Broadleaf Forest Research Articles

Invasion risk of the currently cultivated alien flora in southern Africa is predicted to decline under climate change

Alien species can have massive impacts on native biodiversity, ecosystem functioning, and human livelihoods. Assessing which species from currently cultivated alien floras may escape into the wild and naturalize is essential for efficient and proactive ecosystem management and biodiversity conservation. Climate change has already promoted the naturalization of many alien plants in temperate regions, but whether it is similar in (sub)tropical areas is insufficiently known. In this study, we used species distribution models for 1527 cultivated alien plants to evaluate current and future invasion risks across different biomes and 10 countries in southern Africa. Our results confirm that the area of suitable climate is a strong predictor of naturalization success among the cultivated alien flora. In contrast to previous findings from temperate regions, however, climatic suitability is generally predicted to decrease for potential aliens across our (sub)tropical study region. While increasingly hotter and drier conditions are likely to drive declines in suitability for potential aliens across most biomes of southern Africa, in some the number of potential invaders is predicted to increase under moderate climate change scenarios (e.g. in dry broadleaf forests and flooded grasslands). We found that climatic suitability is expected to decline less for aliens originating from continents with the tropical biome or from the Southern Hemisphere. In addition, we found that the climatically suitable area will decline less for aliens that have already naturalized in the region. While the number of potential invaders may decrease across southern Africa under future climate change, our results suggest that already naturalized aliens will continue to threaten native species and ecosystems.

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).

Global mapping of fractional tree cover for forest cover change analysis

Fractional tree cover facilitates the characterization of forest cover changes using satellite data. However, there are still substantial challenges in generating fractional tree cover datasets that satisfy the requirements of interannual stability for forest cover change monitoring. In this study, a global annual fractional tree cover dataset, named as GLOBMAP Fractional Tree Cover, was generated from MODIS observations with a resolution of 250 m during the period of 2000–2022. The tree cover estimation was improved relative to conventional global tree cover mapping methods by developing highly discriminative input features and near global sampling training data. MODIS annual observations were realigned at the pixel level to eliminate phenological differences among regions. The realigned annual series were condensed into twelve features showing high separability between trees and herbaceous vegetation to reduce the dimension of features. A global covering training dataset, comprising 465.88 million sample points, was extracted across the globe through the aggregation and combination of forest/land cover maps from ESA WorldCover, GlobeLand30, PALSAR FNF, and ESRI Land Cover to improve the representativeness of training data. A feed-forward neural network was calibrated to predict fractional tree cover from MODIS data. The spatial pattern of the estimation results was generally consistent with the CGLS-LC100 product at global scale, with the average MAE in global vegetated areas of 12.50 %, while our dataset provided extended temporal coverage. The interannual stability of the estimated tree cover series was improved compared to MODIS vegetation continuous fields products for deciduous broadleaf forests, evergreen broadleaf forests, and mixed forests, with the global average value of mean absolute deviation (MAD) in tree cover series reduced by 5.8 %, 46.9 %, and 18.3 %, respectively. The estimation results were assessed using globally distributed validation data around BELMANIP 2.1 sites and those derived from the USGS circa 2010 global land cover reference dataset, leading to the R2 values of 0.93 and 0.73, MAE of 4.24 % and 10.55 %, and RMSE of 11.45 % and 17.98 %, respectively. This dataset could enhance the capability to monitor forest cover changes, particularly gradual changes such as forest recovery.

Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China

Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis.

Net Primary Production of Ecoregions Across North America in Response to Drought and Wildfires From 2015 to 2022

AbstractEcosystem models are valuable tools to make climate‐related assessments of change when ground‐based measurements of water and carbon fluxes are not adequate to realistically capture regional variability. The Carnegie‐Ames‐Stanford Approach (CASA) is one such model based on satellite observations of monthly vegetation cover to estimate net primary production (NPP) of terrestrial ecosystems. CASA model predictions from 2015 to 2022 revealed several notable high and low periods in growing season NPP totals in certain biomes. Both Temperate Broadleaf and Boreal Forest production shifted from relatively high average NPP values in 2015 through 2019 to lower levels in 2020, typically representing a loss of 10%–14% of growing season NPP flux. This rapid decline in growing season NPP from 2019 to 2020–2021 was also estimated for the Temperate Grasslands and Savanna, Temperate Conifer Forest, and Tundra biomes. In contrast to the climate patterns in the temperate biomes that developed into severe widespread drought in 2020 and 2021 due to low precipitation totals and extreme hot temperatures, growing season NPP in the Tundra biome was depressed in these same years by colder temperature induced drought conditions at the high latitudes of North America. Drought severity classes were closely associated with different levels of decline in NPP in most biomes. Trends in NPP in areas of the largest wildfires in North America that burned between 2012 and 2021 were examined to assess recovery of vegetation and the resiliency of ecosystems during extreme drought periods.

Maize Cultivation Three Hundred Years Ago Triggered Severe Rocky Desertification in Southwest China

AbstractUnderstanding the forest evolution is vital to answering the reforestation potential in karst areas. Here, we present the first‐ever pollen record in karst depression sediment, combined with comprehensive dating methods (137Cs, 210Pbex, and 14C) and historical documents, to reveal plant change history in southwest Guangxi, a severe rocky‐desertification region. We inferred three stages of “virgin forest–deforestation–sparse tree planting” over the past three centuries. Before the 1780s, the barren mountains used to be a lush mixed broadleaf forest probably. However, maize cultivation, along with explosive population growth and migration, accelerated mountain reclamation and deforestation, leading to severe rocky desertification since the 1780s, featured by the co‐occurrence of Zea pollen appearance and Dicranopteris spore surge from 0.92% to 12.18%. Since the 1930s, sparse tree planting began, as Cupressaceae/Taxodiaceae pollen abruptly increased by 32%. Our study is significant in understanding the rocky desertification causes and guiding ecological restoration in the region.

2021–2022年广东南岭站日尺度气象数据集

The mountainous forest and biodiversity ecological functional area of the Nanling Mountain forest ecosystem is one of the 25 key ecological functional areas in China, covering a variety of forest vegetation types, including tropical and subtropical moist broadleaf forests, coniferous broad-leaved evergreen mixed forest, and mountain top evergreen broad-leaved dwarf forest. The tropical and subtropical moist broadleaf forests are the most typical vegetation type in the Nanling Mountains. Monitoring the meteorological elements in the community can help understand the response mechanism of the tropical and subtropical moist broadleaf forests in the Nanling Mountains to climate change. Based on the Nanling Forest Ecosystem National Field Scientific Observation Research station ("Nanling Station" for short) in the Nanling Mountains, Guangdong, we carried out positioning research in Nanling National Nature Reserve. In this paper, we disclosed the raw data of 10 min average wind speed, precipitation, air temperature, air pressure, solar radiation (i.e. total radiation, reflected radiation, ultraviolet radiation, net radiation, photosynthetically active radiation) as well as relative humidity continuously collected in the subtropical evergreen broad-leaved forest area of the Nanling Station from 2021-2022 on a daily scale after data processing, quality control and evaluation. The dataset accessible to the public can serve as a valuable resource for further insights into the Nanling Mountain Forest Ecosystem, and provide foundation for efforts related to the restoration and protection of forest ecosystems.

2018年浙江古田山24公顷亚热带常绿阔叶林动态监测样地林冠结构和地形数据集

The canopy structure and topographic variables jointly influence the spatiotemporal variation of habitats for important species. However, there remains a considerable scarcity of precise forest canopy structure and topographic data obtained from large-scale forest dynamic plots. LiDAR data, acquired via near-surface remote sensing platforms, facilitates precise computation of both canopy structure and topographic variables. This study selected a 24-hectare dynamic plot in the subtropical evergreen broadleaf forest, located in the core conservation area of Qianjiangyuan National Park, as its focal point for survey. We conducted an analysis and quality control process on the point cloud data in 2018 using a near-surface remote sensing platform for this area, aiming to acquire corresponding canopy structure and topography data. Ground data collected through Real-Time Kinematic (RTK) surveying were used to verify and validate the accuracy of the digital elevation model (DEM). The analysis revealed a mean square error of elevation of 0.07 meters, suggesting a high level of accuracy of the dataset. The 24-hectare subtropical evergreen broad-leaved forest dynamic plot represents a typical low-altitude evergreen broad-leaved forest in the central subtropical region. This dataset can provide essential data support for the monitoring and research of biodiversity in subtropical broadleaved evergreen forests.

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy (R2 = 0.58) is higher than that of using only optical images (R2 = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and it is the highest in the coniferous forest at 46.60 Mg/ha. And the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.

Assessing the Relationship between Tree Growth, Crown Size, and Neighboring Tree Species Diversity in Mixed Coniferous and Broad Forests Using Crown Size Competition Indices

Competition among trees for limited resources (e.g., sunlight, water, and nutrients) impacts their growth differently. Crown plays a crucial role in resource access for trees. However, uncertainties persist regarding crown size differences, tree growth, and tree competition levels between coniferous and broadleaf trees in mixed-age forests. We conducted a study on 3008 live trees across 28 plots in Hunan Province to measure individual crown size and tree growth increment. Introducing a new metric, the Crown Size Competition Index (CSCI), we assessed competition pressure among coniferous and broadleaf species in mixed-age forests. We examined the correlation between competition indices and tree growth increment while also investigating the influence of neighboring species diversity on tree competition. Our results revealed a significant negative correlation between the tree growth of Cunninghamia lanceolata (Lamb.) Hook.(CL) and Phoebe bournei (Hemsl.) Yang (PB) and the competition index. Increased competitive pressure on the focal tree corresponded to a decline in the growth of focal tree volume, with a more pronounced impact observed in PB compared to CL. The diversity of neighboring species contributed to variations in competition for the focal tree, with broadleaved species (PB) exerting a greater influence on the focal tree than coniferous species (CL). These findings underscore the competitive potential of PB in mature coniferous stands and advocate for the restoration of stratified mixes in CL broadleaf forests. Furthermore, our findings support the management strategies for the valuable tree species PB.

The Impact of Canopy on Nutrient Fluxes through Rainfall Partitioning in a Mixed Broadleaf and Coniferous Forest

Rainfall constitutes the primary input in the nutrient flux within forest ecosystems. The forest canopy modulates this flux by partitioning rainfall and selectively absorbing or adding nutrients. In mixed forests, variation in tree species composition regulates rainwater chemical composition, potentially leading to spatial heterogeneity in nutrient distribution and influencing nutrient cycling processes. This study examined the partitioning of rainfall into throughfall and stemflow, as well as their associated nutrient concentrations and fluxes, in a mixed broadleaf and coniferous forest on Changbai Mountain in Northeast China. We observed a rising trend in nutrient contents from rainfall to throughfall and then stemflow. The nutrient contents of stemflow varied largely with tree species due to the differences in canopy structure and bark morphological characteristics. The nutrient input contributed by throughfall and stemflow was 92.30 kg ha−1 during the observation period, and most elements underwent passive leaching through washout except for F− and Na+. We note that the nutrient fluxes in stemflow differed among tree species, with Pinus koraiensis (PK) delivering more acid group anions and Quercus mongolica (QM) providing more cations. Our research provides new insights into nutrient cycling within mixed forest canopies, sparking a transformative advancement in forest management and protection strategies through hydrochemistry-driven solutions.

Diversity of 3D APAR and LAI dynamics in broadleaf and coniferous forests: Implications for the interpretation of remote sensing-based products

Forests substantially mediate the water and carbon dioxide exchanges between terrestrial ecosystems and the atmosphere. The rate of this exchange, including evapotranspiration (ET) and gross primary production (GPP), depends mainly on the underlying vegetation type, health state, and the composition of abiotic environmental drivers. However, the complex 3D structure of forest canopies and the inherent top-view perspective of optical and thermal remote sensing complicate remote sensing-based retrievals of biotic and abiotic factors that eventually determine ET and GPP. This study investigates the sensitivity of remote sensing approaches to 3D variation of abiotic and biotic environmental drivers. We use 3D virtual scenes of two structurally different Swiss forests and the radiative transfer model DART to simulate the 3D distribution of solar irradiance and reflected radiance in the forest canopy. These simulations, in combination with LiDAR data, are used to derive the absorbed photosynthetic active radiation (APAR) and the leaf area index (LAI) in 3D space. The 3D variation of both parameters was quantified and analyzed. We then simulated images of the top-of-canopy bi-directional reflectance factor (BRF) and compared them with the hemispheric-conical reflectance factor (HCRF) data derived from HyPlant airborne imaging spectrometer measurements. The simulated BRF data was used to derive APAR and LAI, and the results were compared to their respective 3D representations. We unravel considerable spatial differences between both representations. We discuss possible reasons for the disagreement, including a potential insensitivity of the inherent top-of-canopy view for the real 3D product dynamics and limitations of the processing of remote sensing data, especially the approximation of effective surface irradiance. Our results can help understanding sources of uncertainties in remote sensing based gas exchange products and defining mitigation strategies.

Modelling Distribution of an Endangered Longhorn Beetle, Callipogon relictus (Coleoptera: Cerambycidae), in Northeast Asia

Based on our own field data and using modeling of modern potential ranges (in the MaxEnt program), an assessment of the spatial distribution of the rare representative of the longhorn beetle family, Callipogon (Eoxenus) relictus Semenov 1899, was conducted in northeast Asia (220 geographic locations). The generated maps of the potential range of C. relictus demonstrate a high likelihood of the species’ presence in the upland areas of southern far east Russia, the provinces of northeastern China, and the Korean Peninsula. Field data also indicate the extensive association of the species with undisturbed broadleaf and coniferous–broadleaf forests in northeast Asia. Maps of the potential distribution of C. relictus in northeast Asia have been compiled based on four climate change scenarios from the present time to 2070. Under all of the climate scenarios used, it is shown that suitable habitats for the species will persist in certain areas of Primorsky Krai, as well as neighboring provinces of the People’s Republic of China and a small enclave on the Korean Peninsula in Gangwon-do province. Significant reduction in suitable conditions for the rare longhorn beetle will occur in the rest of its distribution range.

Optimal representation of spring phenology on photosynthetic productivity across the northern hemisphere forests

Accurate simulation of the onset of spring is crucial for predicting the photosynthetic productivity of forest ecosystems. Nonetheless, the potential of phenology simulations on predictions of forest gross primary productivity (GPP) remains poorly understood, with previous studies generally focused on predicting phenology dates themselves or on limited scales. Here, we constructed a framework through the critical process of leaf growth to couple a terrestrial biosphere model—FORCCHN2 with five widely used phenology models, including one-phase (involving heat forcing), two-phase (considering chilling demand), and multi-phase models (integrating photoperiod effects) through the critical process of leaf growth. We evaluated the GPP performance from the multiple forest biomes in 74 eddy covariance (EC) sites and compared the total trends to satellite observations across the northern hemisphere forests. The predictions of the five models could reproduce 62.41–67.24 % variations of the GPP observations in all EC sites. In the deciduous broadleaf forest sites, we found the two-phase and multi-phase coupled models performed better than the one-phase coupled model. Chilling may play an essential role in controlling photosynthetic activity in deciduous broadleaf forests. Moreover, the multi-phase coupled model integrated photoperiod effects performed best at capturing GPP changes of the whole northern hemisphere forests. The predictions also showed the GPP in the northern hemisphere ranged from 21.68 to 26.06 Pg C year−1, and the total GPP showed an increasing trend. This study provides local and regional available evidence for the impact of phenology on photosynthesis with climate warming. The results highlight the necessity for enhancing understanding of the phenology factors on GPP and integrating the appropriate phenology representation to improve predictions of photosynthetic productivity for forests, especially in prediction to large scales and long-term trends.

The roles of environmental filtering and competitive exclusion in the plant community assembly at Mt. Huangshan are forest-type-dependent

Integrating trait and phylogenetic data are increasingly being used to enhance our understanding of the underlying community assembly processes and optimize the interpretation of unmeasured, phylogenically conserved traits. The biodiversity of Huangshan, China, was higher, but it was suffered from some damage, such as biological invasion and human disturbance. We sampled 176 species from three different forests (EBF: evergreen broadleaf forest; DBF: deciduous broadleaf forest; MNBF: mixed needleleaf and broadleaf forest) which are the representative communities of Mt. Huangshan. Then, we measured six functional traits (leaf thickness, leaf area, specific leaf area, leaf carbon concentration, leaf nitrogen concentration and leaf phosphorus concentration) and constructed a molecular phylogeny tree to assess community structure using species trait-phylogenetic distances. Results showed that six traits are more phylogenetically convergent than predicted, which was not closely related to the evolutionary history of species. The contribution of phylogenetic distance and functional distance to community construction was equal in total layer and more importance was given to functional distances for tree and shrub layer, indicating that closely related species tended to be phenotypically different. The functional trait-phylogenetic of EBF and MNBF was clustering, while DBF exhibited functional trait-phylogenetic over-dispersion. In other words, environmental filtering appears to be the dominant force in EBF and MNBF, while competitive exclusion appears to become more important in DBF. Our study also indicates that spatial factors resulted in a higher explanatory power than environmental factors. In according to better protect forest biodiversity, species coexistence mechanisms should be incorporated into the management of forest communities and to predict their presentation in future environmental scenarios.

Investigating Diurnal and Seasonal Cycles of Vegetation Optical Depth Retrieved From GNSS Signals in a Broadleaf Forest

AbstractVegetation Optical Depth (VOD) has emerged as a valuable metric to quantify water stress on vegetation's carbon uptake from a remote sensing perspective. However, existing spaceborne microwave remote sensing platforms face limitations in capturing the diurnal VOD variations and global products lack site‐level validation against plant physiology. To address these challenges, we leveraged the Global Navigation Satellite System (GNSS) L‐band microwave signal, measuring its attenuation by the canopy of a temperate broadleaf forest using a pair of GNSS receivers. This approach allowed us to collect continuous VOD observations at a sub‐hourly scale. We found a significant seasonal‐scale correlation between VOD and leaf water potential. The VOD diurnal amplitude is affected by soil moisture, plant transpiration and leaf surface water. Additionally, VOD can help independently estimate plant transpiration. Our findings pave the way for a deeper understanding of response of the vegetation to water stress at finer temporal scales.

Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model

Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and Ract25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems.

Trimethylamine from Subtropical Forests Rival Total Farmland Emissions in China.

Many types of living plants release gaseous trimethylamine (TMA), making it a potentially important contributor to new particle formation (NPF) in remote areas. However, a panoramic view of the importance of forest biogenic TMA at the regional scale is lacking. Here, we pioneered nationwide mobile measurements of TMA across a transect of contiguous farmland in eastern China and a transect of subtropical forests in southern China. In contrast to the farmland route, TMA concentrations measured during the subtropical forest route correlated significantly with isoprene, suggesting potential TMA emissions from leaves. Our high time-resolved concentrations obtained from a weak photo-oxidizing atmosphere reflected freshly emitted TMA, indicating the highest emission intensity from irrigated dryland (set as the baseline of 10), followed by paddy field (7.1), subtropical evergreen forests (5.9), and subtropical broadleaf and mixed forests (4.3). Extrapolating their proportions roughly to China, subtropical forests alone, which constitute half of the total forest area, account for nearly 70% of the TMA emissions from the nation's total farmland. Our estimates, despite the uncertainties, take the first step toward large-scale assessment of forest biogenic amines, highlighting the need for observational and modeling studies to consider this hitherto overlooked source of TMA.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Drought effects on soil greenhouse gas fluxes in a boreal and a temperate forest

AbstractChanging water regimes (e.g. drought) have unknown long-term consequences on the stability and resilience of soil microorganisms who determine much of the carbon and nitrogen exchange between the biosphere and atmosphere. Shifts in their activity could feedback into ongoing climate change. In this study, we explored soil drought effects on soil greenhouse gas (GHG; CO2, CH4, N2O) fluxes over time in two sites: a boreal, coniferous forest in Finland (Hyytiälä) and a temperate, broadleaf forest in Austria (Rosalia). Topsoil moisture and topsoil temperature data were used to identify soil drought events, defined as when soil moisture is below the soil moisture at the permanent wilting point. Data over multiple years from automated GHG flux chambers installed on the forest floor were then analyzed using generalized additive models (GAM) to study whether GHG fluxes differed before and after drought events and whether there was an overall, multiyear temporal trend. Results showed CO2 and N2O emissions to be more affected by drought and long-term trends at Hyytiälä with increased CO2 emission and decreased N2O emissions both following drought and over the entire measurement period. CH4 uptake increased at both sites both during non-drought periods and as an overall, multiyear trend and was predominantly affected by soil moisture dynamics. Multiyear trends also suggest an increase in soil temperature in the boreal forest and a decrease in soil moisture in the temperate forest. These findings underline forests as an important sink for CH4, possibly with an increasing rate in a future climate.