Forest Ecosystems In China Research Articles

Divergent responses of forest canopy height to environmental conditions across China

As major components of terrestrial ecosystems, forest ecosystems play an important role in sequestering carbon and hence mitigating climate change. Canopy height is a crucial factor characterizing the structure and function of forest ecosystems, yet the driving mechanism of forest canopy height receives less attentions in China. Here, we utilize the satellite-based forest canopy height product with several environmental and climate factors (e.g. forest age, temperature, etc.) to delineate the spatial distributions of forest canopy height and its drivers in China at 1 km spatial resolution during the period of 2014 to 2018. The random forest is employed for identifying the dominant factors at province level, while Shapley additive explanations (SHAP) analysis is further incorporated at pixel-level to dig into the specific contributions of each driver. The results show that forest age primarily dominates the spatial distributions of forest canopy height across different forest ecosystems of China, followed by mean annual precipitation, soil type, and aspect. SHAP analysis further indicates that other factors, such as soil moisture and wind speed, also play critical roles to shape the spatial patterns of forest canopy height in China, which could not be revealed from province-level random forest analyses. Such results emphasize the priority of incorporating SHAP analysis with random forest to advance our understanding of forest canopy height distributions and benefit future projections. Our study highlights the necessity to characterize the spatial heterogeneity of forest canopy height, which is critical for accurate estimations of forest and even terrestrial carbon sink in China, facilitating the achievement of the goal of “carbon peak in 2030 and carbon neutrality in 2060”.

Water fluxes and energy partitioning over a Larix principis-rupprechtii plantation forest in a dryland mountain ecosystem, Northwest China

Plantations make up a sizable component of forest ecosystems in China's drylands and play an important role controlling the energy balance and water cycle. However, there is limited knowledge regarding the exchange of water and energy in dryland plantations, particularly those located within the intricate and unique environments of dryland mountain regions. The focus of this study was the investigation of a Larix principis−rupprechtii plantation within a dryland mountain ecosystem located in Northwest China. The eddy covariance method was employed to calculate the water and energy fluxes, while the thermal dissipation probe (TDP) method was utilized to measure forest overstory transpiration (T) from 2018 to 2021. The links between energy allocation and various components in water flux were investigated, as well as the variable dynamics of water and energy flux and their relationship with environmental conditions. The findings demonstrated that the annual mean cumulative evapotranspiration (ET) was about 510 mm, accounting for over 82.8 % of the precipitation, which was mainly from soil evaporation and understory transpiration, rather than overstory transpiration (T). The average value of T/ET was 0.25 for the growing season, and surface conductance (Gs) was always higher than canopy conductance (Gc) on both daily and monthly scales. The evaporative fraction (EF) and Priestley-Taylor model coefficient (α) in the growing season were significantly higher than those in the non-growing season, and α>1 from June to September, indicating that the ecosystem in the study area had sufficient water supply. Furthermore, during the growing season, the ratio of latent heat flux (LE) to net radiation (Rn) was larger than that in the non-growing season, while the proportion of sensible heat flux (H) was opposite, and soil heat flux (G) accounted for the smallest proportion of net radiation. This study provides valuable insights into the energy fluxes and water fluxes within the ecosystem of dryland mountain plantations, and it serves as a guide for choosing afforestation tree species in the future.

Bamboo Breeding Strategies in the Context of “Bamboo as a Substitute for Plastic Initiative”

Bamboo breeding strategies are essential in realizing “Bamboo as a Substitute for Plastic (BASP)”. This review article aims to explore the crucial role of breeding strategies in achieving the substitution of plastic products with bamboo as outlined under the BASP Initiative. Firstly, we address the issue of plastic pollution, along with the background of reducing the market share and demand for plastic products. It categorizes the types of bamboo products that can fully or partially replace plastic products in various categories, such as daily necessities, building materials, and industrial products. Then, we investigate which bamboo species can replace which plastic products and propose the need for bamboo improvement. Furthermore, it presents data from positioning observation research stations for bamboo forest ecosystems in China and outlines the essential traits necessary for bamboo substitution, including characteristics like long internode length, extended fiber length, thick culm wall, and optimal cellulose-to-lignin content ratio, among others. Finally, we discuss breeding methods and genetic improvement as key strategies to achieve bamboo substitution and suggest the potential of enhancing bamboo traits to serve as a viable replacement source for plastics. This comprehensive approach aims to enhance bamboo’s growth features and physical properties to meet the criteria for substituting bamboo for plastics effectively.

Root carboxylate release is common in phosphorus-limited forest ecosystems in China: using leaf manganese concentration as a proxy

Abstract Background and aims Certain plant species release root carboxylates in response to phosphorus (P) limitation; however, the prevalence of root exudate release in species in P-limited forest ecosystems remains unexplored due to challenges in field assessment. Methods Manganese (Mn) accumulation in mature leaves can indicate the presence of root carboxylate exudates in rhizosphere soil. To account for environmental factors such as soil pH, a negative reference species that does not release carboxylates is used for comparison. In this study, we assessed multiple forest stands across soil types and different levels of P availability in northern (Gansu) and southern (Guangxi) China. Leaf and soil samples were collected from 188 plant families representing various life forms, and leaf Mn concentration ([Mn]) was analyzed as a proxy for root carboxylate exudation patterns, using Dryopteridaceae as a negative reference. Results The results supported our hypotheses that leaf [Mn] was higher in P-limited forests of southern China compared to P-richer forests of northern China, even though the soil [Mn] was higher in the forests of northern China. Additionally, we observed a higher prevalence of species with high leaf [Mn] across various plant families in Guangxi (82%) than in Gansu (42%). Conclusion Our findings suggest a potential common strategy among plants in Guangxi forests, where root exudates are released in response to P limitation, possibly due to ineffective mycorrhizal symbiosis for nutrient acquisition. The diverse forest systems in China exhibit varying soil P availability, leading to the evolution of plant species with distinct P-acquisition strategies.

Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers.

Microbes inhabiting deep soil layers are known to be different from their counterpart in topsoil yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (>1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18-m depth profiles at 20-50-cm intervals across contrasting aridity conditions in semi-arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity declined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant-derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa-taxa and bacteria-fungi associations and more influence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep-soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria-fungi associations, but increased the relative abundance of aerobic ammonia oxidation, manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, complexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that, even microbial communities and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole-soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios.

Temporal and Spatial Variation Characteristics of NDVI of Forest Ecosystem in China from 1998 to 2015 and its Driving Force of Climate Factors

Temporal and Spatial Variation Characteristics of NDVI of Forest Ecosystem in China from 1998 to 2015 and its Driving Force of Climate Factors

Molecular phylogeny and morphology reveal four new species of Thelephora (Thelephorales, Basidiomycota) from subtropical China, closely related to T. ganbajun.

The genus of Thelephora is a group of cosmopolitan ectomycorrhizal fungi with basidiocarps of morphological diversity that has an extremely scarce species reported from the forest ecosystem in China. In this study, phylogenetic analyses of Thelephora species from subtropical China were carried out based on multiple loci including the internal transcribed spacer (ITS) regions, the large subunit of nuclear ribosomal RNA gene (nLSU), and the small subunit of mitochondrial rRNA gene (mtSSU). Maximum likelihood and Bayesian analyses were used to construct the phylogenetic tree. The phylogenetic positions of four new species, Th. aquila, Th. glaucoflora, Th. nebula, and Th. pseudoganbajun, were revealed based on morphological and molecular evidence. Molecular analyses demonstrated that the four new species were closely related to Th. ganbajun and formed a clade with robust support in the phylogenetic tree. Regarding morphology, they share some common morphological characteristics, including flabelliform to imbricate pilei, generative hyphae more or less covered by crystals, and subglobose to irregularly lobed basidiospores (5-8 × 4-7 μm) with tuberculate ornamentation. These new species are described and illustrated and are compared to similar morphological or phylogenetically related species. A key to the new and allied species from China is provided.

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China.

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

A dataset of carbon and water fluxes and micrometerological elements in Danzhou rubber plantation (2010-2018)

Rubber plantation is one of the main artificial vegetation types in Hainan Island. As an important forest ecosystem in China’s tropical regions, rubber plantation plays an important role in the regional carbon and water cycle. Measuring the carbon and water fluxes is critical for estimating the carbon and water balance and energy exchange of rubber plantation. Since November 2009, near-surface CO2 and H2O fluxes observation have been carried out in Danzhou rubber plantation, including eddy covariance system and meteorological observation system. Up to now, it has accumulated flux and meteorological observation data for 12 years. This dataset organizes the rubber plantation flux and conventional meteorological data in Danzhou from 2010 to 2018, including net ecosystem productivity, ecosystem respiration, gross ecosystem productivity, latent heat flux, sensible heat flux, evapotranspiration, air temperature, air relative humidity, vapor pressure, wind speed, wind direction, atmospheric pressure, total radiation, net radiation, photosynthetically active radiation, soil temperature, soil water content and precipitation. This dataset can provide basis and reference for ecosystem evaluation and sustainable development planning in economic and social.

Does Forest Soil Fungal Community Respond to Short-Term Simulated Nitrogen Deposition in Different Forests in Eastern China?

Nitrogen (N) deposition has changed plants and soil microbes remarkably, which deeply alters the structures and functions of terrestrial ecosystems. However, how forest fungal diversity, community compositions, and their potential functions respond to N deposition is still lacking in exploration at a large scale. In this study, we conducted a short-term (4-5 years) experiment of artificial N addition to simulated N deposition in five typical forest ecosystems across eastern China, which includes tropical montane rainforest, subtropical evergreen broadleaved forest, temperate deciduous broadleaved forest, temperate broadleaved and conifer mixed forest, and boreal forest along a latitudinal gradient from tropical to cold temperature zones. Fungal compositions were identified using high-throughput sequencing at the topsoil layer. The results showed that fungal diversity and fungal community compositions among forests varied apparently for both unfertilized and fertilized soils. Generally, soil fungal diversity, communities, and their potential functions responded sluggishly to short-term N addition, whereas the fungal Shannon index was increased in the tropical forest. In addition, environmental heterogeneity explained most of the variation among fungal communities along the latitudinal gradient. Specifically, soil C: N ratio and soil water content were the most important factors driving fungal diversity, whereas mean annual temperature and microbial nutrient limitation mainly shaped fungal community structure and functional compositions. Topsoil fungal communities in eastern forest ecosystems in China were more sensitive to environmental heterogeneity rather than short-term N addition. Our study further emphasized the importance of simultaneously evaluating soil fungal communities in different forest types in response to atmospheric N deposition.

Linking Leaf Functional Traits with Soil and Climate Factors in Forest Ecosystems in China.

Plant leaf functional traits can reflect the adaptive strategies of plants to environmental changes. Exploring the patterns and causes of geographic variation in leaf functional traits is pivotal for improving ecological theory at the macroscopic scale. In order to explore the geographical variation and the dominant factors of leaf functional traits in the forest ecosystems of China, we measured 15 environmental factors on 16 leaf functional traits in 33 forest reserves in China. The results showed leaf area (LA), carbon-to-nitrogen ratio (C/N), carbon-to-phosphorus ratio (C/P), nitrogen-to-phosphorus ratio (N/P), phosphorus mass per area (Pa) and nitrogen isotope abundance (δ15N)) were correlated with latitude significantly. LA, Pa and δ15N were also correlated with longitude significantly. The leaf functional traits in southern China were predominantly affected by climatic factors, whereas those in northern China were mainly influenced by soil factors. Mean annual temperature (MAT), mean annual precipitation (MAP) and mean annual humidity (MAH) were shown to be the important climate factors, whereas available calcium (ACa), available potassium (AK), and available magnesium (AMg) were shown to be the important climate factors that affect the leaf functional traits of the forests in China. Our study fills the gap in the study of drivers and large-scale geographical variability of leaf functional traits, and our results elucidate the operational mechanisms of forest-soil-climate systems. We provide reliable support for modeling global forest dynamics.

Nonlinear imprints of forest coverage on the relationships between gross primary production (GPP) and landscape patterns

Land use and land cover change (LUCC) has yielded significant impacts on ecosystem structure and functions. Gross primary production (GPP), the largest flux in the terrestrial carbon cycle, has been drastically changed by various LUCC activities. However, the indirect impacts of LUCC on GPP (i.e., local environment changes induced by LUCC), particularly those related to landscape dynamics, remain unclear. Here, we explored the relationships between GPP and landscape metrics of forest ecosystems in China along a forest coverage gradient from 2001 to 2019, using an up-to-date GPP dataset derived from Solar-Induced Chlorophyll Fluorescence and a widely used landcover product MCD12Q1. Results showed that forest landscape features (e.g., core area index, CAI) synchronized with GPP spatially as shown by the distribution of their hot/cold spots on correlations. Furthermore, two nonlinear change patterns of GPP and landscape metrics along forest coverage gradient were found. One is a hump-shaped pattern, which indicated that GPP and some landscape features (e.g., annual median GPP, annual change rate of forest composition) peaked in medium (about 70–80%) forest-cover regions. Another is a super-linear curve, revealing the rapid and accelerated growth of some landscape features (e.g., annual median CAI) in high forest-cover regions. Finally, forest coverage affected the sensitivity of GPP to landscape features in a nonlinear fashion, characterized as an inverted ‘hook’ curve peaking in intermediate (around 70%) forest-coverage regions. Our study proposed the optimal landscape regulation hypothesis to explain the nonlinear pattern: forest GPP is most responsive to the landscape pattern regulation at intermediate to low levels of disturbance, implying that forest coverage has various impacts on landscape features; together they affect GPP nonlinearly. Regulating the forest landscape composition and configuration can enhance the performance of forest ecosystems in addition to managing forest coverage. Thus, we recommend the adoption of suitable region-specific forest management practices to improve GPP and other ecosystem services of forest ecosystems via manipulating landscape composition and configuration.

Dynamics of Vegetation Productivity in Relation to Surface Meteorological Factors in the Altay Mountains in Northwest China

Vegetation productivity, as the basis of the material cycle and energy flow in an ecosystem, directly reflects the information of vegetation change. At the ecosystem level, the gross primary productivity (GPP) refers to the amount of organic carbon fixed by plant bodies. How to accurately estimate the spatiotemporal variation of vegetation productivity of the forest ecosystem in the Altay Mountains in northwest China has become a critical issue to be addressed. The Altay Mountains, with rich forest resources, are located in a semi-arid climate zone and are sensitive to global climate changes, which will inevitably have serious impacts on the function and structure of forest ecosystems in northwest China. In this paper, to reveal the variation trends of vegetation gross primary productivity (GPP) and its response to surface meteorological factors in the Altay Mountains in northwest China, daily temperature and precipitation data from the period of 2000–2017 were collected from seven meteorological stations in Altay prefecture and its surrounding areas; the data were analyzed by using the MODIS GPP model, moving average trend analysis, linear regression analysis and the climate tendency rate method. The results show that: (1) The spatial distribution pattern of GPP in the whole year was almost the same as that in the growing season of vegetation in the Altay Mountains. In the whole mountain range, the proportion of the area which had a GPP value of 400–600 g c/m2 had the highest value; the proportion of the annual and growing season of this area was 41.10% and 40.88%, respectively, which was mainly distributed in the middle and west alpine areas of the Altay Mountains. (2) There was a big gap in the GPP value in the different stages of the vegetation growing season (April to September), which reached the highest value in July, the area with a GPP of 100–150 g c/m2 was the highest, with 36.15%. (3) The GPP of the Altay Mountains showed an overall increasing trend, but the annual fluctuation was relatively large. In 2003, 2008, 2009 and 2014, the GPP showed lower values, which were 385.18 g c/m2, 384.90 g c/m2, 384.49 g c/m2 and 393.10 g c/m2, respectively. In 2007, 2011 and 2016, the GPP showed higher values, which were 428.49 g c/m2, 428.18 g c/m2 and 446.61 g c/m2. (4) In 64.85% of the area of the Altay Mountains, the GPP was positively correlated with annual average temperature, and in 36.56% of the area, the correlation coefficient between temperature and GPP ranged from −0.2 to 0. In 71.61% of the area of the Altay Mountains, the GPP was positively correlated with annual accumulated precipitation, and in 28.39% of the area, the GPP was negatively correlated with annual accumulated precipitation. Under the scenario of global climate change, our study has quantitatively analyzed the long-term dynamics of vegetation GPP and its responses to meteorological factors in the Altay Mountains, which would be helpful for evaluating and estimating the variation trends of forest ecosystems in China, and has important guiding significance for policy formulation to protect forest resources and improve the local ecological environment.

A meta-analysis of understory plant removal impacts on soil properties in forest ecosystems

Understory plants have an important role in regulating various functions and processes in forest ecosystems. However, the overall consequences of understory plant losses on soil properties remain unclear. Here, we used a meta-analysis to determine overall trends in soil organic carbon, total nitrogen and microorganism following understory removal experiments in forest ecosystems of China. Sites with removed understories exhibited significant increases in soil temperature and decreases in soil organic carbon, total nitrogen, soil water content, pH, and microbial biomass. Fungi responded more quickly to plant removal than bacteria. Negative effects were increased with treatment duration and were stronger in broad-leaved forests than in coniferous forests. The responses of soil water, nutrient content, and microbial community composition to understory plant removal diminished as mean annual temperature and precipitation decreased. Our study showed that the effects of understory plant removal on soil properties varied among experimental durations, ecosystem types, and environmental factors (i.e., mean annual temperature, mean annual precipitation, and latitude). These results indicate that understory plants help in sustaining soil organic carbon, total nitrogen, and microbial activities in forest ecosystems. Our findings suggest that understory plants should be maintained to support the health of forest ecosystems.

Analysis of Factors Related to Forest Fires in Different Forest Ecosystems in China

Forests are the largest terrestrial ecosystem with major benefits in three areas: economy, ecology, and society. However, the frequent occurrence of forest fires has seriously affected the structure and function of forests. To provide a strong scientific basis for forest fire prevention and control, Ripley’s K(d) function and the LightGBM algorithm were used to determine the spatial pattern of forest fires in four different provinces (Heilongjiang, Jilin, Liaoning, Hebei) in China from 2019 to 2021 and the impact of driving factors on different ecosystems. In addition, this study also identified fire hotspots in the four provinces based on kernel density estimation (KDE). An artificial neural network model (ANN) was created to predict the probability of occurrence of forest fires in the study area. The results showed that the forest fires were spatially clustered, but the variable importance of different factors varied widely among the different forest ecosystems. Forest fires in Heilongjiang and Liaoning Provinces were mainly caused by human-driven factors. For Jilin, meteorological factors were important in the occurrence of fires. Topographic and vegetation factors exhibited the greatest importance in Hebei Province. The selected driving factors were input to the ANN model to predict the probability of fire occurrence in the four provinces. The ANN model accurately captured 93.17%, 90.28%, 83.16%, and 89.18% of the historical forest fires in Heilongjiang, Jilin, Liaoning, and Hebei Provinces; Precision, Recall, and F-measure based on the full dataset are 0.87, 0.88, and 0.87, respectively. The results of this study indicated that there were differences in the driving factors of fire in different forest ecosystems. Different fire management policies must be formulated in response to this spatial heterogeneity.

Temporal and Spatial Activity Patterns of Sympatric Wild Ungulates in Qinling Mountains, China.

Simple SummaryInformation on species’ niche differentiation will contribute to a greater understanding of the mechanisms of coexistence benefitting the conservation and management of ecological communities. The widespread reduction in apex predators and more restricted hunting management has con-tributed to an increase in the abundance of wild ungulates in the Qinling Mountains, presumably resulting in an intensifying interspecific competition pressure. However, the activity patterns of the species in this region are completely unknown due to difficulty in accessing the locations where they occur. Thus, we used camera trapping to systematically investigate spatial and temporal activity patterns of sympatric ungulates in the Qinling Mountains, where top predators are virtually absent. This intensive camera-trap survey elucidated much more detailed studies of spatial and temporal activity patterns in multiple sympatric wild ungulates under natural conditions. Further, our results provided detailed information of the spatial and temporal ecology of ungulate communities in forest ecosystems, which would be a guide to establishing conservation priorities as well as efficient management programs.Dramatic increases in populations of wild ungulates have brought a new ecological issue in the Qinling mountains. Information on species’ niche differentiation will contribute to a greater understanding of the mechanisms of coexistence, so as to ultimately benefit the conservation and management of ecological communities. In this study, camera trapping was used to investigate spatial and temporal activity patterns of sympatric wild ungulates in the Qinling Mountains of China, where top predators were virtually absent. We obtained 15,584 independent detections of seven wild ungulate species during 93,606 camera-trap days from April 2014 to October 2017. Results showed that (i) the capture rate differed significantly across species, with the capture rate of reeve muntjac being significantly higher than that of other species; (ii) the wild boar had a higher occupancy rates (ψ = 0.888) than other six ungulates, and distance to settlements had a negative relationship with wild boar (β = −0.24 ± 0.17); (iii) the forest musk deer and mainland serow had low spatial overlaps with other five wild ungulates, while spatial overlap indices of any two given pairs of wild ungulates were relatively high; (iv) all wild ungulates species (expect wild boar) were mainly active during crepuscular and diurnal periods, and showed bimodal activity peaks at around 05:00–07:00 and 17:00–19:00; and finally, (v) all wild ungulates showed moderate to high temporal overlaps. The results provided detailed information of the spatial and temporal ecology of wild ungulate communities in forest ecosystems of China, which also would be a guide to establish conservation priorities as well as efficient management programs.

Long-Term Effects of Fire Severity and Climatic Factors on Post-Forest-Fire Vegetation Recovery

As a major disturbance to forest ecosystems, wildfires pose a serious threat to the ecological environment. Monitoring post-fire vegetation recovery is critical to quantifying the effects of wildfire on ecosystems and conducting forest resource management. Most previous studies have analyzed short-term (less than five years) post-fire recovery and limited the driving factors to temperature and precipitation. The lack of long-term and multi-faceted observational analyses has limited our understanding of the long-term effects of fire on vegetation recovery. This study utilized multi-source remote sensing data for a long time series analysis of post-fire vegetation recovery in China based on Google Earth Engine (GEE) cloud computing platform. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Burn Ratio (NBR), and Normalized Difference Moisture Index (NDMI) were selected to quantify the low, moderate, and high severity of burned areas. Ridge Regression Model (RRM) was used to analyze the relationship between 15 driving factors and the vegetation regeneration process. The results show that it took at least 7–10 years for the vegetation index to recover to the pre-fire level after a forest fire. The recovery rate of high severity combustion areas was the fastest within the first two years. From the results of Ridge Regression, it came out that the overall fitting degree of the model with NDVI as the dependent variable was superior than that with EVI. The four variables of temperature, precipitation, soil temperature, and soil moisture were able to explain the change in more detail in vegetation indices. Our study enriches the research cases of global forest fires and vegetation recovery, provides a scientific basis for the sustainable development of forest ecosystems in China, and provides insight into environmental issues and resource management.

Litter decomposition and nutrient release from monospecific and mixed litters: Comparisons of litter quality, fauna and decomposition site effects

Abstract Litter decomposition and nutrient release are key processes for soil C and nutrient cycling. However, the relative importance of the effects of litter quality, fauna and decomposition site on litter decomposition remains poorly understood. Moreover, the macronutrient and micronutrient (C, N, P, K, Ca, Mg, Mn, Cu and Zn) release in the decomposition process are even less well‐known. In this study, we performed 5040 litterbag samplings of monospecific (Larix gmelinii, Acer mono, Quercus mongolica and Juglans mandshurica) and mixed (Larix with each broad‐leaved litter) litter treatments using litterbags with three mesh sizes (for different‐sized fauna) in four paired stands of natural secondary forests compared with adjacent Larix plantations in a temperate forest ecosystem in China. Litter decomposition rate and macronutrient and micronutrient release during decomposition were assessed. We found that variation in litter quality had a considerably greater effect on litter decomposition than fauna and decomposition site. The decomposition constant was significantly related to the initial litter Mg concentration and lignin/P ratio, suggesting the importance of the Mg content and lignin/P ratio in controlling litter decomposition in temperate tree species. We also showed that macrofauna significantly increased monospecific but not mixed litter decomposition. In addition, the decomposition site conditions further influenced monospecific litter decomposition. Furthermore, the litter quality significantly influences N and Mn release in both monospecific and mixed litters. Compared with Larix litter, the average of N and Mn increased 33.9% and 53.8% in three broad‐leaved litters, and 21.4% and 38.0% in three mixed litters respectively. Synthesis. Litter quality is a relatively more important factor than fauna and decomposition site for the decomposition of both monospecific and mixed litters when studied at a local scale in temperate forests. Moreover, litter decomposition enhanced in plantation sites with poor soil nutrient status, which has implications for C and nutrient cycling and long‐term below‐ground forest ecosystem functions.

Stronger microbial decay of recalcitrant carbon in tropical forests than in subtropical and temperate forest ecosystems in China

Soil enzymes are the most potent bioactive components in forest ecosystems. Cellulases and ligninases are vital carbon (C)-degrading enzymes that target different C pools. The ratio of ligninase-to-cellulase activity is good indicator for microbial soil C preference, play an important role in soil C cycling. However, our understanding of enzyme ratios and their drivers across forest ecosystems remains unclear. In this study, we hypothesized that (i) the ligninase-to-cellulase ratio increased from temperate forests to tropical forest ecosystems, and (ii) the dominant factors would be microbial abundances. About 2–3 kg of topsoil (0–10 cm) from each of the ten forest ecosystems were collected across a 3425 km gradient in China between July and August 2019. We analyzed the biogeographic patterns of ligninase and cellulase activities and the ratio of ligninase-to-cellulase activities to determine how this ratio responded to climatic factors, soil properties and substrates, and microbial abundances across the forest ecosystems along the latitudinal gradient. Our findings showed that the average soil ligninase activity was 3.49 nmol h−1 g−1, whereas the average soil cellulase activity was 525.26 nmol h−1 g−1 across the forest ecosystems sampled. The average activity ratio of ligninase-to-cellulase in tropical forest ecosystems was 27.9% higher than that in subtropical forests and 64.2% higher than that in temperate forest ecosystems. The partial least squares path model demonstrated that the ligninase-to-cellulase activity ratio was significantly negatively correlated with soil substrates (r = -0.94, p < 0.001) and significantly positively correlated with microbial abundances (r = 0.38, p < 0.01). The variation partitioning analysis further revealed that soil substrates explained 19.4% variation regarding ligninase-to-cellulase ratio, whereas microbial abundance (fungal abundance) contributed 2.8%. This study provides crucial information about the distribution of enzyme ratios along the latitude gradient, highlights the microbial utilization of recalcitrant C pools in tropical forests, and provides an insight into the response of the global C cycle under a changing climate.

Comparison of two isotope-based methods used in determining forest evapotranspiration partitioning

A comprehensive understanding of the changing patterns and the contribution of each component of the forest ecosystem to evapotranspiration is necessary for an accurate assessment of biological quality, terrestrial ecosystem production, energy exchange, and water use efficiency. In this study, two methods (i.e., the sap-flow and lysimeter methods) were used to partition evapotranspiration (ET) into evaporation(E) and transpiration(T) components using stable isotope techniques. The factors affecting the contribution of T to ET (FT) were evaluated. The results showed that FT presents seasonal variation patterns throughout the year. Both methods accurately simulated the ET partitioning and its seasonal variation. The ET value estimated by the sap-flow method was less than that estimated by the lysimeter method. In contrast, the simulated value of T calculated using the lysimeter method was higher than that calculated by the sap-flow method. However, this was within allowable limits. The lysimeter method is more accurate and suitable for monitoring forest ecosystem evapotranspiration in practical experiments. This study provides a technical reference for the optimal calculation method of ET partitioning using a stable isotope-based technique. It provides baseline information to better understand water vapour exchange in forest ecosystems in China.