Tree functional types simplify forest carbon stock estimates induced by carbon concentration variations among species in a subtropical area

Untangling the impact of plantation type and functional traits on ecosystem nutrient stocks in an experimentally restored forest ecosystem

The primary objective of ecological restoration is recovering biodiversity and ecosystem functioning. While a functional trait-based approach can help understand community assembly and ecosystem function recovery during ecological restoration, there still exists a knowledge gap in assessing how functional traits indicate the mediating roles of the plant community in response to forest restoration effects on ecosystem functions. This study applied the "response-effect trait" framework to investigate experimentally whether the treatment of plantation type has an impact on community trait compositions, which in turn could affect forest ecosystem nutrient stocks - here, carbon (C) and nitrogen (N) and phosphorus (P) stocks in tree, understory, litter and soil pools at an experimental station in subtropical China. We used structural equation models (SEMs) to examine the relationships among plantation type, community weighted mean of traits, and nutrient stocks in each pool. Our results show that most of the tree and understory traits studied were response traits to plantation type. Moreover, certain traits played a significant role in mediating plantation-type effects on C, N and P stocks for understory pool (e.g., understory stem specific density and specific leaf area, tree leaf phosphorus content), and for litter and soil pools (e.g., tree leaf carbon or phosphorus content, understory specific leaf area, leaf nitrogen or phosphorus content), known as "response-effect traits". For the tree pool, only effect traits, and no "response-effect" tree traits, were found for the N stock. Total effects of SEMs indicated that, understory or tree traits can have a greater impact than plantation type on understory or litter C, N or P stocks. After approximately 35 years of natural restoration, exotic plantations exhibited a different community trait characteristic from native plantations. The important roles of traits in mediating the effects of plantation type on non-tree pool C, N and P stocks were highlighted.

A global database of woody tissue carbon concentrations

Woody tissue carbon (C) concentration is a key wood trait necessary for accurately estimating forest C stocks and fluxes, which also varies widely across species and biomes. However, coarse approximations of woody tissue C (e.g., 50%) remain commonplace in forest C estimation and reporting protocols, despite leading to substantial errors in forest C estimates. Here, we describe the Global Woody Tissue Carbon Concentration Database (GLOWCAD): a database containing 3,676 individual records of woody tissue C concentrations from 864 tree species. Woody tissue C concentration data—i.e., the mass of C per unit dry mass—were obtained from live and dead woody tissues from 130 peer-reviewed sources published between 1980–2020. Auxiliary data for each observation include tissue type, as well as decay class and size characteristics for dead wood. In GLOWCAD, 1,242 data points are associated with geographic coordinates, and are therefore presented alongside 46 standardized bioclimatic variables extracted from climate databases. GLOWCAD represents the largest available woody tissue C concentration database, and informs studies on forest C estimation, as well as analyses evaluating the extent, causes, and consequences of inter- and intraspecific variation in wood chemical traits.

Measurement(s)	wood carbon concentrations
Technology Type(s)	elemental analyzer
Factor Type(s)	species
Sample Characteristic - Organism	Plant
Sample Characteristic - Environment	terrestrial biome
Sample Characteristic - Location	Globe

Urban tree carbon density and CO2 equivalent of National Zoological Park, Delhi

In a highly urbanized city like Delhi, the urban forest plays a vital role in climate change mitigation by capturing and storing carbon dioxide (CO₂) from the atmosphere. Urban vegetation helps in increasing carbon sink and CO₂ equivalent (CO₂eq) and also provides other aesthetic and psychological environmental benefits. To understand how urban trees are vital for carbon sink, the present study aimed to quantify the carbon density and CO₂eq in trees at National Zoological Park (NZP), New Delhi, a tropical semi-arid region of India. For this, we estimated tree biomass or dry matter content of 25 species with the help of allometric equations which are available in published literature and applicable for the tropical region. It was observed that the highest diameter at breast height (DBH) was contributed by Ficus sp. while the maximum density among adult tree species found in Albizia procera. The total mean dry matter content, C density, and CO₂eq of NZP were 92.10 Mg ha^-1, 43.61 Mg-C ha^-1, and 168.83 Mg ha^-1, respectively. The highest biomass, C density, and CO₂eq obtained in the species of Ficus benghalensis followed by Ficus racemosa and Azadirachta indica. The data indicates that the trees having the capacity to store carbon are essential for the maintenance of a sustainable environment. Thus, the study suggests that there is a substantial scope to increase the carbon density and CO_2eq in urban city through adopting various management strategies viz. afforestation and reforestation program on degraded and abandoned land to maintain a clean and sustainable environment.

Convergent Variations in the Leaf Traits of Desert Plants

Convergence is commonly caused by environmental filtering, severe climatic conditions and local disturbance. The basic aim of the present study was to understand the pattern of leaf traits across diverse desert plant species in a common garden, in addition to determining the effect of plant life forms (PLF), such as herb, shrub and subshrub, phylogeny and soil properties on leaf traits. Six leaf traits, namely carbon (C), nitrogen (N), phosphorus (P), potassium (K), δ13C and leaf water potential (LWP) of 37 dominant desert plant species were investigated and analyzed. The C, N, K and δ13C concentrations in leaves of shrubs were found higher than herbs and subshrubs; however, P and LWP levels were higher in the leaves of subshrubs following herbs and shrubs. Moreover, leaf C showed a significant positive correlation with N and a negative correlation with δ13C. Leaf N exhibited a positive correlation with P. The relationship between soil and plant macro-elements was found generally insignificant but soil C and N exhibited a significant positive correlation with leaf P. Taxonomy showed a stronger effect on leaf C, N, P and δ13C than soil properties, explaining >50% of the total variability. C3 plants showed higher leaf C, N, P, K and LWP concentration than C4 plants, whereas C4 plants had higher δ13C than C3 plants. Legumes exhibited higher leaf C, N, K and LWP than nonlegumes, while nonlegumes had higher P and δ13C concentration than legumes. In all the species, significant phylogenetic signals (PS) were detected for C and N and nonsignificant PS for the rest of the leaf traits. In addition, these phylogenetic signals were found lower (K-value < 1), and the maximum K-value was noted for C (K = 0.35). The plants of common garden evolved and adapted themselves for their survival in the arid environment and showed convergent variations in their leaf traits. However, these variations were not phylogenetics-specific. Furthermore, marks of convergence found in leaf traits of the study area were most likely due to the environmental factors.

Aggregated Biomass Model Systems and Carbon Concentration Variations for Tree Carbon Quantification of Natural Mongolian Oak in Northeast China

Three systems of additive biomass models were developed and the effects of tree components, tree sizes, and tree growing regions on the carbon concentration were analyzed for Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) in the natural forests of Northeastern China. The nonlinear seemingly unrelated regression (NSUR) method was used to fit each of the three systems simultaneously; namely, aggregated model systems with no parameter restriction (AMS0), aggregated model systems with one parameter restriction (AMS1), and aggregated model systems with three parameter restrictions (AMS3). A unique weighting function for each biomass model was applied to address the heteroscedasticity issue. The systems assertively guarantee the additivity property, in which, the summation of the respective predicted tree components (i.e., root, stem, branch, and foliage) will match the prediction of subtotals (i.e., crown and aboveground) and total biomass. Using one-, two-, and three-predictor combinations (i.e., D (diameter at breast height), D and H (total height), and D, H, and CL (crown length)) as the general model underlying formats, three systems of additive biomass model were developed. Our results indicate that (1) all of the aggregated model systems performed well and the differences between the systems were relatively small; (2) the rank order of the three systems based on an array of statistics are as follows: AMS0 > AMS1 > AMS3; (3) the carbon concentration significantly varied depending on the types of tree tissues and growing regions; (4) the regional respective component carbon concentration and regional weighted mean carbon concentration multiplied by observed biomass value appeared to be the best approach to calculate carbon stock.