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Abstract Maintaining both the structure and functionality of forest ecosystems is a primary goal of forest management. In this study, relationships between structural diversity and aboveground stand carbon (C) stocks were examined in spruce-dominated forests in New Brunswick, Canada. Tree species, size, and height diversity indices as well as a combination of these diversity indices were used to correlate aboveground C stocks. Multiple linear regressions were subsequently used to quantify the relationships between these indices and aboveground C stocks, and partial correlation analysis was also adopted to remove the effects of other explanatory variables. Results show that stand structural diversity has a significant positive effect on aboveground C stocks even though the relationship is weak overall. Positive relationships observed between the diversity indices and aboveground C stocks support the hypothesis that increased structural diversity enhances aboveground C storage capacity. This occurs because complex forest structures allow for greater light infiltration and promote a more efficient resource use by trees, leading to an increase in biomass and C production. Mixed tolerant species composition and uneven-aged stand management in conjunction with selection or partial cutting to maintain high structural diversity is therefore recommended to preserve biodiversity and C stocks in spruce-dominated forests.
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Abstract Applying allometric equations in combination with forest inventory data is an effective approach to use when qualifying forest biomass and carbon storage on a regional scale. The objectives of this study were to (1) develop general allometric tree component biomass equations and (2) investigate tree biomass allocation patterns for Pinus massoniana , a principal tree species native to southern China, by applying 197 samples across 20 site locations. The additive allometric equations utilized to compute stem, branch, needle, root, aboveground, and total tree biomass were developed by nonlinear seemingly unrelated regression. Results show that the relative proportion of stem biomass to tree biomass increased while the contribution of canopy biomass to tree biomass decreased as trees continued to grow through time. Total root biomass was a large biomass pool in itself, and its relative proportion to tree biomass exhibited a slight increase with tree growth. Although equations employing stem diameter at breast height (dbh) alone as a predictor could accurately predict stem, aboveground, root, and total tree biomass, they were poorly fitted to predict the canopy biomass component. The inclusion of the tree height ( H ) variable either slightly improved or did not in any way increase model fitness. Validation results demonstrate that these equations are suitable to estimate stem, aboveground, and total tree biomass across a broad range of P . massoniana stands on a regional scale.
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