Masson pine is a crucial species for afforestation and timber production in China; it plays an important role in mitigating global climate warming and increasing carbon sinks. Previous studies have primarily focused on the carbon sequestration potential and carbon storage of mature Masson pine plantations, while studies on the carbon footprint have received little attention. China produces hundreds of millions of seedlings annually, and estimating the carbon footprint of seedling production is crucial for assessing the carbon sink of forestry. By surveying existing Masson pine nursery operations for primary data in Guangxi, southern China, a new process-based life cycle inventory (LCI) dataset per 4 × 8 cm seedling was created, covering all stages from seed collection to the transportation of seedlings to retailers. Incorporating the new LCI data into the life cycle assessment (LCA) method, the total global warming (GW) impact of Masson pine seedlings was estimated to be 0.0232 kg CO2eq, equivalent to 0.873 kg CO2eq per gallon seeding. In this case, the total environmental impact of the Masson pine seedling was dominated by energy consumption (25.76%), chemical fertilizer production and N₂O emissions generated from its application (34.84%), and woven bag use in seedling dispatch (10.77%). Our results indicated that optimizing energy structures and implementing efficient water and nutrient management strategies could significantly reduce carbon emissions during seedling cultivation. This study highlights the potential for optimizing Masson pine production as a model for low-carbon forestry practices globally.

Widely accepted universal models and hypotheses such as 'high vein density-faster growth and higher productivity' hold that high leaf vein density may promote higher coupling efficiency of carbon and water, indicating that rapid individual growth and high stand productivity, have attracted huge interest. However, these models and hypotheses do not include enough gymnosperm samples, especially conifers cultivated in subtropics. We here examined the values and scaling relationships between leaf vein density and leaf functional traits sampled from center region of the distribution range of Cunninghamia lanceolate, which has been well known for rapid growth. We also retrieved an empirical dataset that included photosynthetic, biochemical, anatomical and hydraulic traits of Cunninghamia lanceolata. The leaf vein density (ranging from 0.34 to 1.09 mm mm(-2)) is extremely low compared to the reported global range (1 to 25 mm mm(-2)), whereas C. lanceolata is famous for both fast-growing and high-yielding in China for a long time. We further verified that higher vein densities were associated with smaller leaves (r = -0.71, P < 0.001), which is consistent with that found in angiosperms. However, we found that vein density-thickness correlations and leaf lifespan plasticity showed opposite trends for C. lanceolate (negative) when compared with global species (positive), and such relationships may indicate the tradeoffs between functional efficiency and productivities. Our results provide an effective complementary assessment of general growth rules, including evaluation of the influence of regional plant trait characterization, configuration of plant species, and traits efficiency for hydraulic potential.

The geographic information system（GIS） course itself has a distinct cartographic character. It has its unique advantages in exploring the elements of curriculum ideology and politics, such as safeguarding national sovereignty and territorial integrity. At present, the studies on the teaching reform of curriculum ideology and politics in GIS under the background of patriotism education in the new era rarely reported. Under the guidance of the principles of structural, subtle influence and journalistic teaching design, by combining news events and courses,national security education and document learning, error identification and correction, history and geography, etc.,knowledge points in each GIS chapter（e.g., introduction, coordinate systems, overlay analysis, layer renderer,georeferencing, etc.） are selected to deeply explore the elements of curriculum ideology and politics under the background of patriotism education in the new era, in order to provide reference for the reform of curriculum ideology and politics.

In order to analyze the primary sources of air pollutants in Chang-Zhu-Tan region, this article selected the environmental monitoring data and meteorological data in the winter of 2019 to calculate the backward airflow trajectories with the Chang-Zhu-Tan region as the starting point by using the backward trajectory model. Combined with the ground concentration monitoring data, cluster analysis, potential source contribution factor (PSCF) analysis, and concentration weighted trajectory (CWT) analysis were carried out to determine the pollutant transportation paths and sources of the potential source area. The results show that air mass transportation mainly comes from three directions: northwest, northeast, and southwest China. The airflow in northwest China moves faster and cleaner, while the airflow from the northeast and southwest moves slowly and carries a high concentration of pollutants. PSCF and CWT analyses show that the critical potential sources are mainly located in this area and some cities next to the study area. This study has important practical significance for the environmental research of Chang-Zhu-Tan region and can provide theoretical reference for regional joint prevention and control of air pollution.

The impacts of ice-storms on forests have received growing attention in recent years. Although there is a wide agreement that ice-storms significantly affect forest structure and functions, how frequent ice-storms and subsequent salvage logging impact productivity of subtropical coniferous forests in the future still remains poorly understood. In this study, we used the Ecosystem Demography model, Version 2.2 (ED-2.2), to project the impact of salvage logging of ice-storm-damaged trees on the productivity of Cunninghamia lanceolata-dominated coniferous forest and C. lanceolata-dominated mixed coniferous and broadleaved forests. The results show that forest productivity recovery is delayed in coniferous forests when there is no shade-tolerant broadleaved species invasion after ice-storms, and C. lanceolata could continue to dominate the canopy in the mixed coniferous and broadleaved forests under high-frequency ice-storms and subsequent salvage logging. The resistance and resilience of the mixed coniferous and broadleaved forests to high-frequency ice-storms and subsequent salvage logging were stronger compared to coniferous forests. Although conifers could continue to dominate the canopy under shade-tolerant broadleaved species invasion, we could not rule out the possibility of a future forest community dominated by shade-tolerant broadleaf trees because there were few coniferous saplings and shade-tolerant broadleaf species dominated the understory. Our results highlight that post-disaster forest management should be continued after high-frequency ice-storms and subsequent salvage logging in C. lanceolata forests to prevent possible shade-tolerant, late successional broadleaf trees from dominating the canopy in the future.

The impacts of drought and/or warming on forests have received great attention in recent decades. Although the extreme drought and/or warming events significantly changed the forest demography and regional carbon cycle, the seasonality quantifying the impacts of these climate extremes with different severities on the productivity of subtropical coniferous forests remains poorly understood. This study evaluated the effects of seasonal drought and/or warming on the net primary productivity (NPP) of subtropical coniferous forests (i.e., Cunninghamia lanceolata and Pinus massoniana forests) from Hengyang–Shaoyang Basin in southern China using the Ecosystem Demography model, Version 2.2 (ED-2.2) and based on the datasets from forest inventory, meteorological reanalysis, and remotely sensed products. The results showed that the goodness of fit of the DBH-height allometric equations was better than that of the default in ED-2.2 after model calibration; the ED-2.2 model qualitatively captured the seasonality of NPP in the subtropical coniferous forests; and the mismatch between simulated annual NPP and MODIS-NPP (MOD17A3HGF) became smaller over time. The effect of seasonal drought on NPP was greater than that of warming; the decline rate of NPP gradually increased and decreased with time (from July to October) under the seasonal drought and warming scenarios, respectively; NPP decreased more seriously under the combined drought-warming scenario in October, with an average decrease of 31.72%, than the drought-only and warming-only scenarios; seasonal drought had an obvious legacy impact on productivity recovery of subtropical coniferous forests, but it was not the case for warming. With the increase in drought severity, the average values of soil available water and NPP together showed a downward trend. With the increase in warming severity, the average values of canopy air space temperature increased, but NPP decreased. Seasonal drought and/or warming limit forest production through decreasing soil moisture and/or increasing canopy air space temperature, which impact on plant photosynthesis and productivity, respectively. Our results highlight the significance of taking into account the impacts of seasonal warming and drought when evaluating the productivity of subtropical coniferous forests, as well as the significance of enhancing the resistance and resilience of forests to future, more severe global climate change.

Abstract The asymptotic height (Hmax) and the size‐scaling exponent (β), proxies of tree size and important forest architectural parameters of the relationships between height and tree diameter of forests, play important roles in the carbon (C) stocks and fluxes of forest ecosystems. However, the patterns and drivers of these values in continental China are not well known. Based on a national forest database of China, we explored the geographic patterns and ecological drivers of Hmax and β and their dynamic relationships with the C sequestration capabilities of the studied ecosystems, including the C stock and allocation, net ecosystem productivity (NEP), and mean ecosystem C turnover time. At the ecosystem scale, the Hmax values ranged from 10.37 to 37.07 m, with a mean value of 25.41 m, showing a decreasing trend with increasing latitude. However, β ranged from 0.598 to 0.73, with a mean value of 0.66, showing an increasing trend with latitude. The spatial patterns of the forest Hmax and β at the national scale were largely controlled by the mean annual temperature, mean annual precipitation, and forest age. These patterns were verified using biomass and forest C sequestration data, showing that the total vegetation C stock as a whole, in aboveground or belowground components, was significantly and positively correlated with Hmax (p < 0.05), revealing apparent “faster first, slower later” increasing trends with Hmax (R2 = 0.759, 0.821, and 0.527, respectively). In addition, for forest >20 m, NEP showed a significant strong negative correlation with Hmax (by −0.967% per 1 m of Hmax, R2 = 0.504, p < 0.05), and a 10‐m increase in Hmax resulted in a decrease in mean ecosystem C turnover time by 16.9% (7.43 years). Taller forests might indeed be expected to have higher autotrophic respiration consumption rates but lower mean ecosystem C turnover time and NEP values than shorter forests. The discovery that community‐level asymptotic height could be used as a comparative predictor of ecosystem C sequestration capacity and its difference among forest biomes underpins the fundamentals of ecological theories and modeling, which has important implications for forest restoration, policy, and management.

Higher land surface temperature (LST) in cities than its surrounding areas presents a major sustainability challenge for cities. Adaptation and mitigation of the increased LST require in-depth understanding of the impacts of landscape features on LST. We studied the influences of different landscape features on LST in five large cities across China to investigate how the features of a specific urban landscape (endogenous features), and neighboring environments (exogenous features) impact its LST across a continuum of spatial scales. Surprisingly, results show that the influence of endogenous landscape features (E-endo) on LST can be described consistently across all cities as a nonlinear function of grain size (g(s)) and neighbor size (n(s)) (E-endo = beta n(s)g(s)(-0.5), where beta is a city-specific constant) while the influence of exogenous features (E-exo) depends only on neighbor size (n(s)) (E-exo = gamma-epsilon n(s)(0.5), where gamma and epsilon are city-specific constants). In addition, a simple relationship describing the relative strength of endogenous and exogenous impacts of landscape features on LST was found (E-endo > E-exo if ns > kg(s)(2/5), where k is a city-specific parameter; otherwise, E-endo < E-exo). Overall, vegetation alleviates 40%-60% of the warming effect of built-up while surface wetness intensifies or reduces it depending on climate conditions. This study reveals a set of unifying quantitative relationships that effectively describes landscape impacts on LST across cities, grain and neighbor sizes, which can be instrumental towards the design of sustainable cities to deal with increasing temperature. (C) 2021 Elsevier B.V. All rights reserved.

Abstract Chinese trees adapted to wet, warm, highly heterogeneous, and dynamic light environments might be expected to systematically differ from other non‐Chinese trees based on stem allometry and life em allo. However, our understanding of the extent to which how differentiation in forest architecture and species size influences tree allometry relates to fundamental physiological and ecological trade‐offs, climate, forest structure, and function is limited. We quantified height–diameter allometries and growth increments of 13,594 individuals of 151 tree species across China. Although trees grew statistically indistinguishably from the theoretically predicted exponent, the height–diameter allometries for trees in China deviated from tree patterns in other continental forests worldwide. Chinese trees showed substantially lower heights at a given diameter for larger trees and asymptotic heights (33.05 m), with values were 5.5% to 43.3% lower than those estimated for continental rain forest trees in South America, Australia, Southeast Asia, and Africa. In total, 149 of 151 species showed significant nonlinearity of height–diameter allometry with asymptotic heights ranging from 6.33 to 50.54 m; additionally, half (74/149 species) fell in the range of 20–33.5 m, averaging 21.98 ± 8.70 m (SE) for canopy trees. In subsets of canopy trees sampled from larger‐statured species and deciduous trees, the approximated maximum heights were 14–42% larger than those of smaller‐statured and evergreens and those in other wet and warmer regions found worldwide. These results suggest flexible phenotypic responses of height–diameter allometries to heterogeneous light conditions along understory‐canopy gradients. Average growth rates, varying from 0.02 to 0.78 cm/yr (mean of 0.25 cm/yr), were positively related to the asymptotic height, diameter at inflection, and allometric intercept but negatively correlated with the shape of the asymptotic curve and allometric slope. Species with asymptotic heights above 30 m or inflection sizes larger than 20 cm clearly depicted higher growth rates than smaller ones.

Due to manganese mining and slag accumulation, the geological structure of the wetland polluted by heavy metals in Xiangtan Manganese Mine area was seriously damaged, hence biodiversity loss, severe soil, and water pollution, as well as serious heavy metal pollution to food, vegetables, and other natural sources. In order to restore the ecological environment of the mining area, in 2015, the ecological restoration test of heavy metal polluted wetlands in the mining area was carried out. The results showed that the Mn content of different parts of Koelreuteria paniculata root from high to low order: fine root > small root > medium root > large root. The Mn content of different parts of Elaeocarpus decipiens root from high to low order: large root > medium root > small root > fine root. The order of Mn content in plants of the wetland restoration from high to low is as follows: Canna warscewiezii > Thalia dealbata > Boehmeria > Pontederia cordata > Typha orientalis > Nerium oleander > Softstem bulrush > Iris germanica > Acorus calamus > Arundo donax > Phragmites australis; The order of Internal Cu content from high to low is as follows: Acorus calamus > Thalia dealbata > Softstem bulrush > Canna warscewiezii > Typha orientalis > Arundo donax > Boehmeria > Iris germanica > Pontederia cordata > Nerium oleander > Phragmites australis; Zn content from high to low order is as follows: Canna warscewiezii > Acorus calamus > Thalia dealbata > Typha orientalis > Pontederia cordata > Arundo donax > Softstem bulrush > Iris germanica > Boehmeria > Phragmites australis > Nerium oleander; Cd content from high to low order is as follows: Phragmites australis > Softstem bulrush > Thalia dealbata > Nerium oleander > Boehmeria > Canna warscewiezii > Acorus calamus > Iris germanica > Typha orientalis > Pontederia cordata > Arundo donax. The results of this study have provided a theoretical basis and decision-making reference for the evaluation of heavy metals polluted wetland restoration, protection, and reconstruction effects and the selection of ecological restoration modes.

Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems. However, the tremendous uncertainty of the GPP still hinders the reliability of GPP estimates and therefore understanding of the global carbon cycle. In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 24 widely used GPP models and the quality of major spatial data layers that drive the models. Results show that global GPP products generated by the 24 models varied greatly in means (from 92.7 to 178.9 Pg C yr−1) and trends (from −0.25 to 0.84 Pg C yr−1). Model structure differences (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty. In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation at the flux tower locations was explained by the spatial dataset), which not only affect GPP simulation but more importantly hinder the simulation and understanding of the earth system. Moving forward, research into the efficacy of model structures and precision of input data may be more important for global GPP estimation.

Plant accumulation of heavy metals (HMs) has become a global concern, however, the bio-accumulation distributions, their inter-correlations, and relationships with influencing factors remain unknown at the national scale in China. Here, we investigate the bio-accumulation capacity, as indicated as bio-concentration factors (BCFs), for chromium (Cr), cadmium (Cd), lead (Pb), copper (Cu), and Zinc (Zn) across plants growing on China (span 22 provinces) and their relationships, based on national meta-analysis of 46 publications (210 species, and 66 families). Results showed that: (1) Plants show diverse capacity of HM accumulation. About 20.6% of the plant tissue cases indicates strong phytoremediation ability for HMs (i.e., Ln BCF > 0), including 38.5% of Cd, 28.5% of Cr, 11.9% of Cu, 7.6% of Pb, and 16.6% of Zn. The families with the greatest numbers of published strong HMs phytoremediation cases are Poaceae, Asteraceae, and Brassicaceae. (2) The capacity to uptake and accumulation one of these HMs is significantly correlated with the ability to absorb the others (p <.001) for both all plants and organs but differ in strength and degree of correlations. The synergistic effect of the accumulation of these metals is more stable in stems than other parts. Leaves and roots differ significantly in metal co-accumulating strengths and enrichment patterns. (3) Experimental factors affect the HM bio-accumulation processes and bio-mediated mechanisms and responsible for substantial heterogeneous in phytoremediation effectiveness. These findings suggest that the use of plant as a soil pollution indicator and phytoremediation technique, synergistic strategies of bio-accumulation as ecological indicators and bio-mediated mechanisms for phytoremediation of polymetallic contaminated areas, with considering sensitivity of influencing factors, might have a crucial effect on the eco-service, eco-environment, and land management and could reduce material hazards by increasing bio-accumulation efficiency, or could reduce human health risk by decreasing potential intake of HMs. We argue that this meta-analysis would aid the assessment of the strength of general HM risks, the evaluation of regional soil contamination hazards and consequences, and the configuration of plant species and traits efficiently for phytoremediation potential.

Zhang et al. (1) raise concerns over the characteristics and change of surface water bodies (WBs) in China reported in our article in PNAS (2). We believe these concerns are not well grounded. The dynamic nature of WBs results in a constant change in their number and extent over large land areas. Therefore, it is important to define a temporal window from whence the WB calculations are referenced, as Zhang et al. (1) highlight. Feng et al. (2) reference previous studies that relied on satellite snapshots acquired at one, or a few, points in time to study WBs across China. These studies, while useful for characterizing WB conditions at the specific points in time, could not accurately estimate the maximum extent, … [↵][1]1To whom correspondence may be addressed. Email: shuguang.liu{at}yahoo.com. [1]: #xref-corresp-1-1

Since China experienced a rapid and unprecedented process of urbanization and climate change from 1978 onwards, pest outbreaks were frequently reported on urban forests, which reflects a significant imbalance between natural regulation and human control. Based on information extracted from all journal articles and reports about insect pests on camphor tree (Cinnamomum camphora) in urban China, we characterized historical patterns and trends in pest outbreaks over large areas. Our results suggested that (1) most distribution areas of C. camphora in urban China had pest records (14 provinces) over the last 50 years, especially at the south-eastern coastal areas; (2) pests on camphor tree in urban China showed an accelerated growth since the 1990s; and (3) pests on camphor tree in urban China were characterized by native and leaf-feeding species. Urbanization seems to positively correlate with urban pest outbreaks. Changes of urban pest outbreaks could largely be described by synchronic changes of socio-economic indicators, of which CO2 emissions as metric tons per capita is the most significant predictor, followed by GDP and human population. Thus, managers and city planners should allocate resources to socio-economic-related pest outbreaks for a sustainable ecosystem.

Water bodies (WBs), such as lakes, ponds, and impoundments, provide essential ecosystem services for human society, yet their characteristics and changes over large areas remain elusive. Here we used unprecedented data layers derived from all Landsat images available between 1984 and 2015 to understand the overall characteristics and changes of WBs between 2 epochs (i.e., 1984 to 1999 and 2000 to 2015) in China. Results show that the abundance estimate of WBs greater than 1 km2 and the total WB surface area were 0.3 to 1.5 times and 0.2 to 0.5 times more than the previous estimates, respectively. The size-abundance and shoreline-area relationships of WBs in China conformed to the classic power scaling law, in contradiction to most previous studies. WB changes with various occurrence probabilities show widespread coexistence of disappearance of existent and emergence of new WBs across China driven primarily by human activities and climate change. Our results highlight the importance of using appropriate long-term satellite data to reveal the true properties and dynamics of WBs over large areas, which is essential for developing scaling theories and understanding the relative impacts of human activities and climate change on water resources in the world.

Aims With progresses of leaf functional traits study,there is an increasing demand to explore the life history strategy and trade-offs in plants,as well as estimate stand productivity,by employing easy and simple leaf parameters.For instance,the interconversion between leaf dry matter content (LDMC) and specific leaf area (SLA)just fit the bill.Cunninghamia lanceolata serves as one of the most important afforestation evergreen needle species in subtropical zone.Building the SLA estimation model based on LDMC could provide a new approach to estimate SLA,and establish a connection path between mechanism explanation and productivity evaluation.Moreover,it could also build a bridge between individual level and large-scale,as well as between actuarial and estimation.Methods Leaf samples were collected from two sampling sites located in C.lanceolata growing region:Huitong County of Hunan Province and Xinyang City of Henan Province.The samples covered fundamentally different niches (aspect,slope position,and canopy depth),and different life history (stand age and leaf age).SLA and LDMC were determined along leaf age gradients,and their value distributions in linkage to different factors were discussed.A general model based on LDMC of C.lanceolata was built to estimate SLA,and the impact of leaf age on the model was explored.Important findings The SLA ofC.lanceolata was (103.15 ± 69.54) cm2·g 1,while LDMC was 0.39 ± 0.11.The LDMC and SLA of C.lanceolata can be estimated by nonlinear model (R2 =0.718 4,p ＜ 0.001),which meets the estimation requirements.One-year-old leaves showed the best fitting model (R2 =0.889,p ＜ 0.001),while old leaves (more than 2-year-old) showed the worst (R2 =0.1001,p ＜ 0.001).Old leaves with a lower SLA (52.28 75.74 cm2·g-1) might imply the relative independence among the variation of LDMC.The model based on LDMC to evaluate SLA is credible and effective.The effects on LDMC and SLA along leaf age gradients indicate leaf sensitivity,life history strategies and trade-offs.

Most of the planet's population currently lives in urban areas, and urban land expansion is one of the most dramatic forms of land conversion. Understanding how cities evolve temporally, spatially, and organizationally in a rapidly urbanizing world is critical for sustainable development. However, few studies have examined the coevolution of urban attributes in time and space simultaneously and the adequacy of power law scaling across cities and through time, particularly in countries that have experienced abrupt, widespread, political and economic changes. Here, we show the temporal coevolution of multiple physical, demographic, socioeconomic, and environmental attributes in individual cities, and the cross-city scaling of urban attributes at six time points (i.e., 1978, 1990, 1995, 2000, 2005, and 2010) in 32 major Chinese cities. We found that power law scaling could adequately characterize both the cross-city scaling of urban attributes across cities and the longitudinal scaling describing the temporal coevolution of urban attributes within individual cities. The cross-city scaling properties demonstrated substantial changes over time signifying evolved social and economic forces. A key finding was that the cross-city linear or superlinear scaling of urban area with population contradicts the theoretical sublinear power law scaling proposed between infrastructure and population. Furthermore, the cross-city scaling between area and population transitioned from linear to superlinear over time, and the superlinear scaling in recent times suggests decreased infrastructure efficiency. Our results demonstrate a diseconomy of scale in urban areal expansion that indicates a significant waste of land resources in the urbanization process. Future planning efforts should focus on policies that increase urban land use efficiency before continuing expansion.

The process-based hybrid model is a promising tool for predicting forest stand production on regional scales. TRIPLEX1.6 was adapted and parameterized to simulate Cunninghamia lanceolata and Pinus massoniana forest stand production in Hunan Province, China, using data from permanent sample plots established by the National Forest Inventory of China (CNFI). Monthly maximum and minimum air temperature and precipitation (derived from interpolation of the data collected at 369 meteorological stations in Hunan between 2000 and 2009) were used to run the model. Model calibrations and simulations were performed through threshold parameters and initial statuses at a regional scale. The species- and site-specific sensitive parameters were adjusted for estimating tree growth rate of different stand age or diameter at breast height (DBH). The improved parameterize procedure actually did increase model practicability. The site and species data for model validation were achieved by applying half the 2009 permanent sample plot data. Estimated stand average tree height (H), DBH, and biomass were validated against the other half of the 2009 data. Simulated results were consistent with the observed data in Hunan Province. Coefficients of determination (r(2)) of predicted and observed data were 0.83 for H, 0.82 for DBH, 0.90 for aboveground biomass, and 0.94 for total biomass, indicating that TRIPLEX1.6 is capable in predicting forest growth and biomass dynamics of subtropical coniferous forests. Moreover, independent validations determined that TRIPLEX1.6 demonstrated competence in extrapolating outcomes on regional scales as well as withstanding rigorous testing in predicting C storage in subtropical forest ecosystems. (C) 2012 Elsevier B.V. All rights reserved.

Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol–climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed “cause–effect relationships” between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost–benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales.

Cunninghamia lanceolata (Lamb.) Hook. has been widely planted in subtropical China to meet increasing timber demands, leading to short-rotation practices that deplete soil nutrients. However, increased nitrogen (N) deposition offsets soil N depletion. While long-term experimental data investigating the coupled effects related to short rotation practices and increasing N deposition are scarce, applying model simulations may yield insights. In this study, the CenW3.1 model was validated and parameterized using data from pure C. lanceolata plantations. The model was then used to simulate various changes in long-term productivity. Results indicated that responses of productivity of C. lanceolata plantation to increased N deposition were more related to stand age than N addition, depending on the proportion and age of growing forests. Our results have also shown a rapid peak in growth and N dynamics. The peak is reached sooner and is higher under higher level of N deposition. Short rotation lengths had a greater effect on productivity and N dynamics than high N deposition levels. Productivity and N dynamics decreased as the rotation length decreased. Total productivity levels suggest that a 30-year rotation length maximizes productivity at the 4.9 kg N ha(-1) year(-1) deposition level. For a specific rotation length, higher N deposition levels resulted in greater overall ecosystem C and N storage, but this positive correlation tendency gradually slowed down with increasing N deposition levels. More pronounced differences in N deposition levels occurred as rotation length decreased. To sustain C. lanceolata plantation productivity without offsite detrimental N effects, the appropriate rotation length is about 20-30 years for N deposition levels below 50 kg N ha(-1) year(-1) and about 15-20 years for N deposition levels above 50 kg N ha(-1) year(-1). These results highlight the importance of assessing N effects on carbon management and the long-term productivity of forest ecosystems.