Modeling biomass allocation strategy of young planted Zelkova serrata trees in Taiwan with component ratio method and seemingly unrelated regressions.

Trees accumulate biomass by sequestrating atmospheric carbon and allocate it to different tree components. A biomass component ratio is the ratio of biomass in a tree component to total tree biomass. Modeling the ratios for Zelkova serrata, an important native reforestation tree species in Taiwan, helps in understanding its biomass allocation strategy to design effective silvicultural treatments. In this study, we applied Component Ratio Method (CRM) to relate biomass component ratios of main stem, large branch, twig, and foliage to tree attributes of Z. serrata from a 9-year-old plantation. Nonlinear and linear CRM models were fitted with Seemingly Unrelated Regression to account for model correlations. Linear CRM models with dbh as the predictor had the best fit with model correlations as high as 80%. About 46% and 40% of total tree biomass was allocated to main stem and large branch, respectively. However, main stem biomass decreased by 1.9% with every 1-cm increase in dbh, but large branch biomass increased by 2.2% instead. Results suggest that dominant Z. serrata trees tend to branch and fork, while smaller trees invest in larger main stem. An early pruning treatment should focus on dominant trees to maintain crown ratio and ensure wood quality.

Double sampling for stratification for the monitoring of sparse tree populations: the example of Populus euphratica Oliv. forests at the lower reaches of Tarim River, Southern Xinjiang, China.

Desertification is a pressing issue in the dry Tarim River basin, which is under anthropogenic stresses. In this study, double sampling for stratification (DSS) is employed to inventory Populus euphratica Oliv. forests in the lower reaches of the Tarim River Basin in Xinjiang, China. The two objectives were evaluating DSS as a sampling technique for monitoring desertification and generating baseline information for permanent observation. Here, DSS consists of two phases: in phase 1, crown cover is observed on a large sample of plots on a high resolution satellite image, and these photo-plots are stratified into five crown cover strata. Phase 2 is a stratified random sample from these photo-plots and the sampled plots are field observed. Approximately 32% of the study area is without P. euphratica trees. As expected, estimated mean poplar tree density and basal area increase with crown cover. DSS takes advantages of stratification (fieldwork efficiency and statistical precision) without the need for a priori strata delineation. It proves feasible for inventory the sparse poplar population and holds promise for the assessment of trees outside the forest, where density varies considerably and pre-stratification is intractable. It can be integrated into permanent observation systems for monitoring vegetation changes.