejected the hypothesis that DPR differs among biomes and supported the hypothesis that the positive DPR is a global phenomenon in forest ecosystems. As expected, ln(es) increased with increasing species richness, but plateaued when richness 6. Consistent with the predictions of the niche complementarity hypothesis, the positive relationship between productivity and richness observed in this study can probably be attributed to improved resource partitioning and or interspecific facilitation, especially as these played out over the multiple years of the study (cf. Isbell et al. 2011). The plateau at the high range of species richness appears to support the hypothesis that a ceiling of productivity gain may occur at a high level of species diversity due to functional redundancy (Naeem et al. 2009); therefore, richness (alone) as a reliable predictor of ecosystem functioning may be limited in natural communities that usually have high richness values (Baiser & Lockwood 2011). Surprisingly, species evenness explained the greatest variation in ln(es) among all predictors, showing that ln(es) increased with increasing evenness, although it had no effect in low ranges. Our results concur with those of a large-scale grassland experiment, in which overyielding in polycultures was mostly attributed to evenness (e.g. Kirwan et al. 2007). The strong positive effects on DPRs from increased evenness provide strong empirical evidence to support the theoretical prediction (see Hillebrand, Bennett & Cadotte 2008) that evenness affects the relative strength of interspecific and intraspecific interactions within communities, therefore causing a shift of DPR both in magnitudes and form. Greater evenness also likely increases functional trait diversity, when calculated using abundance-weighted values. The underappreciated role of evenness in previous empirical studies can be attributed to the limited levels of evenness (high and realistically low ) in those experiments (e.g. Polley, Wilsey & Derner 2003; Isbell, Polley & Wilsey 2009). However, the underlying mechanism for the evenness effect likely reflects both the extent of niche resource utilization and the heterogeneity of functional traits, but these links have not been established, and we call for future experiments to incorporate a wide range of evenness treatments to evaluate these potential links. The presence of contrasting life-history traits contributed considerably to the enhanced ln(es) of polycultures, probably because they are associated with important differences in plant functional traits. This agrees with Darwin s original idea (1859) that the presence of a divergence of character, or variations in life-history traits among species, is essential for reduced interspecific competition as a result of differentiated demands for resources and, in turn, improves productivity (Hector & Hooper 2002). Among all three traits, high shade tolerance variation within a community is likely to be the most important for forest ecosystems. Heterogeneity in shade tolerance is strongly associated with heterogeneity in important functional traits (Reich et al. 2003) and likely leads to more efficient light exploitation and utilization at the ecosystem level (Yachi & Loreau 2007; Coomes et al. 2009). The weak effects of heterogeneity in N fixation on ln(es) may reflect that N fixation from trees only account for a part of the important ecological process in forest ecosystems (Menge & Hedin 2009). The weak effect from heterogeneity in growth rates, which were classified based on growth performance in high-light environments, may be attributable to the high dependence between tree growth rate and shade tolerance (Pacala et al. 1994; Reich et al. 2003), therefore, resulting in the negligible independent effect of the heterogeneityingrowthrates. We found that ln(es) generally increased with stand age, and that the slopes of increasing age effects occurred during two periods: very weakly from about 1 to 20 years and very strongly from 65 to 75 years. The positive age effect is

6 Diversity and productivity relationships 747 consistent with the findings of previous studies (Cardinale et al. 2007; Fargione et al. 2007), in which net diversity effects are found to be positive, and enhanced with life-history stages. It is possible that the two phase changes reflect two key transitional stages in forests (Chen & Popadiouk 2002; Franklin et al. 2002) first, from aggrading and open to increasingly closed canopy (i.e. stand exclusion phase), and second, from mature to ageing (passing out of stem exclusion phase as older trees start to age and have dieback). Alternatively, however, the stepwise pattern of age effects may, to a degree, reflects the data availability. Particularly, the first rapid increase in age effects may represent the response of effect size in tropical plantations, while the second peak of age effects may represent canopy transition in boreal and temperate forests (Taylor & Chen 2011), in which the course of succession may impact the DPRs. Our results show that the stand origin had negligible effects on DPRs. The similarity between natural and planted origins in their effects on DPRs does not support the hypothesis that natural stands may exhibit stronger niche complementarity. Our result that natural stands showed biodiversity effects equal to those observed in plantations may, to a degree, relieve the doubt about the applicability of positive complementarity effects found in controlled experiments to natural systems (Lepsˇ 2004). Thus, the positive biodiversity effects are likely to be ubiquitous regardless of stand origin. Alternatively, the lack of effects from stand origin may be partially a result of the fact that plantations are field experiments at a fairly large scale and are therefore subject to similar environmental variability as observational studies in natural stands. Furthermore, the lack of strong confounding variables that may hide diversity effects, e.g. fertilization would lead to lower diversity but higher productivity in plantations, may also explain the similar responses between natural stands and plantations. In contrast to previous findings (Paquette & Messier 2011), our results show that the complementarity effects are similar across biomes. Paquette & Messier (2011) attributed the higher diversity effects on productivity in boreal, compared with temperate, forests to a stronger beneficial species interaction in the more environmentally stressed boreal climates. While this attribution is consistent with the prediction of the stress-gradient hypothesis (Maestre et al. 2009), which is based on a gradient of local site conditions rather than regional climates, more evidence would be required to generalize biome effects on the interplay between competition and facilitation as an independent mechanism influencing the strength of species complementarity at the biome scale. Testing specific mechanisms for the diversity benefits is beyond the empirical scope of this study but merits conceptual consideration. Key mechanisms are likely to include positive effects of niche differentiation and facilitation, supported by the importance of richness, evenness and trait strategy (i.e. shade tolerance) diversity found in our metaanalysis. Additionally, negative soil plant interactions via host-specific pathogens and or herbivores among conspecific individuals predicted by the Janzen Connell hypothesis (Janzen 1970; Connell 1971) may also explain the higher productivity in polycultures (Schnitzer et al. 2011). The higher productivity with higher species evenness may be the net outcome of both the reduced negative Janzen Connell effect and increased realization of niche differentiation and facilitation. Furthermore, these mechanisms may change and grow in number with stand development and site environments (Maestre et al. 2009; Cavard et al. 2011; Chen & Taylor 2011), as comprehensively documented for herbaceous communities (Isbell et al. 2011). In conclusion, our results show an average of 23.7% higher productivity in forest polycultures than monocultures. This contrasts markedly with a recent paper showing no relationship between productivity and species richness in 48 herbaceous sites (Adler et al. 2011). Although, in part, this may reflect differences in the gradients across stands of contrasting diversity, it may also reflect undiscovered issues and processes that differ in woody and herbaceous systems, and thus merits further study. Additionally, our analysis highlights the critical role of species evenness and the presence of contrasting life-history traits in defining DPRs in forest ecosystems and provides a broad guide for forest management practices aimed at facilitating higher per-unit-area productivity by increasing evenness and the extent of life-history traits among species in polycultures. The limitations of our analysis call for future studies to reveal the mechanisms for the roles of evenness, richness and the extent of life-history traits in defining DPRs. Acknowledgements We thank Ellen Macdonald, Phil Comeau and Yves Bergeron for their engaging discussion on the topic, Kevin Crowe, Dave Morris, Brian Brassard and Margot Downey for their editorial comments and anonymous reviewers for constructive comments to an earlier version of the manuscript. This study was supported by the Natural Sciences and Engineering Research Council of Canada (DG and SPG ). P.B.R. was supported by the US National Science Foundation LTER Program (DEB ) and the Wilderness Research Foundation. References Adler, P.B., Seabloom, E.W., Borer, E.T., Hillebrand, H., Hautier, Y., Hector, A. et al. (2011) Productivity is a poor predictor of plant species richness. Science, 333, Amoroso, M.M. & Turnblom, E.C. (2006) Comparing productivity of pure and mixed Douglas-fir and western hemlock plantations in the Pacific Northwest. Canadian Journal of Forest Research, 36, Baiser, B. & Lockwood, J.L. (2011) The relationship between functional and taxonomic homogenization. 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