Changes In Net Primary Productivity Research Articles

Unraveling the enigma of NPP variation in Chinese vegetation ecosystems: The interplay of climate change and land use change

Global carbon emissions have exacerbated the greenhouse effect, exerting a profound impact on ecosystems worldwide. Gaining an understanding of the fluctuations in vegetation net primary productivity (NPP) is pivotal in the assessment of environmental quality, estimation of carbon source/sink potential, and facilitation of ecological restoration. Employing MODIS and meteorological data, we conducted a comprehensive analysis of NPP evolution in Chinese vegetation ecosystems (VESs), employing Theil–Sen median trend analysis and the Mann–Kendall test. Furthermore, utilizing scenario-based analysis, we quantitatively determined the respective contributions of climate change and land use change to NPP variations across various scales. The overall NPP exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 5.83 gC·m−2·year−1. Forestland ecosystem (FES) displayed the highest rate of increase (9.40 gC·m−2·year−1), followed by cropland ecosystem (CES) (4.00 gC·m−2·year−1) and grassland ecosystem (GES) (3.40 gC·m−2·year−1). Geographically, NPP exhibited a spatial pattern characterized by elevated values in the southeast and diminished values in the northwest. In addition, climate change had elevated 76.39 % of CES NPP, 90.62 % of FES NPP, and 71.78 % of GES NPP. At the national level, climate change accounted for 83.14 % of the NPP changes, while land use change contributed 14.14 %. Notably, climate change emerged as the primary driving force behind NPP variations across all VEGs, with land use change exerting the most pronounced influence on CES. At the grid scale (2 km × 2 km), land use change played a substantial role in all VEGs, contributing 60.01 % in CES, 54.20 % in FES, and 55.61 % in GES of the NPP variations.

Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019

Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) > GDP (0.326) > precipitation (0.255) > elevation (0.167) > slope (0.136) > temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world.

Opposing effects of warming on the stability of above- and belowground productivity in facing an extreme drought event.

Climate warming, often accompanied by extreme drought events, could have profound effects on both plant community structure and ecosystem functioning. However, how warming interacts with extreme drought to affect community- and ecosystem-level stability remains a largely open question. Using data from a manipulative experiment with three warming treatments in an alpine meadow that experienced one extreme drought event, we investigated how warming modulates resistance and recovery of community structural and ecosystem functional stability in facing with extreme drought. We found warming decreased resistance and recovery of aboveground net primary productivity (ANPP) and structural resistance but increased resistance and recovery of belowground net primary productivity (BNPP), overall net primary productivity (NPP), and structural recovery. The findings highlight the importance of jointly considering above- and belowground processes when evaluating ecosystem stability under global warming and extreme climate events. The stability of dominant species, rather than species richness and species asynchrony, was identified as a key predictor of ecosystem functional resistance and recovery, except for BNPP recovery. In addition, structural resistance of common species contributed strongly to the resistance changes in BNPP and NPP. Importantly, community structural resistance and recovery dominated the resistance and recovery of BNPP and NPP, but not for ANPP, suggesting the different mechanisms underlie the maintenance of stability of above- versus belowground productivity. This study is among the first to explain that warming modulates ecosystem stability in the face of extreme drought and lay stress on the need to investigate ecological stability at the community level for a more mechanistic understanding of ecosystem stability in response to climate extremes.

Impacts of Human Activities on the Net Primary Productivity of Vegetation in Chengde's Transitional Region from Plateau to Plain in the Context of Climate Change

Chengde's transitional region from plateau to plain is located in the transition zone of agriculture and livestock and is extremely sensitive to climate change and human activities. This study used the net primary productivity(NPP) of vegetation as an evaluation index to quantify the degree impacts of climate change and human activities on vegetation change in the region. The Thornthwaite Memorial model was used to calculate the potential NPP, and the actual NPP was obtained based on MODIS NPP remote sensing images, using the difference between the actual and potential NPPs to express the amount of change in NPP owing to human activities. We used the slope trend and coefficient of variation method to analyze the trend and stability distribution of the actual NPP, potential NPP, and NPP influenced by human activities, and the correlation between actual NPP and annual precipitation and annual average temperature was analyzed using the correlation coefficient method. Finally, we quantified the impact of climate change and human activities on vegetation change in the region. The results showed that 99.87% of the vegetation in the region was improved and changed steadily, and the proportions of the areas showing positive correlation between actual NPP and annual precipitation and annual average temperature were 99.87% and 91.66%, respectively. The potential NPP showed an increasing trend from northwest to southeast, whereas the trend and stability of the potential NPP both showed an increasing trend from west to east. The area where climate change and human activities played a role in vegetation improvement accounted for 99.71%, and that affected by climate change accounted for 0.14%, with the proportion of human activities leading to vegetation degradation being 0.15%.

Spatial–Temporal Variation Characteristics and Driving Factors of Net Primary Production in the Yellow River Basin over Multiple Time Scales

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts.

Stressors of primary productivity in the north Indian ocean revealed by satellite, reanalysis and CMIP6 data

Phytoplankton contribute to nearly half of the global net primary productivity (NPP) and are critical for marine ecosystems and global food security. Significant changes in ocean temperature, sea surface salinity (SSS), Mixed Layer Depth (MLD), ocean heat content (OHC), pH and dissolved oxygen (O2) at upper 200 m and that averaged between 200 and 600 m to represent the Oxygen Minimum Zones (OMZ) are identified as the key stressors of marine ecosystem. Here, we examine the changes in stressors and their impact on NPP in the north Indian Ocean (NIO) and its major upwelling zones. There is an increase in SST and freshening in the high saline regions as analysed from the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways (SSP) future (2015–2100) projections, which would reduce NPP in NIO in future. We identify MLD, O2 in 0–200 m and OMZ as the main factors controlling NPP in the present (2003–2014) period, but SSS and O2 in 0–200 m and OMZ in the future SSP scenarios. Contribution analysis indicates the dominant role of O2 in modulating NPP in the future scenarios in the upwelling regions (e.g. Northern AS, Somali Coast and South of Sri Lanka). Future projections also suggest a decreasing trend in pH across different SSP scenarios and its highest negative trend (-0.003 ± 11.12 × 10-5) is observed in SSP 3–7.0. These changes are more evident in Arabian Sea and its adjoining upwelling regions, but a dominant role of SSS in changing NPP is present in BoB and its upwelling regions. The interaction of stressors is manifested well in West Coast and East coast of India, where circulation and currents have dominant roles in the exchanges of temperature and salt fluxes between the regions. Changes in NPP in these dynamically active regions are controlled by all stressors unlike in other regions. Henceforth, our study provides new insights on the impact of spatio-temporal changes in stressors on the primary productivity of NIO.

Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity

AbstractClimate change and the resulting natural vegetation succession can alter vegetation productivity. However, the mechanisms underlying future productivity changes under the two influences remain unclear. Here, we used the comprehensive sequence classification system to simulate changes in global potential natural vegetation under different climate scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5), and combined the Carnegie–Ames–Stanford Approach model with random forest to assess the response of net primary productivity (NPP) to climate change and vegetation succession from 2020 to 2100. Except for SSP126, terrestrial NPP in 2100 decreased by 0.86, 2.39, and 2.54 Pg C·a−1 versus 2020 under SSP2‐4.5, SSP5‐8.5, and SSP3‐7.0, respectively. Forest was the primary contributor to terrestrial NPP changes. The total forest area was projected to increase under all scenarios, with SSP2‐4.5 showing the largest increase (358.57 × 104 km2). However, expanding forest regions exhibited a relatively low mean NPP, while stable regions demonstrated a declining pattern. Consequently, forest NPP increased under SSP1‐2.6 but decreased by 4.03, 3.43, and 0.82 Pg C·a−1 in 2100 versus 2020 under SSP5‐8.5, SSP3‐7.0, and SSP2‐4.5, respectively. In comparison, grassland and desert exerted minor influence on terrestrial NPP changes, their total NPP decreased only under the SSP1‐2.6 scenario. The grassland area decreased, but the mean NPP increased, whereas the desert area expanded, resulting in consistent changes in both total and mean NPP. Our results analyzed the effects of climate change and vegetation distribution under its influence on the change of NPP, which can deepen our understanding of their relationship.

Spatial–Temporal Dynamics of Grassland Net Primary Productivity and Its Driving Mechanisms in Northern Shaanxi, China

Grasslands, a vital ecosystem and component of the global carbon cycle, play a significant role in evaluating ecosystem health and monitoring the global carbon balance. In this study, based on the Carnegie–Ames–Stanford Approach (CASA) model, we estimated the Net Primary Productivity (NPP) of grasslands in northern Shaanxi from 2000 to 2020. Employing trend analysis, stability analysis, multiple regression analysis, and residual analysis, the research examined the dynamic changes of grassland NPP and its response to climatic and human factors. Key findings include: (1) Grassland NPP showed a significant increasing trend during 2000–2020, with high-coverage grasslands showing a higher rate of increase than medium and low-coverage grasslands. (2) Most grasslands (>90%) exhibited unstable growth and high NPP fluctuation. (3) While temperature, precipitation, and radiation undulate, the trends were not significant. Rainfall and radiation emerged as dominant factors affecting NPP, with temperature suppressing NPP increase to some extent. (4) Policies like returning farmland to grassland had a positive impact on grassland recovery, vegetation productivity, and regional ecosystem health.

Remote sensing approach in evaluating anthropogenic impacts on the spatiotemporal changes in net primary productivity of the Niger river basin, from 2000 to 2020

Deterioration of the environment can be examined by utilizing a statistical evaluation of the effects of anthropogenic activities (beneficial or detrimental) on net primary productivity. The Niger River Basin's net primary productivity is significant both theoretically and practically for the management of the natural environment. It is important for her member countries to understand vegetation dynamics, maintain carbon balance, and ensure food security in the region. The research applied remote sensing to determine the relative impact of human activities on the net primary productivity of the Niger River Basin from 2000 to 2020. The study simulated the actual and potential net primary productivity using the Carnegie Ames Stanford Approach and Thornthwaite's Memorial Model respectively, while the result of the simulations was used to calculate human-influenced net primary productivity. The slope of the three simulations was calculated and merged in several scenarios using ArcGIS 10.8 to determine the impact of human activities on net primary productivity of the study area. The negative impacts of human activities were recorded in 89.88 % of the investigated area, while 10.12 % of the NRB had signs of positive impacts. Amongst the biomes, urban areas and bare land experienced the largest negative impacts (97.2 % and 99.8 %, respectively). The study advised the effectiveness of ecological restoration programs, through sound scientific and technical methods, such as those used in rural development, nomadic herding, environmental protection, and natural resource management policies.

Spatio-temporal variation and impacting factors of NPP from 2001 to 2020 in Sanjiangyuan region, China: A deep neural network-based quantitative estimation approach

The ecosystem in the Sanjiangyuan region of China is known to all for its simplicity and fragility. Studying the changes in net primary productivity (NPP) of vegetation and its influencing factors will help protect and improve this fragile environment. However, there are missing data in some regions in NPP data collectors, which affect the data availability. Therefore, this article proposes a method for estimating NPP using only a small amount of basic data by constructing a deep neural network (DNN) model suitable for NPP estimation. This method requires only a small amount of basic data to obtain high-precision, complete NPP data. We conducted a precision verification of the NPP estimates in the Sanjiangyuan region and studied the temporal and spatial variation of NPP in the Sanjiangyuan region, the relations between NPP and meteorological factors, and the contributions of climate change and human activities to NPP changes. The results showed that the estimation of NPP was reliable, with coefficient of determination (R2) ranging from 0.893 to 0.933 and root mean square error (RMSE) ranging from 24.904 to 34.550. From 2001 to 2020, the NPP in the study area showed a significant increasing trend (1.32 g C/m2·year−1, P < 0.05). The partial correlation coefficients between climate and NPP were all positive, with the maximum partial correlation coefficient between temperature and NPP (0.308), followed by radiation (0.113) and precipitation (0.067). The contributions of climate change and human activities to NPP changes were 1.298 and − 0.051 g C/m2·year−1, respectively. Climate change was the main driving factor for vegetation regeneration, while human factors were the main cause of vegetation reduction in the study area.

Quantitative assessment of spatiotemporal dynamics in vegetation NPP, NEP and carbon sink capacity in the Weihe River Basin from 2001 to 2020

Vegetation carbon storage is crucial role in terrestrial ecosystems, impacting material and energy conversion processes. The combined influence of climate change and anthropogenic activities underscores their significance in achieving carbon neutrality. Understanding regional climate change necessitates the examination of spatiotemporal variations in net primary productivity (NPP) and net ecosystem productivity (NEP) of vegetation, as well as quantifying the relative contributions of climate change and anthropogenic activities to vegetation dynamics. This study conducts a comprehensive analysis of NPP and NEP changes and influencing factors in the Weihe River Basin (WHRB) employing various analytical techniques, including the Mann-Kendall (M-K) test, partial correlation analysis, and a land use and cover change (LUCC) matrix. The relative contributions of anthropogenic activities and climate change to NPP are investigated through a two-step linear regression approach. Results indicate a significant increase in NPP and NEP within the WHRB from 2001 to 2020, with both climate change and anthropogenic activities exerting positive impacts on vegetation growth. Conversion from grassland to forest results in the greatest net increase in NEP, while conversion to cropland leads to the largest net decrease. Climate change and anthropogenic activities contribute 4.6 gC·m−2·a−1 (64.16%) and 2.57 gC·m−2·a−1 (35.84%) to NPP, respectively. The capacity for vegetation carbon sink demonstrates an overall increase from 2001 to 2020, with higher values observed in the southern region and lower values in the northern region. The western region and areas along the river have transitioned from carbon sinks to carbon sources, while the potential for source-to-sink transformation has gradually intensified from the central to eastern, northern, and southeast regions. This study enhances the understanding of drivers and patterns of vegetation dynamics in response to climate change and anthropogenic activities in the WHRB. The findings provide valuable scientific insights for improving NPP and carbon sink capacities and advancing sustainable development objectives.

Carbon budget response to climate change varies with grassland type in Qilian Mountains, China

Carbon budget estimation is crucial for scientifically assessing the ecosystem’s carbon sequestration capacity. Grasslands in the Qilian Mountains are widely recognized as vulnerable and sensitive to ongoing climate change. However, it is still unclear how the carbon budget of different grassland types responds to climate change in this area. Here, we evaluated the net primary productivity (NPP), soil heterotrophic respiration (RH), and net ecosystem productivity (NEP) of grassland ecosystems in the Qilian Mountains. Correlation analysis, linear regression, and standardized coefficient methodologies were employed to explore the response of NPP, RH, and NEP in various grassland types to changes in temperature, precipitation, and solar radiation over the past two decades. The results showed that the alpine meadow has the highest NPP and NEP while the temperate typical steppe has the highest RH. Grassland NPP, RH, and NEP increased significantly from 2001 to 2020, with a distinct spatial pattern of being low in the northwest and high in the southeast of the Qilian Mountains. NPP and NEP correlated with precipitation and solar radiation more closely than with temperature. Precipitation and solar radiation, respectively, explained 43.7% and 52.9% of grassland NEP alterations, and there was a threshold effect on the response of NEP and NPP to climate factors. Specifically, the NPP and NEP increased at first and then decreased as the precipitation, temperature, and solar radiation increased. The highest NEP and NPP occurred when precipitation ranged from 415 to 455 mm, along with temperatures between − 0.1–1.5 ℃ and solar radiation reaching 7685–7905MJ/m2. The warming and humidification of climate is conducive to the increase of NPP and NEP in alpine meadows and alpine steppes, but when the precipitation exceeds 415 mm and the temperature is greater than 1.5 ℃, the NPP and NEP begin to decrease. Solar radiation dominated changes in NPP and NEP of alpine meadows, alpine steppes, and temperate desert steppes. With the increase of solar radiation, NPP and NEP of these three grasslands first increased and then decreased. However, the changes in NPP and NEP of the temperate typical steppe were most affected by precipitation, and NPP and NEP first increase and then decrease with the increase of precipitation The higher the temperature, the higher the NPP, RH, and NEP of the temperate typical steppe. This study helps better understand how climate change impacts the carbon budget components of different grassland types in the Qilian Mountains and provides more theoretical thoughts on managing local grasslands sustainably.

Quantifying the impacts of natural and human factors on changes in NPP using an optimal parameters-based geographical detector

Investigating the driving mechanisms behind fluctuations in vegetation net primary productivity (NPP) has the potential to enhance our comprehension of ecosystem dynamics and their response to environmental changes. However, identifying the nonlinear and spatiotemporal heterogeneity of factors contributing to NPP variation remains a challenge. This research employed the Theil-Sen trend, Hurst index, and nonparametric Mann-Kendall test methodologies through the utilization of Google Earth Engine (GEE) to detect monotonic trends in NPP, distinguishing between upward and downward trends. The influence of individual factors and their interactive effects on NPP changes were quantified using the optimized parameter-based geographical detector (OPGD) model. The findings revealed a general upward trend in NPP, exhibiting an average growth rate of around 85.06 gC m−2.a-1. Nevertheless, these rates of growth were not uniform, leading to noteworthy fluctuations spanning from 447.44 to 543.69 gC m−2.a-1. The eastern and central regions showcased relatively elevated NPP, whereas the western and southern regions demonstrated comparatively reduced levels. Roughly 51.66% of the areas displayed a rising trend in NPP, with 44.81% of the entire area indicating a noteworthy increase (p < 0.01), including both a substantial increase (p < 0.01) and a moderate increase (0.01 ≤ p < 0.05). The areas witnessing heightened NPP were predominantly situated in the northeastern, southeastern, northwestern sections, and the southwestern portion of Sichuan. The fluctuations in NPP trends displayed mild persistence or slight antipersistence traits, with regions where 0 < H < 0.5 constituting 80.26% of the overall area. Natural factors (such as elevation, mean annual temperature, NDVI, and topographic relief) along with human influences (changes in land use type) were identified as effectively accounting for the fluctuations in NPP. These factors demonstrated interactive effects on NPP, with the synergistic effect resulting in nonlinear enhancement and bilinear enhancement effects. The interaction between these two factors strengthened the influence of each individual factor. Identifying the optimal characteristics or ranges of these factors can facilitate ecological conservation and vegetation restoration. These factors exhibited interactive effects on NPP, with the synergistic effect resulting in nonlinear enhancement and bilinear enhancement effects. The interplay between these two factors heightened the influence of each separate element. Determining the optimal attributes or ranges of these factors can contribute to the facilitation of ecological preservation and the restoration of vegetation.

How are nature-based solutions contributing to the improvement of ecosystem quality in China: A systematic review

Nature-based solutions (NbS), an upgraded version of ecological restoration concept, has developed as an integrated approach to encompass a broad range of actions that protect, restore, or sustainably manage ecosystems to provide benefits to people. However, it is challenging to obtain a thorough understanding of the effectiveness of NbS because of the uncertainty of the assessment in different regions based on ecosystem services. Here, we conduct a synthesis analysis of the effects of ecological interventions on ecosystem quality within China. We systematically analyze 76 studies selected from 275 peer-reviewed papers, which were undertaken at regional scale and published between 2011 and 2022. Average values of ecosystem services before and after the ecological interventions are extracted to quantify the ecosystem changes. Results show that ecological restoration purposefully contributes to the improvements of ecosystem services with an increase in Net Primary Productivity (NPP) of 31% and an increase in soil conservation (SC) of 21%. Effects on water yield (WY), carbon storage (CS) and habitat quality (HQ) are all not significant. Restoration interventions have a mixed effect on ecosystem service trade-offs which may vary by regions. Trade-offs primarily exist between WY and other ecosystem services on the Northeastern Forest Belt and the Southwestern Karst, while synergies mainly exist among NPP, SC and CS on the Loess Plateau and the Qinghai-Tibet Plateau. Moreover, utilizing grey correlation analysis methods, factor analysis reveals that temperature and precipitation are highly correlated with the changes of the five ecosystem services. Natural factors contribute more to the changes of NPP, SC and WY than the human factors. This research is aimed at obtaining an overall perception of the effectiveness of ecological restoration programmes implementation in different regions across China, which will provide references for reducing ecosystem services trade-offs and adjusting the layouts of regional ecological restoration programmes.

Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China

Net primary productivity (NPP) is an important index with which to evaluate the safety and quality of regional carbon sinks. Based on the improved CASA model, climate data, social data, remote-sensing ecological data, and other multi-source data types, this article took a Chinese city, Yangzhou, as the research object, used Theil–Sen medium-trend analysis and the Hurst index to analyze its spatial–temporal-evolution characteristics and future change trends, and used geographical detectors to analyze the impact of climate, social, ecological, and other factors on the change in NPP in the study area, with the intention of providing a theoretical exploration and practical basis for achieving the “dual carbon” goals in the region. The results showed that the annual average NPP levels of the vegetation in Yangzhou in the five sampling years were 445.343 gc/m2·a, 447.788 gc/m2·a, 427.763 gc/m2·a, 398.687 gc/m2·a, and 420.168 gc/m2·a, respectively, exhibiting a trend that first decreases and then increases, with a slight overall decrease from 2000 to 2020. The area in which the vegetation in Yangzhou had the higher grades of NPP increased by 203,874 km², and an increase of 321,769 km² in the lower levels was observed. The NPP level of vegetation showed polarization, with relatively high levels in the surrounding farmland and mountain–forest areas and relatively low levels in densely populated urban areas. The ranking was highest in Baoying and lowest in Gaoyou. From the average NPP of all the land types in the study area, the following trend was exhibited: forest land &gt; farmland &gt; bare soil &gt; impermeable surface &gt; water. The future change in vegetation NPP in Yangzhou City will mainly follow the trend of the past 20 years, with a slow decrease. The NDVI (q = 0.728) and LUCC (q = 0.5601) were the leading driving factors of vegetation NPP change in Yangzhou City, and the interaction effect of double driving factors was greater than that of single driving factors.

Analysis of Net Primary Productivity Variation and Quantitative Assessment of Driving Forces-A Case Study of the Yangtze River Basin.

Net primary productivity (NPP) can indirectly reflect vegetation's capacity for CO2 fixation, but its spatiotemporal dynamics are subject to alterations to some extent due to the influences of climate change and human activities. In this study, NPP is used as an indicator to investigate vegetarian carbon ability changes in the vital ecosystems of the Yangtze River Basin (YRB) in China. We also explored the NPP responses to climate change and human activities. We conducted a comprehensive analysis of the temporal dynamics and spatial variations in NPP within the YRB ecosystems from 2003 to 2020. Furthermore, we employed residual analysis to quantitatively assess the contributions of climate factors and human activities to NPP changes. The research findings are as follows: (1) Over the 18-year period, the average NPP within the basin amounted to 543.95 gC/m2, displaying a noticeable fluctuating upward trend with a growth rate of approximately 3.1 gC/m2; (2) The areas exhibiting an increasing trend in NPP account for 82.55% of the total study area. Regions with relatively high stability in the basin covered 62.36% of the total area, while areas with low stability accounted for 2.22%, mainly situated in the Hengduan Mountains of the western Sichuan Plateau; (3) NPP improvement was jointly driven by human activities and climate change, with human activities contributing more significantly to NPP growth. Specifically, the contributions were 65.39% in total, with human activities contributing 59.28% and climate change contributing 40.01%. This study provides an objective assessment of the contributions of human activities and climate change to vegetation productivity, offering crucial insights for future ecosystem development and environmental planning.

Dynamic changes in net primary productivity of marsh wetland vegetation in China from 2005 to 2015

Wetlands are one of the world's three major ecosystems. Marsh wetland vegetation, with its unique characteristics, is playing an increasingly prominent role in absorbing atmospheric carbon dioxide and mitigating climate warming. This study estimated the annual, seasonal, and monthly trends of marsh wetland vegetation net primary productivity (NPP) in China from 2005 to 2015 based on the Carnegie–Ames–Stanford approach (CASA) model. The temporal and spatial characteristics of NPP were analyzed and the driving factors were discussed. The results showed that the annual marsh wetland vegetation NPP had an increasing trend from 2005 to 2015 on national scale in China, with the lowest and highest values in 2005 (202.03 gCm−2a−1) and 2015 (222.58 gCm−2a−1), respectively. The monthly seasonal variations in marsh wetland vegetation NPP consistent with the typical growth pattern of vegetation observed in China. Monthly marsh wetland vegetation NPP showed a unimodal distribution, and May to September were the main months for NPP accumulation. The highest vegetation NPP values occurred during the summer season. Marsh wetland vegetation types did impact the NPP distribution, Gramineous marsh vegetation contributed most to the total NPP (29%). Spatially, the NPP was higher in east than in the west, and higher in the center than in the south and north. An overall positive impact of precipitation and temperature on the Chinese marsh wetland vegetation NPP was revealed. The study recommended to give greater attention the conservation of Gramineous vegetation alongside the overall protection of Chinese marsh wetlands. Our findings can offer important evaluation results and guidance for the future addressing climate change and conservation and maintenance of the carbon sink capacity of marsh wetland vegetation in China.

Beech bark disease does not reduce the long-term wood production of two forests contrasting in age, productivity, and structure

The distribution of pests and pathogens is increasing in many forested regions, producing uncertainty for ecological functions, including aboveground wood net primary production (NPP). In North American deciduous forests, beech bark disease (BBD) is restructuring and modifying the composition of forest stands, producing gradients of Fagus grandifolia mortality at finer patch scales. We investigated the multi-decadal effects of BBD on the aboveground wood NPP of a moderately productive middle-successional stand positioned on a glacial outwash plain and a relatively high productivity late-successional stand located on a moraine. Despite average stand-scale basal area losses of ∼ 21% from BBD, aboveground wood NPP increased over time in both the middle- and late- successional stands. At the patch scale, the initial magnitude of change in aboveground wood NPP following BBD infestation correlated with the extent of recovery in the late, but not middle, successional stand, suggesting early responses to disturbance sometimes – but not always – predict long-term production patterns. Patch-scale aboveground wood NPP during different stages of BBD infestation was associated with vegetation quantity and production efficiency, with the latter generally increasing in later stages of the BBD progression. We conclude that the aboveground wood NPP of two forest stands increased through late stages of BBD, despite differences in stand productivity, structure, and age, while patch-scale aboveground wood NPP responses were more variable.

Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China

The objective of this study is to analyze the spatiotemporal dynamics of net primary production (NPP) change combined with land use and to further explore the driving factors of NPP change, allowing us to provide a scientific reference point for optimizing the land-use structure and improving regional carbon sequestration capacity. The average annual NPP ranged from 200 to 300 gC/m2•a in the period of 2001–2020 in our study area. We used trend analysis and linear regression analysis to explore the spatial and temporal dynamic changes in annual NPP and analyzed the driving mechanism in a semiarid region (western Jilin Province) of China in the period of 2001–2020. The results showed that NPP presented a trend of fluctuating growth, and the spatial distribution of NPP showed that NPP values of cultivated land, forest and grassland were generally higher than those of other land-use types. The high value in the southeastern region and low value in the northwestern region were identified because there were large areas of cultivated land distributed in the southeastern part of the study area in the period of 2001–2020. The main driving factors that affected NPP were annual precipitation, CO2 emissions, GDP and hours of sunshine. NPP was positively correlated with annual precipitation, CO2 emissions and GDP, and it had a significant negative correlation with hours of sunshine. Our study provides important support for research into land-use structure and improvements to the regional carbon sequestration capacity, making an important contribution to regional sustainable development.

Impacts of urbanisation on vegetation dynamics in Chinese cities

Urbanisation is a global phenomenon that dramatically impacts ecosystems and their services. In recent years, urbanisation in China increased substantially. To improve the urban environment, the Chinese government has implemented several programs to green the cities' central areas and limit forest and grasslands in expanding areas. However, more information is needed about the trend and the factors affecting vegetation status in urban centres and expanding areas. Here we studied the vegetation in these areas in 3345 cities in China using 30 m spatial resolution Annual Net Primary Production (NPP) Change (ANC) and NPP Change Rate (ANCR) in 2000 and 2020. The results showed that most Chinese cities' central areas have a greening trend. However, vegetation cover decreases in expansion areas, mainly in southern cities. The opposite is observed in some urban areas in the country's North. This is mostly attributed to the highest urbanisation trends observed in the South than in the North. The most important factors that affected the ANC trend were mean temperature/precipitation, expansion area, and CO2 changes. ANCR was mainly affected by mean temperature/precipitation. Political decisions such as increasing the greening areas in city centres in China also affected the increase of vegetation in urban cores. In some cities located in the North, the investment in green infrastructure in expansion areas may have also contributed to increased vegetation. The results are important for managers to improve cities' livability, especially in the South of China, where the degradation of the ecosystems is high in expansion areas.