Seasonality Affects Low-Molecular-Weight Organic Acids and Phenolic Compounds' Composition in Scots Pine Litterfall.

BACKGROUND AND AIMS: Secondary plant metabolites, including organic acids and phenolic compounds, have a significant impact on the properties of organic matter in soil, influencing its structure and function. How the production of these compounds in foliage that falls to the forest floor as litterfall varies across tree age and seasonality are of considerable interest for advancing our understanding of organic matter dynamics. METHODS: Monthly, we collected fallen needles of Scots pine (Pinus sylvestris L.) across stands of five different age classes (20, 40, 60, 80, and 100 years) for one year and measured the organic acids and phenolic compounds. RESULTS: Seven low-molecular-weight organic acids and thirteen phenolic compounds were detected in the litterfall. No differences were observed across stand age. Significant seasonal differences were detected. Most compounds peaked during litterfall in the growing season. Succinic acid was the most prevalent organic acid in the litterfall, comprising 78% of total organic acids (351.27 ± 34.27 µg g- 1), and was 1.5 to 11.0 times greater in the summer than all other seasons. Sinapic acid was the most prevalent phenolic compound in the litterfall (42.15 µg g- 1), representing 11% of the total phenolic compounds, and was 39.8 times greater in spring and summer compared to autumn and winter. Growing season peaks in needle concentrations were observed for all thirteen phenolic compounds and two organic acids (lactic, succinic). Citric acid exhibited a definitive peak in late winter into early spring. CONCLUSIONS: Our results highlight the seasonal dynamics of the composition of secondary plant metabolites in litterfall, which is most different at the onset of the growing season. Fresh inputs of litterfall at this time of emerging biological activity likely have seasonal impacts on soil's organic matter composition as well.

Using hyperspectral leaf reflectance to estimate photosynthetic capacity and nitrogen content across eastern cottonwood and hybrid poplar taxa.

Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.

Indirect effects of bark beetle-generated dead wood on biogeochemical and decomposition processes in a pine forest.

Bark beetle outbreaks are increasing in frequency and intensity, generating massive inventories of dead trees globally. During attacks, trees are pre-inoculated with ophiostomatoid fungi via bark beetles, which has been shown to increase termite presence and feeding. These events may, in turn, alter biogeochemical cycles during decomposition. We examined these relationships by experimentally inoculating dead wood with bluestain fungi in a temperate pine forest. Across ten replicate plots, eight 0.5 m-long logs were inoculated with Ophiostoma minus and eight with distilled water. Half of the logs from each inoculation treatment were covered from above with a mesh cage barrier to exclude aboveground beetles while permitting access by belowground decomposers. After 1 year, significant increases in mass (34%) and decreases in moisture content (- 17%) were observed across all treatments, but no consistent changes in density were evident. C concentrations were 12% greater in bark when barriers were present and 17% greater in sapwood when barriers and inoculation fungi were absent. N concentrations were 16% greater in bark for fungal-inoculated logs and 27% greater when barriers were present. C:N ratios in A horizon soils under fungal-inoculated logs were 12% greater. Furthermore, termites were present fourfold more in fungal-inoculated logs than controls and the presence of termites was associated with 6% less C in sapwood and 11% more N in both sapwood and heartwood. Together these results suggest dead wood generated via bark beetle attacks has different biogeochemical responses during initial decomposition phases, which could have implications for the C status in forests following bark beetle outbreaks.