Benefits of tree mixes in carbon plantings

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1 1. Title: Supplementary Table 1. Background information for studies included in metaanalyses Summary: Background information for studies included in meta-analyses. Format type: PDF File size: 441 KB SUPPLEMENTARY INFORMATION DOI: /NCLIMATE1862 Benefits of tree mixes in carbon plantings 2. Title: Supplementary Table 2. Average effect of tree richness on productivity; Categorical by species richness level Summary: Meta-Analysis results for the average effect of tree richness on productivity. Table includes: (a) variance-weighted, random factor analysis with species richness levels as categories, and (b) unweighted, fixed factor analysis with species richness levels as categories. Format type: PDF File size: 441 KB 3. Title: Supplementary Table 3. Comparison of mixtures to most-productive monocultures; Categorical by species richness level Summary: Meta-Analysis results for the comparison of mixtures to most-productive included monoculture. Table includes: (a) variance-weighted, random factor analysis with species richness levels as categories, and (b) unweighted, fixed factor analysis with species richness levels as categories. Format type: PDF File size: 519 KB 4. Title: Supplementary Table 4. Effects of nitrogen fixers and evidence of other possible mechanisms; Categorical by nitrogen fixer presence or absence Summary: Meta-Analysis results for the effects of nitrogen fixers on mixed-stand biomass and evidence of other possible mechanisms. Table includes: (a) examination of the average effect of nitrogen fixers on mixed-tree biomass via a variance-weighted, random factor analysis with nitrogen fixer presence/absence in tree mixture as categories, (b) examination NATURE CLIMATE CHANGE 1

2 of the average effect of nitrogen fixers on mixed-tree biomass via an unweighted, fixed factor analysis with nitrogen fixer presence/absence as categories, (c) examination of nitrogen fixer s effect on biomass in mixtures vs most-productive monocultures via a variance-weighted, fixed factor analysis with nitrogen fixer presence/absence as categories, and (d) examination of nitrogen fixer s effect on biomass in mixtures vs most-productive monocultures via an unweighted, fixed factor analysis with nitrogen fixer presence/absence as categories. Format type: PDF File size: 430 KB 5. Title: Supplementary Methods & Results Summary: Detailed methods and results for all meta-analyses. Format type: PDF File size: 437 KB

3 Benefits of tree-mixes in carbon plantings Supplementary Methods & Results Methods To conduct our meta-analyses we compiled all published studies we could locate that reported tree productivity data of monocultures vs tree mixtures grown at the same site. We found studies through an iterative process. We first conducted a Web of Science search using the key terms: plantation, diversity, and biomass or productivity or carbon. We then expanded our search drawing on the bibliographies listed within these studies. We included studies where tree monocultures and mixtures were grown at the same site because such direct comparison eliminates differences in productivity stemming from abiotic conditions, a noted driver of stand productivity 1. Included studies met the following criteria: (1) all reported aboveground biomass as Mg ha -1, or in units that could be converted to these, and (2) the planting designs employed distinguished between diversity and density effects. When experiments generated data contributing to multiple manuscripts, such as when forest productivity was reported over the maturation of the plantation, we included just the article that reported the most complete data set meeting the above criteria. Our search spanned multiple literatures with studies derived from carbon, forestry, and biodiversity-ecosystem functioning literatures. We encountered studies that used different metrics to measure productivity including stand biomass, tree/stand basal diameter, tree/stand basal area, and tree height. For the purpose of meta-analysis, it is not usually advised to combine different metrics 2. We chose to include studies in our analysis that reported biomass as Mg ha -1. We hypothesized this metric, which is a direct measure of biomass rather than a biomass index, most accurately 1

4 represented the carbon sequestered in stand above-ground biomass. In addition to studies reporting stand above-ground biomass, we also found studies that reported productivity as Mg ha -1 yr -1 or which reported both standing biomass (Mg ha -1 ) and productivity (Mg ha -1 yr - 1 ). We focused our analyses on biomass because we encountered more of these studies, and wanted to include the maximum sample size possible. From each study we extracted data on the richness levels included, sample size, whether trees were nitrogen fixers, and the mean and variance measures for biomass (Supplementary Table 1). Three studies did not include variance measures for all possible extractable cases and/or had a sample size of one 3-5. Because variance-weighted analyses are more powerful than unweighted analyses 6,7, we first conducted analyses using only data where variance data were available. We call these analyses weighted analyses. Missing variance information is a common problem when trying to conduct meta-analyses with ecological data and eliminating data without variance information can greatly reduce the number of studies included in an analysis. As such, an alternative accepted method is to weight all studies equally 6. We followed our initial analysis with a secondary analysis that weighted all cases equally to see how including more data-points might alter our results. We call these analyses unweighted analyses. We compiled a total of 48 possible cases from eight studies, each consisting of a comparison between a monoculture and a planted diversity level within a study. Therefore, if a study had two diversity levels (e.g. 1- and 2-species plantings) with two separate species being grown in monoculture, that study would have two comparisons included in our analysis (Mix 2

5 vs Species-A and Mix vs Species-B). This type of data-use is common in meta-analysis, although it is noted that this may raise issues with statistical independence 6. We selected the commonly used log response ratio (LnR) as our measure of effect size 6. This metric is particularly useful because it gives a direct measure of the proportional change in the targeted metric when back transformed after calculating summary statistics. In our case we used the log of mean biomass production in mixtures divided by mean biomass production in monoculture. We used random-effects models for all analyses to avoid the assumption that there was one true effect size across studies 6,7. We used the MetaWin program for all analyses 2. For analyses, we calculated mean effect size across cases and generated bias-corrected bootstrapped 95% confidence intervals using 5000 randomizations of data values. Bootstrapped intervals are more robust to data that do not meet the assumptions of conventional significance testing 6, as is the case when sample size is low and/or cases are drawn from multiple studies. For all analyses we report the unlogged response ratio and the 95% confidence interval. We performed three core meta-analyses using the general procedures outlined above to assess differences in biomass production due to tree richness. (1) First, we compared averaged productivity values of tree mixes vs monocultures. This is equivalent to asking if forests with higher species richness than monocultures on average result in higher biomass production. The categorical model allowed us to determine how 3

6 biomass at each richness level differed from monocultures. Across compiled studies we had four possible comparisons (monocultures vs 2-species mixes, 3-species mixes, 4-species mixes, or 5-species mixes). The weighted, categorical, random-effects model included a total of 27 cases (Supplementary Table 2a). We followed the weighted analysis with an unweighted, categorical analysis, which enabled us to include all of our collected cases (N=48) (Supplementary Table 2b). Because variance was assigned as 1 for all cases in this unweighted analysis, the estimate of pooled variance was less than or equal to zero, and therefore the MetaWin program analysed the data using a fixed-effects model. (2) We next investigated how the productivity of tree mixtures compared to their mostproductive monoculture (Supplementary Table 3a,b). This comparison is important because managers may base planting decisions on whether a mixture can perform better then the monoculture with the highest production, not an average productivity of all possible monocultures. We conducted an initial analysis using a weighted, categorical, randomeffects model (N=8) (Supplementary Table 3a). We followed the initial analysis with an unweighted, categorical model, which enabled us to include more of our collected cases (N=17) (Supplementary Table 2b). Because variance was assigned as 1 for all cases in this unweighted analysis, the estimate of pooled variance was less than or equal to zero, and therefore the MetaWin program analysed the data using a fixed effects model. Since each mixture is only used one time in case comparisons for this analysis, there was not issue with non-independence. 4

7 The categorical analysis of mixtures vs most productive monocultures dropped one richness level due to not enough cases for comparison (5-species vs monoculture; Potvin et al. 2011). We therefore also conducted a non-categorical analysis for both weighted and unweighted data (N=9 categorical, N=18 no categories ; Table 3 a, b). MetaWin defaulted to a fixed-effects model for the unweighted analysis because the estimate of pooled variance was less than or equal to zero. (3) Our final meta-analysis examined possible mechanisms by which increased tree richness might influence biomass production in mixtures (Supplementary Table 4a-d). Many of the studies in our analysis include nitrogen fixing trees in their experimental design. Nitrogen fixing trees may increase biomass in mixes by adding limiting nutrients to the soil, thereby facilitating the growth of neighboring trees. We wanted to examine the influence of nitrogen fixing trees on biomass production, and also determine if there was evidence for other unidentified mechanisms that favoured greater biomass production in mixtures. To do so, we divided our cases into two groups: experiments including nitrogen fixers and experiments not including nitrogen fixers (Supplementary Table 1). We then ran a categorical meta-analyses with these two groups as categories and assessed: (1) if mixtures including nitrogen fixers produced more biomass than monocultures, and (2) if mixtures not including nitrogen fixers produced more biomass than monocultures. We looked at both the averaged effect of diversity treatments on productivity as in Analysis #1 (Supplementary Table 4a,b) as well as the effect of diversity compared to the most productive monoculture as in Analysis #2 (Supplementary Table 4c,d). Results 5

8 Average effect of tree richness on productivity Weighted, categorical: We were able to use 27 cases from 6 studies for the categorical, weighted comparison of biomass in monocultures vs mixtures (Supplementary Table 1). No cases were excluded due to low sample sizes within any single category. Meta-analysis from these cases indicates that tree richness generally increases biomass production with 95% confidence (Supplementary Table 2a). Two of the three diversity categories included in this analysis showed an increase in forest biomass in diverse treatments compared with monocultures with 95% confidence (2-species vs monocultures and 4-species vs monocultures) (Supplementary Table 2a). The final category of 5-species vs monocultures showed a mean increase in biomass of 26% but the 95% confidence interval overlapped zero (-19 to 87%), indicating that there was no difference in biomass production between mixes and monocultures at this diversity level. Unweighted, categorical: We were able to include all 48 cases from 8 studies when conducting the unweighted, categorical comparison of biomass in monocultures vs mixtures (Supplementary Table 1). No cases were excluded due to low sample sizes within any single category. As in the weighted analysis, meta-analysis of unweighted cases indicates that tree richness generally increases biomass production with 95% confidence (Supplementary Table 2b). Including all 48 cases increased the number of categories included in the analysis from three to four, and added a category comparing 3-species mixtures to monocultures. The 5-species mixture vs monoculture category again had a mean indicating an increase in biomass with 6

9 increasing richness (29%) and a 95% confidence interval that crossed zero (-15 to 97%). All other categories showed significant increases in biomass production with increasing tree richness (Supplementary Table 2b). Comparison of mixtures to most-productive monocultures Weighted, categorical: We were able to use 8 cases from 5 studies for the categorical weighted comparison of biomass in most productive monoculture vs mixtures (Supplementary Table 1). Both categories included in the analysis (2-species vs monoculture and 4-species vs monoculture) showed an increase in forest biomass in diverse treatments compared with monocultures with 95% confidence, and total effect size was positive with a positive confidence interval (Supplementary Table 3a). One case was excluded from the categorical analysis due to a singular sample in the 5- species vs monoculture category. When the analysis was repeated without categories, mean effect size remained positive but the 95% confidence interval crossed zero (Supplementary Table 3a). Unweighted, categorical: Unweighted analysis increased case number to 17 derived from 8 studies and added an additional category to the analysis (3-species mixtures vs most productive monocultures). Results for the unweighted analysis were similar to those in the weighted analysis, with all categories indicating an increase in stand biomass with increasing diversity when compared to most productive monocultures (Supplementary Table 3a,b). 7

10 Again, one case was excluded from the categorical analysis due to a singular sample in the 5-species vs monoculture category. When the analysis was repeated without categories, mean effect size and 95% confidence interval were greater than 1 indicating that increasing stand diversity resulted in greater biomass than most productive constituent monocultures. This result differed from the weighted analysis (Supplementary Table 3a,b). Effects of nitrogen fixers and evidence of other possible mechanisms Average effect of treatments on biomass; Weighted, categorical: We were able to use 27 cases from 6 studies for the categorical weighted analysis that separated N-fixer containing treatments from those without N-fixers (Supplementary Table 1). As with the analysis that binned data according to richness level, this meta-analysis indicates that increased tree richness generally increases biomass production with 95% confidence (Supplementary Table 4a). Total mean unlogged effect size was 1.68, corresponding to a 68% increase in biomass in mixtures compared to monocultures (95% CI: 40% to 103%). While the overall finding in this analysis is that productivity increases with diversity, only the N-fixer category indicated a positive relationship (Supplementary Table 4c), while the no N- fixer category indicated a neutral relationship due to a 95% CI that crossed zero. These results imply that tree mixtures including N-fixers increased in productivity compared to the average values of monocultures, while mixtures without N-fixers produced similar amounts of biomass as averaged monocultures (Supplementary Table 4a). 8

11 Average effect of treatments on biomass; Unweighted, categorical: We were able to include 48 cases from 8 studies when conducting the unweighted, categorical N-fixer analysis (Supplementary Table 1). This analysis again indicated that increased tree richness generally increases biomass production with 95% confidence (Supplementary Table 4b), however, in this analysis both N-fixing and non N-fixing categories indicated a positive role for tree richness on biomass with 95% confidence (Supplementary Table 4b). Comparison of mixtures to most-productive monocultures; Weighted, categorical: We were able to use 9 cases from 6 studies for the categorical weighted comparison of biomass in most productive monoculture vs mixtures using categories for N-fixers and No N-fixers (Supplementary Table 1). This meta-analysis indicates that tree richness is similar between tree mixtures and single most productive monocultures with 95% confidence (Supplementary Table 4c). While the overall finding in this analysis is that biomass does not increase with diversity, the N-fixer category indicated a positive relationship (Supplementary Table 4c). The mean effect size for the No N-fixer category was negative with a 95% confidence interval that crosses zero. These results imply that mixes including N-fixers increased in productivity compared to the most productive constituent monoculture, while mixes without N-fixers, on average, produced similar amounts of biomass to the most productive monoculture. Comparison of mixtures to most-productive monocultures; Unweighted, categorical: Using unweighted data allowed us to increase total analysis sample size to 18 cases from 8 studies (Supplementary Table 1). Adding more cases led to different overall results, with mixtures 9

12 increasing in stand biomass compared with single most productive monocultures. In addition, the mean unlogged effect size of No N-fixer treatments shifted from negative to positive (i.e. from a 25% decrease in biomass with increasing richness to a 6% increase). Despite this, the 95% confidence intervals still overlapped zero (-15 to 24%; Supplementary Table 4d), indicating that biomass in the No N-fixer tree mixes produced similar amounts of biomass as most productive monocultures. 10

13 References 1 Vilá, M., Vayreda, J., Gracia, C. & Ibáñez, J. J. Does tree diversity increase wood production in pine forests? Oecologia 135, (2003). 2 MetaWin: Statistical Software for Meta-Analysis v.2.0 (Sinaurer Associates, Sunderland, Massachusetts, 1999). 3 Potvin, C. et al. An ecosystem approach to biodiversity effects: Carbon pools in a tropical tree plantation. Forest Ecology and Management 261, (2011). 4 Austin, M. T. Short-rotation biomass trial of mixed and pure stands of nitrogen-fixing trees and Eucalyptus grandis. Australian Forestry 60, (1997). 5 Parrotta, J. Productivity, nutrient cycling, and succession in single- and mixed-species plantations of Casuarina equisetifolia, Eucalyptus robusta, and Leucaena leucocephala in Puerto Rico. Forest Ecology and Management 124, (1999). 6 Gurevitch, J. & Hedges, L. V. in Design and analysis of ecological experiments eds S. M. Scheiner & J. Gurevitch) (Oxford University Press, 2001). 7 Gurevitch, J. & Hedges, L. V. Statistical issues in ecological meta-analyses. Ecology 80, (1999). 11

14 Benefits of tree-mixes in carbon plantings Supplementary Table 1. Background information for studies included in meta-analyses Source Location Exp/Obs Species richness Austin, MT, JL Brewbaker, R Wheeler, JH Fownes. Australian Forestry 60, (1997). Bauhus, J, AP van Winden, AB Nicotra. Canadian Journal of Forest Research 34, (2004). Kaye, JP, SC Resh, MW Kaye, RA Chimner. Ecology 81, (2000). Forrester, DI, AL Cowie, J Bauhus, JT Wood. Forrester, RT. Plant and Soil 280, (2006). Parrotta, JA. Forest Ecology and Management 124, (1999). Piotto, D, D Craven, F Montagnini, F Alice. New Forests 39, (2010). Hawaii, USA Victoria, Australia Hawaii, USA Pot trial, Australia Puerto Rico Exp 1,2 57.6, 59.6, 60.3* (mix 2&3), 63.4*(mix 1) Exp 1,2 35, 68* Mean above-ground biomass (Mg ha -1 ) per treatment. The monoculture value used for overyielding analysis is marked with an * Monoculture 2 species 3 species 4 species Mix 1: 53.6 Mix 2: 60.2 Mix 3: species N-fixer in study Number of cases Variance obtainable (i.e. in text, figure, via calculation) Mechanism for increased productivity as attributed by study authors Yes 6 No Authors did not interpret Yes 2 Yes Above-ground niche segregation, resource use complementarity. Exp 1, , * Yes 2 Yes Not known. Suggest synergistic relationship between tree composition and tree carbon accumulation. Exp 1, , 39.60* Yes 2 Yes Facilitation. However, competition between species increased when N was added, reducing Acacia growth. Exp 1,2 62.5, 71.8*(mix 2), 105.3*(mix 1&3) Costa Rica Exp 1,4 Expt 1: 0,0, 30.7, 86.3* Expt 2: 0, 56.7, 99.1, 17.1* Expt 3: 0, 33.2, 37.6, 47* Mix 1: 96.5 Mix 2: Mix 3: Yes 6 No Not known. Suggest nutrient or light use efficiency greater in mix, or site conditions differed among treatments. - - Expt 1: 99.4 Expt 2: Expt 3: Yes 12 Yes Reduced disease/pest infestation, less competition for resources. Notes Reported data for 3 harvest rotations at: 1, 2, and 4 years. Only included the 4- year rotation in metaanalysis. No variance information given. Field trials include two densities: 2m or 3m. Data from trials averaged for meta-analysis. Pot study with 4 nutrient manipulations: Low P, High P, Low N, High N. Data from trials averaged for metaanalysis. No variance information given. Reported 3 separate experiments using different suites of trees. Kept separate for meta-analysis.

15 Potvin, C, L Mancilla, N Buchmann, J Monteza, T Morre, M Murphy, Y Oelmann, M Scherer-Lorenzen, BL Turner, W Wilcke, F Zeugin, S Wolf. Forest Ecology and Management 261, (2011). Pretzsch, H, G Schütz. European Journal of Forest Research 128, (2009). Sardinilla, Panama South Bavaria, Germany Exp 1,3,5 5.88, 8.86, 11.58, 17.89* (mix 1&2), 23.14*(mix 3&6-sp) - Mix 1: 20.93, Mix 2: 22.91, Mix 3: No 14 Yes for 1- and 5- species treatments Did not interpret effects of species richness on above-ground tree biomass. Reported no significant richness effect on total aboveground biomass (includes trees, litter and coarse woody debris). Obs 1, , 347* No 4 Yes Facilitation of spruce by beech via induced greater crown efficiency & increased nutrient supply. In polycultures, resource acquisition complementarity leads to reduced competition. Data obtained from authors because the carbon in the tree pool was not specified in paper. Used 2009 data. No variance information for 3-species stands because a single rep of these treatments was planted. Treatments including Cordia alliodora were excluded from the analysis because no data was collected in these monocultures due to a large tree die-off. Reported 2 separate experiments using different locations. Reported that abiotic conditions varied widely, so kept separate for meta-analysis.

16 Benefits of tree-mixes in carbon plantings Supplementary Table 2. Average effect of tree richness on productivity; Categorical by species richness level a. Weighted, Random N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 27 (6) 1.68 (68% increase) 41 to 103% increase 2-sp vs mono 10 (4) 1.67 (67% increase) 33 to 115% increase 3-sp vs mono no cases 4-sp vs mono 12 (1) 1.86 (86% increase) 42 to 150% increase 5-sp vs mono 5 (1) 1.26 (26% increase) -19 to 87% neutral b. Unweighted, Fixed (due to estimate of pooled variance 0) N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 48 (8) 1.57 (57% increase) 39 to 78% increase 2-sp vs mono 22 (6) 1.38 (38% increase) 20 to 61% increase 3-sp vs mono 9 (1) 1.93 (93% increase) 52 to 152% increase 4-sp vs mono 12 (1) 1.86 (86% increase) 44 to 152% increase 5-sp vs mono 5 (1) 1.29 (29% increase) -15 to 97% neutral

17 Benefits of tree-mixes in carbon plantings Supplementary Table 3. Comparison of mixtures to most-productive monocultures; Categorical by species richness level a. Weighted, Random* N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 8 (5) 1.25 (25% increase) 15 to 46% increase 2-sp vs mono 5 (4) 1.37 (37% increase) 11 to 83% increase 3-sp vs mono no cases 4-sp vs mono 3 (1) 1.17 (17% increase) 15 to 19% increase 5-sp vs mono excluded from analysis due to single case *When analysis completed without using categories, the Overall effect is neutral rather than increasing (mean % change = 18% increase, 95% CI = -4 to 39). This is due to the 5- species treatment not being eliminated from the analysis. b. Unweighted, Fixed (due to estimate of pooled variance 0)* N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 17 (8) 1.20 (20% increase) 8 to 36% increase 2-sp vs mono 11 (6) 1.20 (20% increase) 2 to 43% increase 3-sp vs mono 3 (1) 1.25 (25% increase) 21 to 31% increase 4-sp vs mono 3 (1) 1.17 (17% increase) 15 to 20% increase 5-sp vs mono excluded from analysis due to single case *When analysis completed without using categories, thus including the 5-species treatment, the Overall effect remains increasing (mean % change = 17% increase, 95% CI = 4 to 32).

18 Benefits of tree-mixes in carbon plantings Supplementary Table 4. Effects of nitrogen fixers and evidence of other possible mechanisms; Categorical by nitrogen fixer presence or absence Average effect of treatments on biomass a. Weighted, Random N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 27 (6) 1.68 (68% increase) 40 to 103% increase N-fixer vs mono 18 (4) 1.89 (89% increase) 55 to 138% increase No N-fixer vs mono 9 (2) 1.24 (24% increase) -5 to 58% neutral b. Unweighted, Fixed (due to estimate of pooled variance 0) N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 48 (8) 1.57 (57% increase) 38 to 79% increase N-fixer vs mono 30 (6) 1.58 (58% increase) 35 to 88% increase No N-fixer vs mono 18 (2) 1.56 (56% increase) 27 to 92% increase Comparison of mixtures to most-productive monocultures c. Weighted, Fixed (due to estimate of pooled variance 0) N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 9 (6) 1.17 (17% increase) -2 to 37% neutral N-fixer vs mono 6 (4) 1.26 (26% increase) 16 to 48% increase No N-fixer vs mono 3 (2) 0.75 (25% decrease) -33 to 5% neutral d. Unweighted, Fixed (due to estimate of pooled variance 0) N cases Unlogged mean effect size 95% CI Overall effect (# studies) (% change mixes vs mono) Total 18 (8) 1.17 (17% increase) 3 to 32% increase N-fixer vs mono 12 (6) 1.22 (22% increase) 6 to 44% increase No N-fixer vs mono 6 (2) 1.06 (6% increase) -15 to 24% neutral