SUPPORTING INFORMATION

Additional supporting information may be found in the online version of this article:

Appendix S1 Supporting figures and tables

Table S1. Pearson’s correlation coefficients for pairwise correlations between aboveground net

primary production (ANPP) and physiographic, stand characteristics and canopy complexity

predictor variables, as well as for pairwise correlations between all independent variables.

Significant correlations (P < 0.1) are shown in bold.

Table S2. Principal component loadings for the first three principal components (PC1 to PC3).

Loadings greater than 0.3 are shown in bold to highlight patterns. PC1 was used to represent

canopy temporal dynamics during 2009-2015, where positive numbers represent high among-

year variability in canopy complexity metrics and negative numbers represent canopies that are

becoming taller and more porous. Note that ANPP is negatively correlated with PC1.

Correlations greater than r ≥ 0.30 are shown in bold (McCune and Grace, 2002).

Table S3. Goodness-of-fit indices for the nine structural equation models conducted. Chi-square

(χ2) tests with degrees of freedom (df) and P-value, root mean square error of approximation

(REMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI),

goodness-of-fit index (GFI), and Akaike information criteria (AIC). Significant correlations (P <

0.1) are shown in bold.

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Table S4. The standardized regression coefficients for the direct and indirect effects (based on

path analysis) of hypothesized linkages among predictor variables and their effects on canopy

complexity metrics and aboveground net primary production (ANPP) for the nine structural

equation models. Model 7 had poor fit statistics and therefore excluded (See Table S3).

Figure S1. NMS ordination axis 3 solution for species composition at the UMBS Ameriflux site.

ACPE, Acer Pensylvanicum; ACRU, Acer rubrum; ACSA, Acer saccharum; AMEL,

Amelanchier spp.; BEAL, Betula alleghaniensis; BEPA, Betula papyrifera; FAGR, Fagus

grandifolia; PIRE, Pinus resinosa; PIST, Pinus strobus; POGR, Populus grandidentata; POTR,

Populus tremuloides; PRSE, Prunus serotina; and QURU, Quercus rubra.

Figure S2. Standardized model-weighted estimates of each parameter predicting aboveground

net primary productivity. Significant beta coefficients are indicated by †= p<0.10, *= p<0.05,

**= p<0.01.

Figure S3. The best-fit structural equation models (SEM) relating site physiography, stand

dynamics, canopy complexity and aboveground net primary productivity (ANPP). Predictor

variables were derived from the set of candidate multiple regression models (See Table 2). SEMs

were broadly consistent in which predictor variables had significant effects, as well as the

magnitude and direction of these effects, with model 5 having the lowest AIC and highest

variance in ANPP explained. Solid and dashed lines indicate direct and indirect effects on

canopy complexity (objective 1) and ANPP (objective 2), respectively. The standardized

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regression coefficient is shown for each path and the R2 indicates the total variation in a

dependent variable that is explained by the combined independent variables.

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Table S1:

ANPP PC1 BOC ELH POR Rc TOC LAI StemDen DBHσ DBHskew DBHkurtGini

ANPP 1.00PC1 -0.34 1.00BOC -0.10 0.10 1.00ELH 0.13 -0.46 0.39 1.00POR -0.04 0.01 0.46 0.27 1.00Rc 0.32 -0.14 -0.18 0.40 0.36 1.00TOC 0.15 -0.20 0.39 0.67 0.68 0.65 1.00LAI 0.07 -0.06 0.00 0.19 0.11 0.24 0.26 1.00StemDen 0.28 -0.24 -0.26 -0.20 -0.38 -0.11 -0.19 0.12 1.00DBHσ 0.09 -0.08 0.13 0.36 0.15 0.32 0.29 0.25 -0.60 1.00DBHskew 0.10 -0.01 -0.02 -0.31 -0.35 -0.34 -0.29 0.09 0.32 -0.04 1.00DBHkurt 0.07 0.04 -0.13 -0.32 -0.44 -0.32 -0.34 0.14 0.48 -0.27 0.90 1.00Gini 0.04 -0.17 0.25 0.21 -0.01 -0.02 0.10 0.16 -0.34 0.79 0.18 -0.11 1.00

Elv -0.25 0.00 -0.34 0.00 -0.17 -0.06 -0.14 0.07 0.08 -0.05 -0.18 -0.02 -0.21

SM 0.09 -0.11 0.04 0.43 0.29 0.40 0.49 0.20 -0.21 0.30 -0.33 -0.33 0.11

OMC 0.07 -0.16 0.05 0.07 0.00 0.00 0.11 0.45 0.10 0.10 0.14 0.03 0.11

NMS1 0.09 -0.03 -0.14 0.25 0.08 0.28 0.15 0.18 0.07 -0.14 -0.39 -0.15 -0.44

NMS2 -0.40 0.03 -0.16 0.00 -0.24 -0.34 -0.25 -0.11 0.04 -0.09 0.03 0.17 -0.11

NMS3 0.20 0.02 -0.05 0.07 0.09 0.19 0.24 0.29 0.17 -0.09 -0.11 -0.08 -0.18

H’ -0.10 -0.02 0.01 -0.25 -0.11 -0.26 -0.21 0.10 -0.17 0.31 0.21 0.03 0.41

continued

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Table S1 continued

Elv SM OMC NMS1 NMS2 NMS3 H’

1.000.06 1.000.20 0.00 1.000.28 0.21 -0.06 1.000.55 -0.13 0.00 0.21 1.000.06 0.28 0.40 -0.04 -0.22 1.000.01 0.02 -0.02 -0.30 -0.03 -0.23 1.00

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Table S2

Parameter PC1 PC2 PC3BOCcv 0.68 0.09 0.32BOCsl -0.62 -0.07 0.41ELHcv 0.25 0.09 0.60ELHsl 0.19 0.75 -0.07PORcv 0.64 -0.17 0.29PORsl -0.70 0.02 0.43Rccv 0.52 0.59 -0.15Rcsl 0.11 0.50 -0.38TOCcv 0.63 -0.31 0.34TOCsl -0.57 0.55 0.43NPP -0.34 -0.05 0.10

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Table S3

χ2 values associated with a non-significant P value (>0.05) indicate no difference between observed and expected covariance matrices. An RMSEA and SRMR <0.05 and a GFI and CFI >0.05 indicate a good model fit (Hooper et al., 2008). Model 3 included Gini and NMS1 and had poor χ2 values even after removal of pathways suggested by the “modindices” function in R, and was therefore excluded from SEM analyses.

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Model # χ2 df P value RMSEA SRMR CFI GFI AIC1 1.915 3 0.590 0.000 0.043 1.000 0.988 7422 1.099 3 0.777 0.000 0.034 1.000 0.989 7413 9.867 3 0.020 0.216 0.098 0.702 0.942 7474 1.877 3 0.590 0.000 0.034 1.000 0.993 7185 1.586 3 0.663 0.000 0.031 1.000 0.987 7156 1.331 3 0.722 0.000 0.030 1.000 0.988 7217 0.514 3 0.916 0.000 0.024 1.000 0.989 7408 1.592 3 0.661 0.000 0.044 1.000 0.999 7379 1.790 3 0.617 0.000 0.042 1.000 0.988 744

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Table S4

Pathway Effect (pathway coefficient)PC1 Elv DBHσ Stem

DenGini Rc NMS1 NMS2 H’

Model 1Direct to ANPP -0.31 -0.26 NS - - 0.25 NS - -Indirect ANPP through PC1 - - NS - - NS NS - -Indirect ANPP through Rc - - NS - - - NS - -Indirect ANPP through DBHσ - NS - - - - - - -Indirect ANPP through NMS1 - NS - - - - - - -Pathway to PC1 - - NS - - NS NS - -Pathway to Rc - - 0.36 - - - 0.33 - -

Model 2Direct to ANPP -0.24 -0.28 - 0.27 - 0.29 NS - -Indirect ANPP through PC1 - - - NS - NS NS - -Indirect ANPP through Rc - - - NS - - NS - -Indirect ANPP through StemDen

- NS - - - - - - -

Indirect ANPP through NMS1 0.28 - - - - - - -Pathway to PC1 - - - -0.27 - NS NS - -Pathway to Rc - - NS - - 0.29 - -

Model 3Direct to ANPPIndirect ANPP through PC1Indirect ANPP through RcIndirect ANPP through GiniIndirect ANPP through NMS1Pathway to PC1Pathway to Rc

Model 4Direct to ANPP -0.31 NS NS - - NS - -0.29 -Indirect ANPP through PC1 - - NS - - NS - NS -Indirect ANPP through Rc - - NS - - - - NS -Indirect ANPP through DBHσ - NS - - - - - - -Indirect ANPP through NMS2 - -0.16 - - - - - - -Pathway to PC1 - - NS - - NS - NS -Pathway to Rc - - 0.29 - - - - -0.32 -

Model 5Direct to ANPP -0.24 NS - 0.26 - 0.21 - -0.27 -Indirect ANPP through PC1 - - - NS - NS - NS -

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Indirect ANPP through Rc - - - NS - - - NS -Indirect ANPP through StemDen

- NS - - - - - - -

Indirect ANPP through NMS2 - -0.15 - - - - - - -Pathway to PC1 - - - -0.26 - NS - NS -Pathway to Rc - - - NS - - -0.34 -

Model 6Direct to ANPP -0.32 NS - - NS NS - -0.29 -Indirect ANPP through PC1 - - - - NS NS - NS -Indirect ANPP through Rc - - - - - NS - NS -Indirect ANPP through Gini - NS - - - - - - -Indirect ANPP through NMS2 - 0.55 - - - - - - -Pathway to PC1 - - - - NS NS - NS -Pathway to Rc - - - - NS - - -0.34 -

Model 7Direct to ANPP -0.32 -0.25 NS - 0.23 - - NSIndirect ANPP through PC1 - - NS - NS - - NSIndirect ANPP through Rc - - NS - - - - NSIndirect ANPP through DBHσ - NS - - - - - -Indirect ANPP through H - NS - - - - - -Pathway to PC1 - - NS - NS - - NSPathway to Rc - - 0.34 - - - - 0.22

Model 8Direct to ANPP -0.25 -0.26 - 0.25 0.28 - - NSIndirect ANPP through PC1 - - - NS NS - - NSIndirect ANPP through Rc - - - NS - - - NSIndirect ANPP through StemDen

- NS - - - - - -

Indirect ANPP through H - NS - - - - - -Pathway to PC1 - - - -0.28 NS - - NSPathway to Rc - - - NS - - - NS

Model 9Direct to ANPP -0.32 -0.25 - - NS 0.23 - - NSIndirect ANPP through PC1 - - - - NS NS - - NSIndirect ANPP through Rc - - - - NS - - - NSIndirect ANPP through Gini - NS - - - - - - -Indirect ANPP through H - NS - - - - - - -Pathway to PC1 - - - - NS NS - - NSPathway to Rc - - - - NS - - - NSEffects (pathway coefficients) describe the relative strength of the relationship between a given predictor variable and its dependent variable. Positive values indicate a positive relationship, negative values indicate a negative relationship, NS indicates non-significant relationships (P > 0.10), and a – symbol indicates the variable was not included in the pathway. Note – The SEM for Model 3 could not be run due to poor fit statistics.

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Figure S1.

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Figure S2

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Figure S3

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