Deciduous Broad-leaved Forest Research Articles

Vegetation and environmental changes on the Northeast China Plain during warm periods since MIS 3

Understanding past terrestrial ecosystem responses to climate changes is vital for future predictions, but research in densely populated plains is limited due to insufficient materials. This study focuses on vegetation and environmental changes in the Northeast China Plain since Marine Isotopic Stage (MIS) 3 using pollen analysis from core WCZK03. The findings reveal significant shifts in vegetation that correspond to climatic events. During MIS 3 (52–29 cal ka BP), the region was predominantly a lake environment with vegetation transitioning from grassland dominated by Poaceae, Chenopodiaceae and Artemisia to conifer-broadleaf mixed forests as the climate ameliorated. The Last Glacial Maximum (29–17 cal ka BP) was characterized by loess deposits, followed by drought-tolerant grassland (17.0–11.3 cal ka BP) dominated by Artemisia and Chenopodiaceae in the plain. The onset of the Holocene witnessed the expansion of conifer and broad-leaved deciduous forests in hilly areas and the retreat of grassland in the plain. The sedimentary sequence shows transitions from fluvial-lacustrine deposits to loess-like and black soil deposits, showing significant environmental changes. This study suggests that changes in vegetation on the Northeast China Plain were closely related to regional climate patterns and were more responsive to climate changes than the surrounding mountainous areas.

High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

Influences of climate change on carbon and water fluxes of the ecosystem in the Qinling Mountains of China

Climate change is one of the foremost challenges confronting the contemporary world. Carbon and water cycles and their interconnections of ecosystems are playing a pivotal role in assessing the impacts of climate change on ecosystems. The Qinling Mountains of China represent a focal area for research on global climate change and regional adaptation strategies. Based on historical and CMIP6 (Coupled Model Intercomparison Project Phase 6) future climatic data, the Biome-BGC model, which was sufficiently optimized with the Biome-BGC-PEST package, was used to simulate the historical and future trends of GPP (Gross primary productivity), ET (Evapotranspiration), and WUE (Ecosystem water use efficiency) in the Qinling Mountains, so as to elucidate the dynamics and spatiotemporal variations of carbon and water fluxes amidst climate change. The results showed that the GPP, ET, and WUE of the Qinling Mountains heterogeneously distributed in space, due to the dramatic disparities in carbon and water fluxes among different vegetation types in the Qinling Mountains. Among the various vegetation types, evergreen needle-leaved forest had the highest annual mean value of WUE. In the historical period of 1979-2018, deciduous broad-leaved forest had the highest annual mean value of GPP and ET, while evergreen needle-leaved forest would dominate in the future (2021-2100). For the annual GPP and ET flux variations, different vegetation cover types followed varying peak patterns in the Qinling Mountains. Deciduous broad-leaved forest, evergreen needle-leaved forest, and shrub meadow showed ’single-peak’ pattern annually, whereas the crop area displayed ’double-peak’ pattern due to the different growing seasons of crops. There also were temporal variations for the carbon and water fluxes within the same vegetation type. Future projections suggested a continuous increase in GPP and ET fluxes, underscoring the consistent role of the Qinling Mountains in water resource conservation and carbon sink amid evolving climatic conditions.

Comparative Analysis of Bacterial Community Structures in Earthworm Skin, Gut, and Habitat Soil across Typical Temperate Forests.

Earthworms are essential components in temperate forest ecosystems, yet the patterns of change in earthworm-associated microbial communities across different temperate forests remain unclear. This study employed high-throughput sequencing technology to compare bacterial community composition and structure in three earthworm-associated microhabitats (skin, gut, and habitat soil) across three typical temperate forests in China, and investigated the influence of environmental factors on these differential patterns. The results indicate that: (1) From warm temperate forests to cold temperate forests, the soil pH of the habitat decreased significantly. In contrast, the physicochemical properties of earthworm skin mucus exhibited different trends compared to those of the habitat soil. (2) Alpha diversity analysis revealed a declining trend in Shannon indices across all three microhabitats. (3) Beta diversity analysis revealed that the transition from warm temperate deciduous broad-leaved forest to cold temperate coniferous forest exerted the most significant impact on the gut bacterial communities of earthworms, while its influence on the skin bacterial communities was comparatively less pronounced. (4) Actinobacteria and Proteobacteria were the predominant phyla in earthworm skin, gut, and habitat soil, but the trends in bacterial community composition differed among the three microhabitats. (5) Mantel tests revealed significant correlations between bacterial community structures and climatic factors, physicochemical properties of earthworm habitat soil, and physicochemical properties of earthworm skin mucus. The findings of this study offer novel perspectives on the interplay between earthworms, microorganisms, and the environment within forest ecosystems.

Investigation of classification of support vector machine on the estimation of the deciduous broad-leaved forests area in the north of Iran

Investigation of classification of support vector machine on the estimation of the deciduous broad-leaved forests area in the north of Iran

Frass deposition from pest outbreaks affects soil organic carbon and its relationship with environmental factors in a deciduous broad-leaved forest

Forest defoliators are one of the major biological disturbances to forest ecosystems. As one of the abnormal nutrient input paths into forest ecosystems, frass deposition from the pest outbreak plays a critical role in regulating soil organic carbon (SOC) in forest ecosystems. However, how frass deposition affects SOC and its fractions in forests remains unclear. Based on a severe outbreak of defoliator in an oak-sweetgum mixed forest in Jigong Mountain in 2014, we compared the difference in SOC between plots with and without frass deposition for 4 consecutive years. The results showed that frass deposition led to a significant increase of 25.1 % in soil microbial biomass C (MBC) and 32.0 % in dissolved organic C (DOC) in 2014, which further escalated to 50.4 % and 50.6 % in the subsequent year (2015), respectively. The response of SOC to frass deposition lagged behind MBC and DOC. Specifically, there was no change in SOC in 2014, but a significant increase (50.9 %) was observed in the subsequent 2–3 years. The positive dependences of MBC and DOC upon fine root biomass were negated under frass deposition, while the relationship between SOC and fine root biomass remained unaffected. Soil organic carbon and DOC showed non-linear responses to frass amount and the changed soil nitrogen content. Our finding that the response of SOC to frass deposition lagged behind soil labile C indicates that SOC exhibits a certain resilience towards forest disturbance. The findings also imply that investigating the long-term impacts of frass deposition on SOC in forests would contribute to the scientific assessment of forest C cycling under disturbance.

Sensitivity of temperate vegetation to precipitation is higher in steppes than in deserts and forests

Recognizing the sensitivity of vegetation to precipitation is fundamental to monitoring and assessing ecosystem health and predicting ecosystem response to climate change. However, there is a lack of detailed, large-scale assessment of the sensitivity of temperate vegetation to climate variability. We analyzed the spatiotemporal trends of NDVI in Inner Mongolia (IM) for the period from 2000 to 2020, evaluated the sensitivity of NDVI to precipitation by constructing a linear mixed-effects model, and assessed the relative importance of an array of biotic and abiotic factors in affecting the vegetation sensitivity to precipitation using random forest models. We found that (1) the NDVI in IM had a significant or slight increase in 80.86% of the region during the studied two decades, though it showed a large inter-annual fluctuation, and the areas with stable or decreasing NDVI were mainly in the desert areas. Precipitation and wind speed were the two major drivers for the NDVI changes. (2) The typical steppes demonstrated the highest sensitivity to precipitation, followed by desert steppes and forest steppes, broad-leaved forests, and deserts and coniferous forests. (3) The major factor affecting the vegetation sensitivity to precipitation varied with vegetation types; it is temperature in the coniferous forests, forest steppes and steppe deserts, but it is the sunshine hours in the deciduous broad-leaved forests, the typical steppes, and the desert steppes. The high sensitivity of the typical steppe vegetation to precipitation is related with its location on climate gradient, it has a high production potential but limited by precipitation under a semiarid climate. Our analysis deepens the understanding of the NDVI sensitivity to precipitation and its influencing factors in different vegetation regions, and is valuable for predicting vegetation changes and developing vegetation management strategies to adapt to climate changes.

Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning

As an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.

The Effects of Different Vegetation Restoration Models on Soil Quality in Karst Areas of Southwest China

Rocky desertification is a devastating process in Karst areas of Southwest China and induces serious fragmentation in ecosystems. Therefore, vegetation restoration and the scientific evaluation of soil quality are key restorative strategies in these areas. In this study, a natural closed forest and a disturbed forest with three restoration models, including an evergreen broad-leaved forest, mixed forest, and deciduous forest, were investigated in Huanjiang County. More than nineteen soil properties (including physical, chemical, and biotic properties) were analyzed across treatments, and principal component analyses (PCA) were combined with a minimum data set (MDS) applied to evaluate the soil quality. Our study sought to identify a vegetation restoration model to improve the soil quality in this area. We demonstrated that soil physical and chemical properties, microbial biomass, and enzyme activities significantly differed across all of the models. Soil water content, capillary porosity, total porosity, organic carbon, total phosphorus, available phosphorus, and urease activity were high in the mixed forest, leading to better physical soil properties. Also, relatively high soil total nitrogen, total potassium, available nitrogen, available potassium, microbial biomass C and N, catalase, sucrose, and alkaline phosphatase levels were observed in the deciduous broad-leaved forest, resulting in improved soil chemical properties. Based on the minimum data set (MDS) method, six indicators, including non-capillary porosity, organic carbon, total phosphorus, pH, microbial biomass nitrogen, and urease activity, were selected to evaluate the soil quality across the models. Our data showed that, among the five models, the deciduous broad-leaved forest had the highest soil quality index (0.618), followed by the mixed forest (0.593). Stepwise regression analysis showed that soil organic carbon explained 79.9% of the variations in the soil quality indices, suggesting it was a major factor affecting the soil quality. Thus, vegetation restoration models mainly comprised of native tree species effectively improved the soil quality in Karst rocky desertification areas, with deciduous broad-leaved forests displaying the best effects, followed by mixed forests.

Effects of the Co-Application of Glucose, Nitrogen, and Elevated Temperature on Buried Black Soil Carbon in a Cool Temperate Deciduous Broad-Leaved Forest

Accurately predicting the feedback mechanisms between forest ecosystem carbon cycling and climate change is crucial for effective climate mitigation. Understanding soil organic carbon (SOC) responses to the combined impacts of plant biomass, litter, and nitrogen deposition, especially regarding temperature sensitivity, is essential but remains poorly understood. We conducted incubation experiments using buried black soil from a cool temperate deciduous broad-leaved forest in Japan, which has high C content and a highly stable molecular structure. The stepwise addition of glucose and a temperature increase from 15 to 35 °C accelerated SOC mineralization by 74.0 mg C kg−1 with a positive priming effect (PE) during the 49-day incubation period, while the simultaneous addition of nitrogen had no significant effect on this phenomenon, with SOC mineralization measured at 75.5 mg C kg−1. Conversely, glucose mineralization was significantly accelerated by 10%, from 241.0 to 261.3 mg C kg−1, by stepwise nitrogen addition and temperature increase. Under the combined impacts, the Q10 value of the soil increased significantly from 1.6 to 2.0 compared to that in the unmodified conditions, primarily due to the stepwise addition of glucose. We also found a strong positive correlation between activation energy (Ea) and Q10. This result strongly supports the carbon quality–temperature (CQT) hypothesis. These results likely stem from interactions between SOC quality and carbon availability, suggesting that, in the future, climate change is likely to have a positive feedback effect, especially on buried black soils.

Response of Stand Spatial Structure to Nitrogen Addition in Deciduous Broad-Leaved Forest in Jigong Mountain

Significant influences on tree growth and forest functionality are attributed to nitrogen (N) addition. However, limited research has been conducted on the effects of N addition on forest spatial structure. In this study, we examined the effects of different N addition methods and concentrations on the stand spatial structure of a deciduous broad-leaved forest over the period 2012 to 2017. Five N addition treatments were implemented: CK (control group without N addition), CN25 (low N concentration added to the canopy), CN50 (high N concentration added to the canopy), UN25 (low N concentration added to the understory), and UN50 (high N concentration added to the understory). The results showed a moderate influence of N addition (CN25, CN50, UN25, UN50) on optimizing the stand spatial structure. CN25, CN50, and UN25 increased the mean values of the mingling degree (M) and neighborhood comparison (U), while decreasing the mean value of the uniform angle index (W), although these effects were not significant. Enhancements in the average value of the crowding degree (C) and comprehensive spatial structure index (CSSI) between 2012 and 2017 were found in all five treatments, demonstrating statistical significance. Assessing the distribution of the stand spatial structure index, CN25, CN50, and UN25 increased the proportion of M at an intensity (M = 0.75) and extreme intensity (M = 1), while decreasing the proportion at zero intensity (M = 0), weak intensity (M = 0.25), and moderate intensity (M = 0.5). A decrease in the proportion of trees was noted when U = 0 (excluding UN50), with no discernible pattern found in the frequency distribution of other values. CN50 and UN25 increased the proportion of W at a moderate level (W = 0.5), while CN25 and UN50 reduced it. No clear pattern was detected in the frequency distributions of other values. All five treatments increased the proportion of C at the maximum level (C = 1), while decreasing the proportions at levels of 0, 0.25, and 0.5 in 2017. Intriguingly, nitrogen addition treatments appeared to optimize the stand spatial structure to some extent and stimulated the growth of trees with larger diameters. Nevertheless, the short duration of the data collection period, spanning only five years, may have influenced the significance of the outcomes, underlining the requirement for extended studies. Conclusively, N deposition adjusted and enhanced the stand spatial structure to various degrees within the research region, providing valuable insights for further optimization of forest management.

Holocene Vegetation Dynamics Revealed by a High-Resolution Pollen Record from Lake Yangzonghai in Central Yunnan, SW China

Long-term regional vegetation dynamics is essential for the understanding of past land cover changes. High-resolution pollen analysis of a 1020 cm core from a large lake, Lake Yangzonghai (YZH), in central Yunnan, SW China, was conducted to reveal regional vegetation dynamics in the lake catchment over the past 13,400 years. Pollen record, principal component analysis (PCA) of pollen percentages of major arboreal taxa, and plant abundances estimated from the “Regional Estimates of VEgetation Abundance from Large Sites” (REVEALS) model show five successional stages of vegetation dynamics since 13,400 cal. a BP: regional vegetation with high coverages in the lateglacial (13,400–11,400 cal. a BP) was dominated by evergreen broadleaved forest (EBF) and deciduous broadleaved forest (DBF), together with some grass meadows and marshes; pine forest and alder forest expanded in the early Holocene (11,400–9000 cal. a BP) when vegetation coverages were still high; regional vegetation with low coverages was dominated by sweetgum forest, together with some pine forest during the mid-Holocene (9000–4200 cal. a BP); more pine forest, grass meadows and marshes occupied the lake catchment during the late Holocene (4200–800 cal. a BP), when vegetation coverages were higher than the average of the past 13,400 years; regional vegetation with low coverage was dominated by grass meadows and marshes, great deforestation happened in the last 800 years. Regional vegetation dynamics over the past 13,400 years in the Lake YZH catchment was the result of regional vegetation response to climate changes during the lateglacial and early–mid Holocene, and to human activities mainly during the late Holocene.

The Relationship between Trait-Based Functional Niche Hypervolume and Community Phylogenetic Structures of Typical Forests across Different Climatic Zones in China

Functional traits are pivotal for understanding the functional niche within plant communities. Yet, the relationship between the functional niches of typical forest plant communities across different climatic zones, as defined by functional traits, and their association with community and phylogenetic structures remains elusive. In this study, we examined 215 woody species, incorporating 11 functional traits spanning leaf economy, mechanical support, and reproductive phenology, gathered from forests in four climatic zones from tropical, subtropical, warm-temperate to cold-temperate zones in China and supplemented by the literature. We quantified the functional niche hypervolume (FNH), reflecting the multidimensional functional niche variability. We then probed into the correlation between the FNH and community and phylogenetic structures of forests. Our findings reveal that species richness significantly influences the geographic variance of functional niche space in forest vegetation across different climatic zones. Specifically, a community’s species richness correlates positively with the functional niche breadth occupied by the community species. The FNH of woody plants across diverse forest types shows significant associations with both the mean phylogenetic distance (MPD) and the mean nearest phylogenetic taxon distance (MNTD) of the communities. There is a progressive increase in tropical rainforest (TF), subtropical evergreen deciduous broad-leaved mixed forest (SF), and warm-temperate coniferous broad-leaved mixed forest (WF), followed by a decline in the cold-temperate coniferous forest (CF). This pattern suggests potential environmental filtering in CF, which may constrain the spatial extent of plant functional niches. Our research underscores the substantial variability in the FNH across China’s typical forest vegetation, highlighting the complex interplay between functional traits, community richness, and phylogenetic distance.

Effect of Altitude Gradients on the Spatial Distribution Mechanism of Soil Bacteria in Temperate Deciduous Broad-Leaved Forests.

Soil bacteria are an important part of the forest ecosystem, and they play a crucial role in driving energy flow and material circulation. Currently, many uncertainties remain about how the composition and distribution patterns of bacterial communities change along altitude gradients, especially in forest ecosystems with strong altitude gradients in climate, vegetation, and soil properties. Based on dynamic site monitoring of the Baiyun Mountain Forest National Park (33°38'-33°42' N, 111°47'-111°51' E), this study used Illumina technology to sequence 120 soil samples at the site and explored the spatial distribution mechanisms and ecological processes of soil bacteria under different altitude gradients. Our results showed that the composition of soil bacterial communities varied significantly between different altitude gradients, affecting soil bacterial community building by influencing the balance between deterministic and stochastic processes; in addition, bacterial communities exhibited broader ecological niche widths and a greater degree of stochasticity under low-altitude conditions, implying that, at lower altitudes, community assembly is predominantly influenced by stochastic processes. Light was the dominant environmental factor that influenced variation in the entire bacterial community as well as other taxa across different altitude gradients. Moreover, changes in the altitude gradient could cause significant differences in the diversity and community composition of bacterial taxa. Our study revealed significant differences in bacterial community composition in the soil under different altitude gradients. The bacterial communities at low elevation gradients were mainly controlled by stochasticity processes, and bacterial community assembly was strongly influenced by deterministic processes at middle altitudes. Furthermore, light was an important environmental factor that affects differences. This study revealed that the change of altitude gradient had an important effect on the development of the soil bacterial community and provided a theoretical basis for the sustainable development and management of soil bacteria.

Analysis of Spatial Differentiation of NDVI and Climate Factors on the Upper Limit of Montane Deciduous Broad-Leaved Forests in the East Monsoon Region of China

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m−2/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10−3/a). NDVI’s lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation’s vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation’s diverse adaptability to climatic variations.

Analysis of vertical differentiation of vegetation in Taishan World Heritage site based on cloud model

While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan’s deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model—Expectation (EX), Entropy (EN), and Hyper-entropy (HE)—we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan’s forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82–39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79–7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan’s vegetation vertical differentiation.

The Nitrogen Cycle of a Cool-Temperate Deciduous Broad-Leaved Forest

The nitrogen (N) cycle, a major biogeochemical cycle in forest ecosystems, notably affects ecosystem multifunctionality. However, the magnitude and role of organic N and the snow season remain uncertain in this cycle. We assessed the N flux and pool data of a temperate deciduous broad-leaved forest to clarify N cycle processes. The results showed that the most important component of the N pool was the soil N pool. The N demand of the site amounted to 139.4 kg N ha−1 year−1 and was divided into tree production (83.8%) and bamboo production (16.2%). We clarified that retranslocation (37.4%), mineralization at a soil depth of 0–5 cm (15.3%), litter leachate (4.6%), throughfall (2.3%), and canopy uptake (0.5%) provided 60.1% of the N demand. In terms of soil at 0–5 cm in depth, the net mineralization rate during the snow season contributed to 30% of the annual mineralization. We concluded that the study site was not N-saturated as a result of a positive N input–output flux budget. More than half of the total N was accounted for by dissolved organic N flowing through several pathways, indicating that organic N plays a vital role in the cycle. The mineralization rate in the soil layer during the snow season is an important link in the N cycle.

Eco-physiological adaptation strategies of dominant tree species in response to canopy and understory simulated nitrogen deposition in a warm temperate forest

Nitrogen (N) is an indispensable element for plant growth and survival. The increased global N deposition due to human activities has aroused attention to forest ecosystems, but most studies have not considered the process of N interception, assimilation, and leaching in forest canopy. To investigate the responses and adaptive strategies of dominant tree species in a warm temperate forest to atmospheric N deposition, this study implemented five treatments: control treatment, canopy simulated low N deposition, canopy simulated high N deposition, understory simulated low N deposition, and understory simulated high N deposition. The morphological and physiological traits and nutrient uptake strategies of three dominant tree species (Liquidambar formosana, Quercus acutissima, and Quercus variabilis) in warm temperate deciduous broad-leaved forest were studied under different forms and levels of stimulated N deposition. The results showed that the specific leaf area (SLA) of the three dominant tree species decreased under high N application. To optimize resource absorption and improve nutrient transport rates, the specific root length (SRL) and specific root area (SRA) of the three tree species tended to decrease, while the average root diameter (ARD) increased. Under canopy N deposition (CAN), the contents of carbon (C), N, total chlorophyll (Tchl), and total amino acids in Q. variabilis leaves were lower than those under understory N deposition (UAN) treatment. Furthermore, principal component analysis (PCA) revealed the differences between different genera (L. formosana and Q. variabilis, L. formosana and Q. acutissima) under various N treatments, and there was no significant difference between the same genus (Q. variabilis and Q. acutissima). The results indicated that the eco-physiological characteristics of different tree species under N deposition treatment were species-specific, and traditional UAN perhaps exaggerated the effect of atmospheric N deposition on tree growth characteristics. Our study considers the retention and redistribution of forest canopy, which is more in accordance with the natural N deposition, and helps us to understand the eco-physiological responses of dominant tree species in forest ecosystem to atmospheric N deposition.

Interactions between leaf traits and environmental factors help explain the growth of evergreen and deciduous species in a subtropical forest

It is well known that evergreen and deciduous species possess different functional traits and utilize different strategies in growth and adaptation to environments. However, little work has been done to elucidate whether leaf habits mediate the effect of trait-environment interactions on plant performance. In this study, our subjective was to illuminate whether relative growth rate of deciduous and evergreen species is influenced by multiple trait-environment interactions. We conducted measurements on eight leaf traits of 1230 individuals belonging to 25 species in a subtropical evergreen and deciduous broad-leaved mixed forest. Additionally, we collected data on topographic factors, edaphic variables and competition index. Subsequently, we employed generalized linear mixed model to analyze plant relative growth rate, considering high-order trait-environment interactions for both evergreen and deciduous species. We also visualized the effects of these interactions on growth patterns. Our results showed that leaf habits were divided by trait PC1 (41.8%) which was related to leaf lifespan and resource acquisition. Evergreen species tended to have greater interspecific variation compared to deciduous species. Notably, the inclusion of trait-environment interactions significantly improved growth predictions for both leaf habits, although explanatory power of deciduous models was always higher than that of evergreen species. Furthermore, we observed variability in the effects of trait-environment interactions on plant performance varied between leaf habits, leading to different optimal models for each leaf habit, even when they shared similar trait-environment context. These results indicated that difference of life history strategies between leaf habits could be reflected by trait-environment interactions. We emphasized the importance of leaf habits in explaining forest productivity and functions, and future research should focus on the effects of leaf habits on other demographic metrics to understand species coexistence in mixed forests.

Restoring Subtropical Forests: Alleviating P Limitation and Introducing C Limitation Using Evergreen Broad-Leaved Tree Species

Determining which species to utilize for the artificial restoration of subtropical secondary forests has become a focal point in forestry and ecology. To compare the effects of the subtropical secondary forest artificial restoration model on soil microbial nutrient acquisition and limitation, we examined secondary forests (CKs), evergreen coniferous forests (GCPs), evergreen coniferous mixed broad-leaved forests (GCBMs), evergreen mixed broad-leaved forests (GBMs), and natural deciduous broad-leaved mixed forests (DBMs) as research subjects. Among them, GCPs, GCBMs, and GBMs were dominated by the species of the early, middle, and climax stages of subtropical forest succession, respectively. The activities and stoichiometry of β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid phosphatase (ACP) in the topsoil were analyzed. The results showed that the forest type significantly affects the activities of BG and LAP rather than NAG or ACP. The BG activity in DBMs was the lowest, while the LAP activity in CKs was significantly higher than that in plantations. Furthermore, the nutrient limitation of microbes was quantitatively analyzed by using the vector analysis of enzyme stoichiometry. The soil microbes in the study area were co-limited by C and P, and the nutrient limitation was in the order of C > P > N. Among the forests, the enzyme stoichiometric ratios in GCPs and DBMs were closest to 1:1:1. From CKs to GBMs, the microbial C limitation was increased, while the P limitation was decreased. The C limitation in DBMs was slightly lower than that in CKs. Overall, the P limitation in evergreen plantations was less than that in CKs. The soil bulk density, C/P, and N/P significantly influenced enzyme activities and stoichiometry. These results suggest that the artificial restoration of subtropical forests using evergreen species alleviated P limitation, while using deciduous broad-leaved species offered potential for alleviating microbial C limitation. Compared with evergreen broad-leaved species, employing pioneer and mid-successional or deciduous broad-leaved species can better achieve balanced microbial nutrient requirements.