Although the understory vegetation abundance, diversity, and composition associated with stand development in natural forests have been well reported, only a few studies have examined the age-related changes of understory vegetation in fast-growing planted forests in reclaimed soils. This study measured the understory vegetation and soil variables in 8-, 12-, and 18-year-old poplar plantations in reclaimed coastal saline soil of Eastern China. This study examined how the abundance, diversity, and composition changed with stand development and the soil variables. Further, structural equation modeling (SEM) was used to evaluate the direct and indirect factors influencing the abundance and plant diversity throughout stand development. Herb abundance was significantly higher in the youngest and oldest stands, whereas shrub abundance was higher in the middle-aged stands. Shannon’s diversity index was significantly higher in the youngest stand for herbs, whereas it was highest in the middle-aged stands for shrubs. A multivariate analysis revealed that the herb and shrub composition were influenced by the stand age, total soil carbon and soil pH. The most parsimonious SEM model showed the negative direct effects of the stand age and the negative indirect effects of the stand age via the soil variables on shrub abundance, shrub diversity, and herb diversity, suggesting that the increase of overstory biomass with the stand age reduces resources available for the understory. Our results revealed that understory diversity and composition might change with stand development mediated by the changes in understory light and soil resources in fast-growing plantations.

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Silvicultural practices applied in managed forest plantations may help counteract the effects of climate change by influencing soil surface CO2 efflux (Fs). Understanding the effects of silvicultural practices on Fs will provide unbiased estimates of carbon fluxes and allow better silvicultural decisions for carbon sequestration. Therefore, we assessed how Fs differed seasonally across silvicultural practices (i.e., stocking levels, clone, fertilization and weed control treatments) and evaluated the effects of soil temperature (Ts) and soil volumetric water content (θv) on Fs across these practices for a mid-rotation (14 year-old) Pinus radiata plantation in the Canterbury region of New Zealand. There were significant differences in Fs (p < 0.05) over the four seasons, three levels of stocking, and five clones. The effects of fertilization and weed control applied 12 years previously on Fs were insignificant. Annual estimate of Fs (mean ± 1 standard deviation) from the study site was 22.7 ± 7.1 t ha−1 a−1 in the form of CO2 (6.2 ± 2.1 t ha−1 a−1 in the form of C). Fs values were consistently higher in plots with 1250 stems ha−1 compared to 2500 stems ha−1, which may be related to a strong soil resource limitation because of the close spacing in the latter plantation. Significant differences in Fs across clones suggest that variations in carbon partitioning might explain their growth performance. Silvicultural treatments influenced Fs response to soil temperature (p < 0.05), resulting in models explaining 28–49% of the total variance in Fs. These findings provide insights into how silvicultural management decisions may impact Fs in mid-rotation radiata pine plantations, contributing towards developing more precise and unbiased plantation carbon budgets.

Background: Additivity has long been recognised as a desirable property of systems of equations to predict the biomass of components and the whole tree. However, most tree biomass studies report biomass equations fitted using traditional ordinary least-squares regression. Therefore, we aimed to develop models to estimate components, subtotals and above-ground total biomass for a Pinus radiata D.Don biomass dataset using traditional linear and nonlinear ordinary leastsquares regressions, and to contrast these equations with the additive procedures of biomass estimation.Methods: A total of 24 ten-year-old trees were felled to assess above-ground biomass. Two broad procedures were implemented for biomass modelling: (a) independent; and (b) additive. For the independent procedure, traditional linear models (LINOLS) with scaled power transformations and y-intercepts and nonlinear power models (NLINOLS) without y-intercepts were compared. The best linear (transformed) models from the independent procedure were further tested in three different additive structures (LINADD1, LINADD2, and LINADD3). All models were evaluated using goodness-of-fit statistics, standard errors of estimates, and residual plots.Results: The LINOLS with scaled power transformations and y-intercepts performed better for all components, subtotals and total above-ground biomass in contrast to NLINOLS that lacked y-intercepts. The additive model (LINADD3) in a joint generalised linear least-squares regression, also called seemingly unrelated regression (SUR), provided the best goodness-of-fit statistics and residual plots for four out of six components (stem, branch, new foliage and old foliage), two out of three subtotals (foliage and crown), and above-ground total biomass compared to other methods. However, bark, cone and bole biomass were better predicted by the LINOLS method.Conclusions: SUR was the best method to predict biomass for the 24-tree dataset because it provided the best goodness-of-fit statistics with unbiased estimates for 7 out of 10 biomass components. This study may assist silviculturists and forest managers to overcome one of the main problems when using biomass equations fitted independently for each tree component, which is that the sum of the biomasses of the predicted tree components does not necessarily add to the total biomass, as the additive biomass models do.