Decreased Temperature with Increasing Elevation Decreases the End-Season Leaf-to-Wood Reallocation of Resources in Deciduous Betula ermanii Cham. Trees

In situ 13CO2 labeling reveals that alpine treeline trees allocate less photoassimilates to roots compared with low-elevation trees

Carbon (C) allocation plays a crucial role for survival and growth of alpine treeline trees, however it is still poorly understood. Using in situ 13CO2 labeling, we investigated the leaf photosynthesis and the allocation of 13C labeled photoassimilates in various tissues (leaves, twigs and fine roots) in treeline trees and low-elevation trees. Non-structural carbohydrate concentrations were also determined. The alpine treeline trees (2000 m. a.s.l.), compared with low-elevation trees (1700 m a.s.l.), did not show any disadvantage in photosynthesis, but the former allocated proportionally less newly assimilated C belowground than the latter. Carbon residence time in leaves was longer in treeline trees (19 days) than that in low-elevation ones (10 days). We found an overall lower density of newly assimilated C in treeline trees. The alpine treeline trees may have a photosynthetic compensatory mechanism to counteract the negative effects of the harsh treeline environment (e.g., lower temperature and shorter growing season) on C gain. Lower temperature at treeline may limit the sink activity and C downward transport via phloem, and shorter treeline growing season may result in early cessation of root growth, decreases sink strength, which all together lead to lower density of new C in the sink tissues and finally limit the growth of the alpine treeline trees.

Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region

Accurate and effective mapping of forest aboveground biomass (AGB) in heterogeneous mountainous regions is a huge challenge but an urgent demand for resource managements and carbon storage monitoring. Conventional studies have related the plot-measured or LiDAR-based biomass to remote sensing data using pixel-based approaches. The object-based relationship between AGB and multi-source data from LiDAR, multi-frequency radar, and optical sensors were insufficiently studied. It deserves the further exploration that maps forest AGB using the object-based approach and combines LiDAR data with multi-sensor images, which has the smaller uncertainty of positional discrepancy and local heterogeneity, in heterogeneous mountainous regions. To address the improvement of mapping accuracy, satellite LiDAR data from GEDI and ICEsat-2, and images of ALOS-2 yearly mosaic L band SAR (Synthetic Aperture Radar), Sentinel-1 C band SAR, Sentinel-2 MSI, and ALOS-1 DSM were combined for pixel- and object-based forest AGB mapping in a vital heterogeneous mountainous forest. For the object-based approach, optimized objects during a multiresolution segmentation were acquired by the ESP (Estimation of the Scale Parameter) tool, and suitable predictors were selected using an algorithm named VSURF (Variable Selection Using Random Forests). The LiDAR variables at the footprint-level were extracted to connect field plots to the multi-sensor objects as a linear bridge. It was shown that forests’ AGB values varied by elevations with a mean value of 142.58 Mg/ha, ranging from 12.61 to 514.28 Mg/ha. The north slope with the lowest elevation (<1100 m) had the largest mean AGB, while the smallest mean AGB was located in the south slope with the altitude above 2000 m. Using independent validation samples, it was indicated by the accuracy comparison that the object-based approach performed better on the precision with relative improvement based on root-mean-square errors (RIRMSE) of 4.46%. The object-based approach also selected more optimized predictors and markedly decreased the prediction time than the pixel-based analysis. Canopy cover and height explained forest AGB with their effects on biomass varying according to the elevation. The elevation from DSM and variables involved in red-edge bands from MSI were the most contributive predictors in heterogeneous temperate forests. This study is a pioneering exploration of object-based AGB mapping by combining satellite data from LiDAR, MSI, and SAR, which offers an improved methodology for regional carbon mapping in the heterogeneous mountainous forests.

Enhancement of ecosystem carbon uptake in a dry shrubland under moderate warming: The role of nitrogen-driven changes in plant morphology

Net ecosystem CO₂ exchange is the result of net carbon uptake by plant photosynthesis and carbon loss by soil and plant respiration. Temperature increases due to climate change can modify the equilibrium between these fluxes and trigger ecosystem-climate feedbacks that can accelerate climate warming. As these dynamics have not been well studied in dry shrublands, we subjected a Mediterranean shrubland to a 10-year night-time temperature manipulation experiment that analyzed ecosystem carbon fluxes associated with dominant shrub species, together with several plant parameters related to leaf photosynthesis, leaf morphology, and canopy structure. Under moderate night-time warming (+0.9°C minimum daily temperature, no significant reduction in soil moisture), Cistus monspeliensis formed shoots with more leaves that were relatively larger and denser canopies that supported higher plant-level photosynthesis rates. Given that ecosystem respiration was not affected, this change in canopy morphology led to a significant enhancement in net ecosystem exchange (+47% at midday). The observed changes in shoot and canopy morphology were attributed to the improved nutritional state of the warmed plants, primarily due to changes in nitrogen cycling and higher nitrogen resorption efficiency in senescent leaves. Our results show that modifications in plant morphology triggered by moderate warming affected ecosystem CO₂  fluxes, providing the first evidence for enhanced daytime carbon uptake in a dry shrubland ecosystem under experimental warming.

Saprotrophic fungal diversity predicts ectomycorrhizal fungal diversity along the timberline in the framework of island biogeography theory

Island biogeography theory (IBT) is one of the most fruitful paradigms in macroecology, positing positive species-area and negative species-isolation relationships for the distribution of organisms. Biotic interactions are also crucial for diversity maintenance on islands. In the context of a timberline tree species (Betula ermanii) as "virtual island", we surveyed ectomycorrhizal (EcM) fungal diversity along a 430-m vertical gradient on the top of Changbai Mountain, China, sampling fine roots and neighboring soils of B. ermanii. Besides elevation, soil properties and plant functional traits, endophytic and saprotrophic fungal diversity were assessed as candidate predictors to construct integrative models. EcM fungal diversity decreased with increasing elevation, and exhibited positive diversity to diameter at breast height and negative diversity to distance from forest edge relationships in both roots and soils. Integrative models further showed that saprotrophic fungal diversity was the strongest predictor of EcM fungal diversity, directly enhancing EcM fungal diversity in roots and soils. Our study supports IBT as a basic framework to explain EcM fungal diversity. The diversity-begets-diversity hypothesis within the fungal kingdom is more predictive for EcM fungal diversity within the IBT framework, which reveals a tight association between saprotrophic and EcM fungal lineages in the timberline ecosystem.

Non-Structural Carbohydrate Storage Strategy Explains the Spatial Distribution of Treeline Species

Environmental factors that drive carbon storage are often used as an explanation for alpine treeline formation. However, different tree species respond differently to environmental changes, which challenges our understanding of treeline formation and shifts. Therefore, we selected Picea jezoensis and Betula ermanii, the two treeline species naturally occurring in Changbai Mountain in China, and measured the concentration of non-structural carbohydrates (NSC), soluble sugars and starch in one-year-old leaves, shoots, stems and fine roots at different elevations. We found that compared with P. jezoensis, the NSC and soluble sugars concentrations of leaves and shoots of B. ermanii were higher than those of P. jezoensis, while the starch concentration of all the tissues were lower. Moreover, the concentration of NSC, soluble sugars and starch in the leaves of B. ermanii decreased with elevation. In addition, the starch concentration of B. ermanii shoots, stems and fine roots remained at a high level regardless of whether the soluble sugars concentration decreased. Whereas the concentrations of soluble sugars and starch in one-year-old leaves, shoots and stems of P. jezoensis responded similarly changes with elevation. These findings demonstrate that compared with P. jezoensis, B. ermanii has a higher soluble sugars/starch ratio, and its shoots, stems and fine roots actively store NSC to adapt to the harsh environment, which is one of the reasons that B. ermanii can be distributed at higher altitudes.

Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve

Forest condition is the baseline information for ecological evaluation and management. The National Forest Inventory of China contains structural parameters, such as canopy closure, stand density and forest age, and functional parameters, such as stand volume and soil fertility. Conventionally forest conditions are assessed through parameters collected from field observations, which could be costly and spatially limited. It is crucial to develop modeling approaches in mapping forest assessment parameters from satellite remote sensing. This study mapped structure and function parameters for forest condition assessment in the Changbai Mountain National Nature Reserve (CMNNR). The mapping algorithms, including statistical regression, random forests, and random forest kriging, were employed with predictors from Advanced Land Observing Satellite (ALOS)-2, Sentinel-1, Sentinel-2 satellite sensors, digital surface model of ALOS, and 1803 field sampled forest plots. Combined predicted parameters and weights from principal component analysis, forest conditions were assessed. The models explained spatial dynamics and characteristics of forest parameters based on an independent validation with all r values above 0.75. The root mean square error (RMSE) values of canopy closure, stand density, stand volume, forest age and soil fertility were 4.6%, 33.8%, 29.4%, 20.5%, and 14.3%, respectively. The mean assessment score suggested that forest conditions in the CMNNR are mainly resulted from spatial variations of function parameters such as stand volume and soil fertility. This study provides a methodology on forest condition assessment at regional scales, as well as the up-to-date information for the forest ecosystem in the CMNNR.