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Stand structural diversity and species with leaf nitrogen conservative strategy drive aboveground carbon storage in tropical old-growth forests

Background: Tropical old-growth forest ecosystems are essential for global carbon regulation. Even there are mounting evidences for the significance of species and functional composition, stand structure and elevation gradients on aboveground carbon storage, the relative strengths of these drivers and whether elevation effects via biotic factors are not clear. Furthermore, the mechanisms (the mass-ratio hypothesis or niche complementarity hypothesis) are still poorly understood. Methods: We analyzed aboveground carbon storage, species diversity, stand structural diversity, community-weighted mean (CWM) of functional traits and functional diversity (FDvar) using date from 56 old-growth forest communities with different elevation gradients in Dawei mountain of southwestern China. Multiple regression models were used to test the relative importance of the predictor variables and structural equation model was used to explore the direct and indirect influences on the aboveground carbon storage. Results: Our optimal multiple regression model show aboveground carbon storage is mostly affected by diameter at breast height (DBH) diversity, followed by FDvar of dry matter concentration in mature leaves and CWM nitrogen concentration in young leaves. The final structural equation model indicates elevation indirectly affected aboveground carbon storage via DBH diversity. The stand structural diversity, but not species diversity or functional diversity, enhanced aboveground carbon storage. Conclusions: Our results indicate mass-ratio and niche complementarity effect promote aboveground carbon storage simultaneously. The complex stand structure and species with leaf nitrogen conservative strategy were the crucial drivers of aboveground carbon storage in tropical old-growth forests.


Background
Tropical forests are essential in global carbon (C) regulation (Saatchi et al. 2011). There are increasing evidence demonstrated the relationships between ecosystem functioning and biodiversity in natural forests ( The mass ratio hypothesis (Grime et al. 1998) and niche complementarity hypothesis (Tilman et al. 1997) are the two main mechanisms how species diversity would influence ecosystem functioning. The niche complementarity hypothesis demonstrates functional diversity (FDvar) and species diversity could increase the efficiency of resource utilization, thereby increasing C storage (Díaz et al. 2011). The mass ratio hypothesis predicts the main species in the community determined ecosystem functions and can be examined by the correlations between ecosystem functioning and community-weighted mean (CWM) functional traits (Díaz et al. 2007). And, the species that enhanced ecosystem functioning may be differently dominated by conservative or acquisitive traits (Conti et  Abiotic factors were also the important drivers of aboveground C storage through determining plant survival and growth (Sullivan et al. 2017;Li et al. 2019). Microclimate impacts species abundances and distribution (Murphy et al. 2015), which in turn affects both biological and physical stand attributes (Fahey et al. 2015). Thus, abiotic factors could indirectly affect C storage by affecting biotic factors (Fotis et al. 2018;Li et al. 2019). Elevation, considered as a comprehensive factor reflecting climate, negatively affected the ecosystem functioning (Fotis et al. 2018). Meanwhile, Cavanaugh (2014) reported elevation was negatively correlated with species diversity and functional diversity, however, no effect was found on C storage.
In this study, we address three questions using 56 plots in tropical natural forests in southwestern China. First, how biotic (species diversity, stand structural diversity, functional diversity and functional composition) and abiotic (elevation gradients) factors drive aboveground C storage? Second, how these predictor variables relatively drive aboveground C storage. Third, how the predictor variables directly or indirectly influence aboveground C storage. Thus, we hypothesize (1) species diversity, stand structural diversity and functional diversity can enhance aboveground C storage simultaneously (2) CWM functional traits affected aboveground C storage positively or negatively; (3) elevation negatively affects aboveground C storage; elevation can also indirectly affect aboveground C storage via biotic factors.

Site description and plots design
Our study was conducted in Dawei Mountain area (22°35′-23°07′ N, 103°20′-104°03′ E), located in Yunnan Province, Southwestern China. The annual rainfall is 1700-1900 mm. The annual average temperature is 22.6 ℃. The coldest month (January) is 15.2 ℃ and the hottest month (July) is 27.7 ℃. There is no frost in the whole year. The elevation range between 225 m and 2365 m for the highest peak of Dajian Mountain. The forest types change significantly from the bottom to the top of mountain.
In order to ensure the comparability of forest communities in different elevation gradients, only old-growth forests far away from disturbance were selected. A vegetation and soil comprehensive investigation was conducted before plot selection. With the help of local forestry department, four elevation gradients, containing 800 m, 1200 m, 1600 m and 2000 m, respectively were selected and fourteen 20 m × 20 m plots were set with a distance more than 100 m for each plot in each elevation gradient. Total 56 plots were selected. Between 2017-2018, all the individual trees were identified in the plot or lab through the collected specimen to species level (Li et al. 2019). Details of species composition are listed in Annex 1 of Additional file 1.

Quantification of aboveground C storage
We measured the diameter at breast height (DBH) of all the trees with DBH higher than 5 cm in each plot. The telescopic pole was used to measure tree height lower than 18 m and the clinometer was used for the height measurement for the other trees. The allometric equation, which was according to height, DBH and wood density, was used to calculate the aboveground biomass of individuals whose DBH were higher than 5 cm (Chave et al. 2014). Aboveground C storage was calculated by multiplying aboveground biomass by 0.5 (Chave et al. 2005). Wood density for most species were obtained from field investigation and other species' wood density were used the wood density of average family or average wood density of corresponding plot (Cavanaugh et al. 2014). The global allometric equation was calculated as follows: where ρ represents the wood density (g cm − 3 ), H represents the height (m), DBH represents diameter at breast height (cm). where CWM (trait X ) represents the CWM X trait, s represents the species number in each plot, p i represents i th species' relative abundance in the plot and x i represents the i th species' trait value.
Functional diversity was calculated as follows (Conti and Díaz. 2013):

Statistical analyses
One-way ANOVA and least square difference (LSD) multiple comparison tests were used to test the difference in aboveground C storage and abiotic factors among different elevations (Annex 3 of Additional file 1). Pearson correlation coefficients were employed to investigate the relationships among aboveground C storage, species diversity, functional dominances, functional diversity, stand structural diversity and elevation in Annex Simple and multiple linear regressions were employed to explore the relationships between aboveground C storage and explanatory variables (Li et al. 2019). The combination of 28 indices yields total 268435455 models, which were beyond the capacity of R (Ali et al. 2017). To select the optimal subset of predictor variables of the aboveground C storage, ordinary least squares multiple regression analyses was first conducted with all 12 CWM indices, resulting 4095 possible models. Then we used the other 15 biotic indices (FDvar, species diversity and stand structural diversity) to conduct multiple regression analyses, which resulted 32367 possible models. Finally, we combined the elevation with the two former best subsets of predictors to conduct multiple analyses and the 8 predict variables resulted 255 possible models. All possible models were evaluated with corrected Akaike information criterion (AICc) and the best-fit regression model were selected based on the lowest AICc values (Li et al. 2019;Wen et al. 2019). Multicollinearity was diagnosed by variance inflation factor (VIF) and all VIF values were lower than 10 in the best-fit regression model, which suggested our results were not affected by collinearity among predictor variables (Graham 2003). Detailed statistics of all models are provided in Annex 2 of Additional file 2, respectively. The 'glmulti' package in R 3.6.1 was used to select models (Calcagno 2013).

Correlations between aboveground C storage and influencing factors
Aboveground C storage was most strongly affected by DBH (R 2 = 0.413, P < 0.001) and height (R 2 = 0.207, P < 0.001) diversity ( Figure. 1). The CWM nitrogen concentration of young leaves negatively affected aboveground C storage (R 2 = 0.08, P = 0.034). Aboveground C storage decreased with FDvar of LDMC and SLA in both young and mature leaves. The FDvar of phosphorus concentration in young leaves (R 2 = 0.072, P = 0.045) and elevation gradients (R 2 = 0.067, P = 0.03) also negatively affected aboveground C storage.
The relative importance of all predictor variables for aboveground C storage Only 4 predictor variables were retained in the optimal multiple regression model, account for 57.21% of the variation in aboveground C storage (Table 1). DBH diversity still most importantly affected aboveground C storage (P < 0.001, β = 0.47). The FDvar of leaf dry matter content in mature leaves (P = 0.009, β = -0.27) and CWM nitrogen concentrations of young leaves (P = 0.011, β = -0.25) both negatively affected aboveground C storage significantly. The height diversity didn't significantly affect the aboveground C storage in the final multiple regression model (P = 0.081, β = -0.18). Table 1 The optimal model resulted from a sequence of regression analyses of aboveground carbon storage.

Model and predictor
Coeff. Beta t P R 2 AICc

The direct and indirect impacts on aboveground C storage
The optimal SEM accounted for 53, 13, 5, 2 and 1% of the variation in aboveground C storage, DBH diversity, height diversity, FDvar of LDMC in mature leaves and CWM nitrogen concentration in young leaves (Figure. 2). The DBH diversity directly enhanced aboveground C storage significantly (P = 0.002, β = 0.48), whereas CWM young leaves' nitrogen concentration (P = 0.009, β = -0.27) and FDvar of mature leaves' dry matter content (P = 0.034, β = -0.27) both had significant negative direct effect ( Figure. 2, Table 2). Elevation negatively affected DBH diversity directly (P < 0.001, β = -0.37) and didn't affect aboveground C storage directly (P = 0.906, β = -0.02). However, elevation had a significant negative indirect effect via DBH diversity (P = 0.002, β = -0.18). The total effect of elevation was significant (P = 0.044, β = -0.30). Note: The indirect effect of elevation was calculated by multiplying the standardized effects of all paths on one route, from elevation to mediator, and then to aboveground carbon storage, while the total effect was calculated by adding standardized direct and indirect effects.

Discussion
Biodiversity promoting ecosystem functioning has long been debated during last two decades (Forrester and Bauhus. 2016). Inconsistent with the hypothesis, no correlation between aboveground C storage and species diversity was found, which may be caused by a potent effect of the dominant productive species Unexpected, significant negative associations between FDvar of leaf functional traits and aboveground C storage were found, which was not consistent with the niche complementarity hypothesis and some previous results (Ali et al. 2016). Functional diversity is positively related to species diversity (Annex 1 of Additional file 2) and low trait diversity maybe caused by the large dominated species ( We found the stand structure diversity was the most important driver of aboveground C storage, followed by CWM LNC and FDvar of leaf traits. This result indicates niche complementarity may have a more important effect on promoting ecosystem functioning than mass-ratio effect. Both consideration the niche complementarity and mass-ratio effect simultaneously can better explain the mechanisms of ecosystem function (Table 1)

Conclusions
The date from 56 old-growth forest plots indicate both niche complementarity and mass-ratio effect are essential in promoting ecosystem functioning. The stand structural diversity strongly enhanced the aboveground C storage, but not the species diversity or functional diversity. The species dominated by leaf nitrogen conservative strategy drive the aboveground C storage. Elevation has a significant filtering effect on DBH diversity and CWM leaf functional traits. The elevation indirectly affected the aboveground C storage via DBH diversity. Our results indicated complex stand structure and species with leaf nitrogen conservative use strategy can maximize aboveground C storage in tropical old-growth forests.
Declarations as well as for pairwise correlations between all independent variables. Annex 2 Results for the Model Selection procedure, based on Akaike Information Criterion (AICc).

Figure 2
The optimal structural equation model relating aboveground carbon storage to predictor variables. Model-fit statistics and standardized regression coefficient of each path are shown in the figure. Significant routes and