Forest In Northeastern China Research Articles

Chemical Composition and Deposition Characteristics of Precipitation into a Typical Temperate Forest in Northeastern China

The chemical compositions and deposition characteristics of atmospheric precipitation affect the structure and function of forest ecosystems and reflect regional air quality. Although northeastern China constitutes a vital forested area, few relevant studies reveal the chemical composition and the nitrogen (N) and sulphur (S) deposition characteristics within precipitation. In this study, we monitor precipitation chemistry during 2018–2020 at a rural forested site in northeastern China (the Qingyuan site) and compare it with those from background sites (Mondy in Russia and Ochiishi in Japan) and highly anthropogenically influenced areas (Beijing). The precipitation pH range was 4.7–8.0 (volume-weighted average 6.2). The average concentration of total ions in precipitation was 459 μmol L−1, representing a moderate pollution level. Nitrate (NO3−, 73 μmol L−1) and ammonium (NH4+, 133 μmol L−1) were the major anions and cations in the precipitation. Total inorganic nitrogen (TIN) deposition was 12.3–15.9 kg N ha−1 year−1 (NH4+-N deposition accounted for 54–67%), lower than the average level in China (19.4 kg N ha−1 year−1). Annual precipitation sulphate (SO42−) deposition was 4.9–6.7 kg S ha−1 year−1. Seventy-two percent of the precipitation ions at our site originated from human activities. This work has revealed that N and S deposition is an important ion deposition component in atmospheric precipitation in the study of temperate forests in northeastern China. Nitrogen deposition, as a source of vital nutrients in the forest ecosystem, may promote forest growth and, thereby, forest carbon sequestration.

Effects of nitrogen deposition on carbon allocation between wood and leaves in temperate forests

Societal Impact StatementIncreasing atmospheric nitrogen (N) deposition represents a major global change factor, but its long‐term effect on tree growth and carbon (C) sequestration remains uncertain. Our manipulation experiment and meta‐analysis reveal that N deposition in temperate and boreal forests promoted tree growth and the allocation of more C into wood than into leaves in China and worldwide. Thus, N deposition may increase forest C sequestration through enhanced wood production and distribution of C into stable sinks. In the context of achieving “Carbon Neutrality,” understanding how N deposition affect long‐term forest C sinks will help us with mitigation strategies under climate change.Summary Increased nitrogen (N) deposition is driving many temperate and boreal forests in the Northern Hemisphere towards N saturation. However, it is uncertain how long‐term N deposition affects tree growth and carbon (C) allocation in these forests. To investigate this, we treated temperate larch and mixed forests in northeastern China with N additions for 8 years. In addition, we collected data from 25 N‐addition experiments in temperate and boreal forests worldwide to reveal the overall effects of N on tree growth and C allocation. Nitrogen additions significantly promoted total biomass increment by 24% in both study forests, with on average additional 8 kg C per kg N gain into woody biomass over the study period. Nitrogen additions increased the ratio of woody biomass increment to foliage litterfall production in the larch forest (by 34%). Literature data analysis also revealed greater N promotion on wood (24%) over foliage (9%) production. However, the positive effect on foliage diminished over time. These results combined imply that N deposition may promote tree growth in temperate and boreal regions and drive proportionally more photosynthate allocation into wood than leaves, thus may enhance forest C sequestration in the long run.

Climate-sensitive tree height-diameter models for mixed forests in Northeastern China

Models based on height-diameter (H-D) are helpful for estimating forest biomass and carbon storage on a large scale. Climate change has an impact on allometric height-diameter growth. H-D models explaining climatic effects are still inadequate due to the complexity of mixed forests. This paper developed climate-sensitive mixed-effects models for 14 tree species based on 316 natural mixed forest plots in Northeastern China and compared the response of tree height growth to environmental gradients for multiple species. The results showed that the species heterogeneity leads to the differences in scaling exponents in varying degrees. The analysis revealed that incorporating mean annual temperature (MAT), mean annual precipitation (MAP), dominant tree height (HT), basal area of the larger trees (BAL), and Shannon diversity index significantly enhanced the predictive ability of the model. By exploring the tree height change patterns of each tree species under different gradients of environmental factors, we found that increases in temperature, precipitation, site quality, and competition intensity positively affected height growth in mixed forests. In contrast, increases in species diversity inhibited height growth. The degree to which environmental factors regulate the H-D allometric growth of trees in mixed forests is related to species specificity. This paper provides theoretical support for tree height estimates of major tree species in mixed forests under global climate change by introducing environmental factors to improve the H-D model of 14 tree species.

El Niño–Southern Oscillation affects the species-level temporal variation in seed and leaf fall in a mixed temperate forest

El Niño–Southern Oscillation (ENSO), the variation between anomalously cold (La Niña) and warm conditions (El Niño), is one of the most prominent large-scale climate patterns with worldwide effects. Elevated seed and leaf fall has been found at the positive phase of ENSO (El Niño) in tropical forests. However, how seed and leaf fall respond to ENSO at species level is understudied, especially in temperate forests. In this study, we monitored seed and leaf fall at the species-level at 150 points across a 25-ha temperate forest in northeastern China over a span of 12 years. Using time series and wavelet analyses, we assessed three hypotheses: 1) temperate tree species' seed and leaf fall are strongly, but differently, correlated with ENSO and, 2) community synchrony in seed and leaf occurred both at seasonal and ENSO scales; finally, 3) local climatic modulated the effects of ENSO on seed and leaf fall. We found that ENSO was significantly correlated with seed and leaf fall of all species, although correlation strength varied across species (r = 0.206–0.658). Specifically, ENSO indices (ENSO12 or ENSO34) accounted for the most variation in seed and leaf fall of Acer pseudo-sieboldianum (40 % and 34 %, respectively) and ranged 4 %–31 % in all other species. Leaf fall was synchronous with ENSO cycles with a period of 2–7 years, but community synchrony of seed fall was only detected at seasonal scales. ENSO influenced seed fall of Fraxinus mandshurica and Tilla amurensis by mediating rainfall and relative humidity, respectively, highlighting the interactive effects of local climate and ENSO. Our findings highlight the potential effects of ENSO on ecosystems outside of tropical regions and improve our ability to predict regeneration dynamics and nutrient cycling of temperate forests under the context of global change.

Tree mycorrhizal type mediates conspecific negative density dependence effects on seedling herbivory, growth, and survival.

Tree mycorrhizal type plays an important role in promoting plant species diversity and coexistence, via its mediating role in conspecific negative density dependence (CNDD), i.e., the process by which an individual's performance is impaired by the density of conspecific plants. Previous findings suggest that ectomycorrhizal (EM) tree species are generally less susceptible to CNDD than arbuscular mycorrhizal (AM) tree species, due to the chemical and physical protection that EM fungi provide their host with. We examined how CNDD effects on leaf herbivory, seedling growth, and survival differ between AM and EM seedlings of ten tree species collected over 3years in an old-growth temperate forest in northeastern China. We found that AM and EM seedlings differed in how conspecific density affected their leaf herbivory, seedling growth, and survival. Specifically, AM seedlings leaf herbivory rates significantly increased with increasing conspecific seedling and adult density, and their growth and survival rates decreased with increasing conspecific adult density, these patterns were, however, absent in EM seedlings. Our work suggests that AM seedlings have a performance disadvantage relative to EM seedlings related to the negative effects from conspecific neighbors. We highlight the importance of integrating information on seedling leaf herbivory, seedling growth, to provide further understanding on potential mechanisms driving differences in CNDD between AM and EM tree seedlings.

Unravelling Trait-Environment Relationships at Local and Regional Scales in Temperate Forests.

Understanding the trait–environment relationships has been a core ecological research topic in the face of global climate change. However, the strength of trait–environment relationships at the local and regional scales in temperate forests remains poorly known. In this study, we investigated the local and regional scale forest plots of the natural broad-leaved temperate forest in northeastern China, to assess what extent community-level trait composition depends on environmental drivers across spatial scales. We measured five key functional traits (leaf area, specific leaf area, leaf carbon content, leaf nitrogen content, and wood density) of woody plant, and quantified functional compositions of communities by calculating the “specific” community-weighted mean (CWM) traits. The sum of squares decomposition method was used to quantify the relative contribution of intraspecific trait variation to total trait variation among communities. Multiple linear regression model was then used to explore the community-level trait–environment relationships. We found that (i) intraspecific trait variation contributed considerably to total trait variation and decreased with the spatial scale from local to regional; (ii) functional composition was mainly affected by soil and topography factors at the local scale and climate factor at the regional scale, while explaining that variance of environment factors were decreased with increasing spatial scale; and (iii) the main environment driver of functional composition was varied depending on the traits and spatial scale. This work is one of the few multi-scale analyses to investigate the environmental drivers of community functional compositions. The extent of intraspecific trait variation and the strength of trait–environment relationship showed consistent trends with increasing spatial scale. Our findings demonstrate the influence of environmental filtering on both local- and regional-scale temperate forest communities, and contribute to a comprehensive understanding of trait–environment relationships across spatial scales.

Additive Root Biomass Equations for Betula platyphylla Suk. Plantations in Northeast China

Most of the forest biomass models that have been developed so far focus on the study of the aboveground biomass of forest trees and the prediction of belowground biomass remains obviously insufficient. Moreover, most of the existing studies on the estimation of the belowground biomass of trees have considered roots as a whole, ignoring the differences in composition and function of roots within different diameter classes. In this study, we measured the root biomass of birch plantation forests in northeastern China using extensive destructive sampling, in which we divided the root system into three parts: coarse, medium, and fine roots. We selected the best model base form from three common allometric biomass equations and determined the most appropriate error structure for the two sets of models using likelihood comparisons. The additive and disaggregated models were fitted using maximum likelihood with open-source software. We also added the site factor as a dummy variable into the two models. Finally, the competency of the two models was tested using ten-fold cross-validation. The results showed that both models could provide relatively accurate estimates of birch root biomass but that the disaggregated model performed slightly better than the additive model.

Timing of leaf fall and changes in litter nutrient concentration compromise estimates of nutrient fluxes and nutrient resorption efficiency

Nutrient resorption in the canopy and return to soil with litterfall are two major nutrient processes in forested ecosystems. However, leaf-fall phenology and seasonal dynamics of canopy nutrient concentration have not been comprehensively quantified, compromising the confidence in estimates of litterfall nutrient fluxes and nutrient resorption efficiency. Here, we used high-frequency litter collections for 28 species in nine plots for a temperate deciduous broadleaved forest in northeastern China. Based on leaf-fall phenology and temporal shifts in nitrogen (N) and phosphorus (P) concentrations in leaf litter for major tree species, we quantified the errors in canopy nutrient flux and resorption efficiency for 15 species and nine plots. The day of year (DOY) of start of leaf-fall, DOY of peak leaf-fall, and the length of leaf-fall period differed dramatically among tree species. Concentrations of N and P in leaf litter usually declined during the summer and autumn seasons. Annual nutrient fluxes in total canopy litterfall at the ecosystem level were 65.70 ± 6.14 kg N ha−1 and 4.70 ± 0.80 kg P ha−1 of which leaf litterfall accounted for 78 and 76%, respectively. The N (NRE) and P (PRE) resorption efficiencies at the ecosystem level calculated based on the percentage of nutrient pool resorbed from canopy leaves were 49.8 ± 3.9 and 48.0 ± 7.1%, respectively. Calculating nutrient fluxes with species-specific nutrient concentration at the DOY of peak leaf-fall (Nupeak) underestimated N and P fluxes by an average of 11 and 14%; the corresponding errors in N and P fluxes estimated using plot-specific Nupeak were –5 and –7%. In addition, NRE and PRE for major species based on Nupeak were overestimated by an average of 9 and 12%, and those for nine plots by 11 and 17%; using plot-specific Nupeak lowered mean errors in NRE and PRE to 10 and 11%. These results indicate that estimating canopy nutrient fluxes and resorption efficiency using litterfall collected at the DOY of peak leaf-fall can lead to non-negligible errors, and species-specific combined litterfall collected at and slightly before the DOY of peak leaf-fall may be a better metric for estimating nutrient flux and resorption efficiency at both species and ecosystem scales.

Topography, Diversity, and Forest Structure Attributes Drive Aboveground Carbon Storage in Different Forest Types in Northeast China

Forests regulate air quality and respond to climate change by storing carbon. Assessing the driving factors of forest aboveground carbon (AGC) storage is of great importance for forest management. We assumed that different forest types would affect the relationship between species richness, stand density, individual tree size variation, and AGC. In order to test and verify it, we analyzed the inventory data of 206 fixed plots (20 m × 20 m) of Jingouling Forest Farm, taking advantage of the piecewise structural equation model (pSEM) to explore the effects of species diversity, stand structure attributes, and topography on the AGC storage in the Wangqing Forest in Jilin Province. In addition, in this study, we aimed to investigate whether the fixed factors (species diversity, stand structure attributes, and topography) influenced AGC storage more significantly than the random factor (forest type). According to the results of pSEM, the selected factors jointly explain the impact on 33% of AGC storage. The relationship between stand density and AGC is positive, and the impact of individual tree size variation on AGC storage is negative. Species richness has direct and indirect impacts on AGC storage, and the indirect impact is more significant through individual tree size variation. Both elevation and slope are significantly negatively associated with AGC storage. Forest type explains the impact on 12% of AGC storage, which means the relationship between AGC and predictors varies across forest types. The results provide a scientific basis for the protection and management decision of natural forests in northeastern China.

The Right-Skewed Distribution of Fine-Root Size in Three Temperate Forests in Northeastern China.

Trees can build fine-root systems with high variation in root size (e.g., fine-root diameter) and root number (e.g., branching pattern) to optimize belowground resource acquisition in forest ecosystems. Compared with leaves, which are visible above ground, information about the distribution and inequality of fine-root size and about key associations between fine-root size and number is still limited. We collected 27,573 first-order fine-roots growing out of 3,848 second-order fine-roots, covering 51 tree species in three temperate forests (Changbai Mountain, CBS; Xianrendong, XRD; and Maoershan, MES) in Northeastern China. We investigated the distribution and inequality of fine-root length, diameter and area (fine-root size), and their trade-off with fine-root branching intensity and ratio (fine-root number). Our results showed a strong right-skewed distribution in first-order fine-root size across various tree species. Unimodal frequency distributions were observed in all three of the sampled forests for first-order fine-root length and area and in CBS and XRD for first-order fine-root diameter, whereas a marked bimodal frequency distribution of first-order fine-root diameter appeared in MES. Moreover, XRD had the highest and MES had the lowest inequality values (Gini coefficients) in first-order fine-root diameter. First-order fine-root size showed a consistently linear decline with increasing root number. Our findings suggest a common right-skewed distribution with unimodality or bimodality of fine-root size and a generalized trade-off between fine-root size and number across the temperate tree species. Our results will greatly improve our thorough understanding of the belowground resource acquisition strategies of temperate trees and forests.

Shift from Acquisitive to Conservative Root Resource Acquisition Strategy Associated with Increasing Tree Age: A Case Study of Fraxinus mandshurica

Tree age has an important effect on the form and function of fine roots. Previous studies have focused on the variations in root morphological and chemical traits among tree ages, while less attention has been given to the physiological traits, impeding a full understanding of the relationship between root resource acquisition strategy and tree age. Here, we measured root morphological (diameter, specific root length, specific root area and tissue density), chemical (nitrogen concentration) and physiological (respiration and exudation rate) traits of young, middle-aged and mature trees of Fraxinus mandshurica in a temperate secondary forest in northeastern China. Our overall aim was to determine how root traits and related resource acquisition strategy change with tree age. The results showed that from young to mature trees, root diameter gradually increased, but specific root length, specific root area, root nitrogen concentration, respiration and exudation rates all decreased, and the significant differences were mainly found between young and mature trees. Pearson’s correlation analysis revealed that the relationships of root respiration and exudation rates to root morphological and chemical traits depended on tree age and the specific traits examined, but these correlations were all significant except for root tissue density when the data were pooled across all tree age classes. Principal component analysis (PCA) showed that the conservative traits represented by root diameter, and the acquisitive traits such as root respiration and exudation rates and related morphological and chemical traits, occupied two ends of the first axis, respectively, while root tissue density occupied one end of the second axis, partially confirming the conceptual framework of “root economics space”. Standardized major axis (SMA) analysis of root exudation and respiration rates showed that young trees allocated more root carbon flux to the formation of root exudation, compared to middle-aged and mature trees. Our findings suggest that root resource acquisition strategy in F. mandshurica appears to shift from an absorptive to conservative strategy associated with increasing tree age, which may have substantial consequences for individual growth and interspecific competition, as well as belowground carbon allocation in ecosystems.

Functional traits explain growth–mortality trade-offs in a mixed broadleaf-conifer forest in northeastern China

Functional traits explain growth–mortality trade-offs in a mixed broadleaf-conifer forest in northeastern China

Effects of biotic and abiotic drivers on the growth rates of individual trees in temperate natural forests

Understanding the driving factors for individual tree growth is of great significance to devise effective forest management strategies. Such information can be used to select appropriate tree harvesting times, and to provide valuable guidance for creating suitable local environmental conditions during the period of tree growth. However, currently it remains unclear how biotic and abiotic factors affect individual tree growth on large geographical scales. In the present study, based on a dataset from 457 field plots and the growth records of 7,560 individual trees in natural forests in northeastern China, the effects of tree size, local environmental conditions, and regional climate characteristics on the 5-year basal area increment (BAI) of the three dominant species (Quercus mongolica, Betula platyphylla, and Larix gmelinii) were investigated using nonlinear mixed-effects models. Although the magnitude and direction of the drivers’ effects on the BAI differed among the three species, the following variables explained major proportions of the variation (conditional R2 ranging from 0.57 to 0.66) in the BAI: 1) Diameter at breast height; 2) Mean temperature and precipitation during the growing seasons; and 3) Species mingling and the basal areas of larger trees. In addition, an interactive effect between temperature and precipitation was significant for Q. mongolica. On the basis of our results, we identified the most important variables among multiple drivers affecting the growth of individual trees, and developed individual tree growth models for the three species. Our results provide an initial basis for the future scientific management of some of the most important natural forest regions in the world.

Forest fire risk estimation in a typical temperate forest in Northeastern China using the Canadian forest fire weather index: case study in autumn 2019 and 2020.

China's forest cover has increased by approximately 10% as a result of sustainable forest management since the late 1970s. The forest ecosystem area affected by fire is increasing at an alarming rate of approximately 600,000 ha per year. The northeastern part of China, with a forest cover of 41.6%, has the greatest percentage of acres affected by forest fires. This study combines field and satellite weather data to determine factors that influence dead fuel moisture content (FMC). It assesses the use of the Canadian forest fire weather index to determine the daily forest fire danger in a typical temperate forest in Northeastern China during autumn. Based on the Wilcoxon test for paired samples, the observed and predicted values of FMC showed similar variation in eight of eleven sampling sites (72.7%), with a p value > 0.05. Three sampling plots presented lower predicted values of FMC than observed values (27.3%), with a p value < 0.05. The calculation of fire risk using the Canadian Forest Fire Weather Rating System (CFFDRS) in Maoer Mountain forest ecosystems presented low, medium or high risk; thus, the CFFDRS is suitable for determining fire danger in our study region. Along with these results, this study served to compare the use of FMC-metre field data and China Weather Station data to evaluate fire danger. The results of this study led us to suggest the multiplication of meteorological stations in fire-prone regions.

Long-term leaf C:N ratio change under elevated CO2 and nitrogen deposition in China: Evidence from observations and process-based modeling

Climate change, elevating atmosphere CO2 (eCO2) and increased nitrogen deposition (iNDEP) are altering the biogeochemical interactions between plants, microbes and soils, which further modify plant leaf carbon‑nitrogen (C:N) stoichiometry and their carbon assimilation capability. Many field experiments have observed large sensitivity of leaf C:N ratio to eCO2 and iNDEP. However, the large-scale pattern of this sensitivity is still unclear, because eCO2 and iNDEP drive leaf C:N ratio toward opposite directions, which are further compounded by the complex processes of nitrogen acquisition and plant-and-microbial nitrogen competition. Here, we attempt to map the leaf C:N ratio spatial variation in the past 5 decades in China with a combination of data-driven model and process-based modeling. These two approaches showed consistent results. Over different regions, we found that leaf C:N ratio had significant but uneven changes between 2 time periods (1960-1989 and 1990-2015): a 5% ± 8% increase for temperate grasslands in northern China, a 3% ± 6% increase for boreal grasslands in western China, and by contrast, a 7% ± 6% decrease for temperate forests in southern China, and a 3% ± 5% decrease for boreal forests in northeastern China. Additionally, the structural equation models indicated that the leaf C:N change was sensitive to ΔNDEP, ΔCO2 and ΔMAT rather than ΔMAP and ecosystem types. Process-based modeling suggested that iNDEP was the main source of soil mineral nitrogen change, dominating leaf C:N ratio change in most areas in China, while eCO2 led to leaf C:N ratio increase in low iNDEP area. This study also indicates that the long-term leaf C:N ratio acclimation was dominated by climate constraint, especially temperature, but was constrained by soil N availability over decade scale.

Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: Implications for forest ecosystem carbon cycling

Fire plays a critical part in regulating soil carbon (C) budgets in forest ecosystems. However, few studies have focused on the effects of fire on soil respiration (Rs) and its heterotrophic (Rh) and autotrophic (Ra) components. In this study, we examined the response of Rs, Rh, and Ra to fire and the driving factors that affect Rs after a fire disturbance in a Dahurian larch (Larix gmelinii) forest in northeastern China. Fire disturbance significantly increased Rh by approximately 24% and significantly reduced Ra by approximately 66% in burned plots compared to unburned plots. The ratio of Rh to Rs significantly increased from 0.57 in the unburned plots to 0.79 in the burned plots. Fire disturbance also led to cumulative Rs C efflux increasing by approximately 10% and 28% during the growing season and the period of spring freeze–thaw cycles (FTC) in the burned plots. Furthermore, using structural equation modelling, we showed that T5 (soil temperature at 5 cm depth) was the main abiotic factor affecting Rs in both unburned and burned plots. Fine root biomass (FR) was the dominant biotic driver of Rs in the control plots, whereas microbial biomass carbon (MBC) was the dominant biotic driver of Rs in burned plots. Our findings demonstrated a significant discrepancy in the response of Rs components to fire disturbance. Forest fires can cause considerable losses of soil C in a cold temperate forest ecosystem, which suggests that proactive management of forest regeneration should be carried out after forest fire disturbances.

The changes in species composition mediate direct effects of climate change on future fire regimes of boreal forests in northeastern China

Abstract Direct effects of climate change (i.e. temperature rise, changes in seasonal precipitation, wind patterns and atmospheric stability) affect fire regimes of boreal forests by altering fire behaviour, fire seasons and fuel moisture. Climate change also alters species composition and fuel characteristics, which subsequently alter fire regimes. However, indirect effects of climate change are often simplified or neglected in the direct climate–fire relationship models and dynamic global vegetation models. This may result in high uncertainties associated with existing projections of fire regimes for climate change scenarios. Moreover, few studies have examined fire regime predictions beyond the 21st century, and consequently, how the fire regimes of boreal forests would respond to climate change at the long term (&gt;100 years) are not clear. We develop a coupled modelling framework integrating direct and indirect effects of climate change to predict fire occurrence probability and burned area for boreal forests in northeastern China. We applied repeated measures ANOVA to quantify direct and indirect effects of climate change on fire regimes in the short (0–50 years), medium (60–100 years) and long term (150–200 years). Results showed that for the 21st century, direct effects of climate change are likely to exert a stronger influence on fire regimes than indirect effects. However, increases in fire occurrence probability and burned area will accelerate the transition of boreal forests to temperate forests in the period 2100–2200, and thereby reduce fire occurrence probability and burned area. This suggests that vegetation change will mediate direct effects of climate change on fire regimes of boreal forests at the long term. Synthesis and applications. Vegetation change will mediate direct effects of climate change on fire regimes of boreal forests at the long term. This finding suggested that policymakers may consider adaptive management by planting deciduous species to reduce fire occurrence probability and resistant management by reducing competition to promote boreal species under changing climate conditions.

Biodiversity increased both productivity and its spatial stability in temperate forests in northeastern China.

Although the relationship between biodiversity and ecosystem functioning has been extensively studied, it remains unclear if the relationships of biodiversity with productivity and its spatial stability vary along productivity gradients in natural ecosystems. Based on a large dataset from 2324 permanent forest inventory plots across northeastern China, we examined the intensity of species richness (SR) and tree size diversity (Hd) effects on aboveground wood productivity (AWP) and its spatial stability among different productivity levels. Structural equation modeling was applied, integrating abiotic (climate and soil) and biotic (stand density) factors. Our results demonstrated that both SR and Hd positively affected AWP and its spatial stability, and the intensity of these positive effects decreased with increasing productivity. At low productivity levels, SR and Hd increased spatial stability by reducing spatial variability and increasing mean AWP. At high productivity levels, stability increased only through mean AWP increase. Moreover, temperature and stand density affected the AWP directly and indirectly via biodiversity, and the strength and direction of these effects varied among different productivity levels. We concluded that biodiversity could simultaneously enhance productivity and its spatial stability in temperate forests, and that the effect intensity was uniform along productivity gradients, which provided a new perspective on relationships within biodiversity-ecosystem functioning.

Understanding patterns and potential drivers of forest diversity in northeastern China using machine‐learning algorithms

AbstractQuestionForest ecosystems are the most important global repositories of terrestrial biodiversity. The mixed temperate forests in northeastern China constitute one of the most biodiverse temperate regions globally and provide nearly one‐third of China's wood supply. We ask what are the spatial patterns and potential drivers of tree species diversity in mixed temperate forests.LocationTemperate, mixed forests of northeastern China.MethodsUsing a large set of ground‐source forest inventory data (FIN) and geospatial covariates derived from published raster layers, we compared different machine‐learning and statistical models to study spatial patterns of tree species diversity and their underpinning drivers.ResultsThe spatial distribution of tree species diversity (species richness and evenness) varied greatly across northeastern China. Tree species diversity varied most with climatic (annual precipitation and annual mean temperature), topographic (elevation and slope), and anthropogenic factors. Anthropogenic factors affected tree species evenness (importance value = 13%) more than tree species richness (importance value = 9%). Based on these relationships, we mapped spatial patterns of tree diversity throughout the region at a 1 km × 1 km resolution.ConclusionsOur findings shed light on the processes behind community assembly and biodiversity patterns in mixed temperate forests in northeastern China, and provide a benchmark for future assessment of biodiversity. Our high‐resolution tree species diversity maps can be useful to landowners and land management agencies in their decision‐making processes about sustainable forest management, biodiversity conservation, and forest restoration — a priority task outlined by the recently implemented 2050 China National Forest Management Plan.

Analysing species abundance distribution patterns across sampling scales in three natural forests in Northeastern China

Understanding how and why species abundance distributions (SADs) vary with sampling scale has been a long-standing issue in ecology. By fitting various SAD models with observations collected in three large forest field plots, the objective of this study is to explore how the shape of SADs and the predictive ability of SAD models vary with sampling scales. Based on a large dataset collected in the Changbaishan, Jiaohe and Liangshui forests in northeastern China, observed SADs were compared with SADs estimated using five different models (log-normal, broken stick, Zipf, niche preemption and neutral model) at four sampling scales (10 × 10 m, 30 × 30 m, 60 × 60 m and 90 × 90 m). The results show that the studied SADs are scale dependent. Niche-based models provided a better fit at small sample sizes, the predictive ability decreasing with increasing sampling scale. The neutral model performed better at large sample sizes, the predictive ability increasing with increasing sampling scale. We identify the models that provided the best fit to observed species abundance distributions across spatial scales, and conclude that there is not one best SAD model for all spatial scales. Future studies should consider the scale effects on the species abundance distribution.