Changes In Primary Production Research Articles

Uncertainty in land use obscures global soil organic carbon stock estimates

The impact of land use and land cover change (LULCC) on soil organic carbon (SOC) stock is one of the most uncertain items in estimating the global C budget. Despite the improvements in satellite monitoring techniques and inventory data in recent decades, the uncertainty in modeled LULCC-induced SOC changes stemming from the choice of land use datasets remains largely unknown. Using a process-based model, the Dynamic Land Ecosystem Model (DLEM), we investigated global SOC changes during 1900–2018 driven by six LULCC datasets (i.e., LUH2-GCB2019, ESA CCI-LC, MODIS, GLASS-GLC, HH, and RF), which were generated by varied data sources and methodologies. The simulated global SOC stock was negatively affected by land conversions in all the LULCC datasets; however, the corresponding SOC loss was highly different ranging from 33.11 Pg C to 106.20 Pg C. Such significant differences in the global SOC stock estimates due to LULCC uncertainty were mainly located in boreal and temperate forests of the northern high latitudes and were most likely attributed to the LULCC-induced changes in vegetation net primary production. Meanwhile, regions exhibiting large divergence in relative changes of LULCC-induced SOC loss were mainly located in the low latitudes. When considering the interactive effects of LULCC with other environmental factors, the simulated SOC showed divergent trends, increasing in MODIS-, ESA CCI-LC-, and GLASS-GLC-based estimations, but decreasing in LUH2-GCB2019-, HH-, and RF-based estimations. These results highlight the importance of the accuracy of LULCC data in determining the global carbon budget. Future efforts are required for harmonizing satellite observations and inventory data both spatially and temporally to better represent the land conversion processes in terrestrial ecosystem modeling.

Climate warming negatively affects plant water-use efficiency in a seasonal hydroperiod wetland

Climate warming has substantial influences on plant water-use efficiency (PWUE), which is defined as the ratio of plant CO2 uptake to water loss and is central to the cycles of carbon and water in ecosystems. However, it remains uncertain how does climate warming affect PWUE in wetland ecosystems, especially those with seasonally alternating water availability during the growing season. In this study, we used a continuous 10-year (2011-2020) eddy covariance (EC) dataset from a seasonal hydroperiod wetland coupled with a 15-year (2003-2017) satellite-based dataset (called PML-V2) and an in situ warming experiment to examine the climate warming impacts on wetland PWUE. The 10-year EC observational results revealed that rising temperatures had significant negative impacts on the interannual variations in wetland PWUE, and increased transpiration (Et) rather than changes in gross primary productivity (GPP) dominated these negative impacts. Furthermore, the 15-year satellite-based evidence confirmed that, in the study region, climate warming had significant negative consequences for the interannual variations in wetland PWUE by enhancing wetland Et. Lastly, at the leaf-scale, the light response curves of leaf photosynthesis, leaf Et, and leaf-scale PWUE indicated that wetland plants need to consume more water during the photosynthesis process under warmer conditions. These findings provide a fresh perspective on how climate warming influences carbon and water cycles in wetland ecosystems.

Bioen-OSMOSE: A bioenergetic marine ecosystem model with physiological response to temperature and oxygen

1.Marine ecosystem models have been used to project the impacts of climate-induced changes in temperature and oxygen on biodiversity mainly through changes in species spatial distributions and primary production. However, fish populations may also respond to climatic pressures via physiological changes, leading to modifications in their life history that could either mitigate or worsen the consequences of climate change.2.Building on the individual-based multispecies ecosystem model OSMOSE, Bioen-OSMOSE has been developed to account for high trophic levels’ physiological responses to temperature and oxygen in future climate projections. This paper presents an overview of the Bioen-OSMOSE model, mainly detailing the new developments. These consist in the implementation of a bioenergetic sub-model that mechanistically describes somatic growth, sexual maturation and reproduction as they emerge from the energy fluxes sustained by food intake under the hypotheses of a biphasic growth model and plastic maturation age and size represented by a maturation reaction norm. These fluxes depend on temperature and oxygen concentration, thus allowing plastic physiological responses to climate change.3.To illustrate the capabilities of Bioen-OSMOSE to represent realistic ecosystem dynamics, the model is applied to the North Sea ecosystem. The model outputs are confronted with population biomass, catch, maturity ogive, mean size-at-age and diet data of each species of the fish community. The model succeeds in reproducing observations, with good performances for all indicators. A first exploration of current spatial variability in species’ bioenergetic fluxes resulting from temperature, oxygen, and food availability is presented in this paper, highlighting the role of temperature.4.This new model development opens the scope for new fields of research such as the exploration of seasonal or spatial variation in life history in response to biotic and abiotic factors at the individual, population and community levels. Understanding such variability is crucial to improve our knowledge on potential climate change impacts on marine ecosystems and to make more reliable projections under climate change scenarios.

Exploring quantification and analyzing driving force for spatial and temporal differentiation characteristics of vegetation net primary productivity in Shandong Province, China

Exploring the spatiotemporal changes of vegetation net primary productivity (NPP) and its driving mechanism is crucial for green and low-carbon development. Based on the MOD17A3HGF data, this study introduced the gravity center model, wavelet analysis, coefficient of variation, Hurst index, correlation analysis, and Thornthwaite Memorial model to explore the spatiotemporal patterns of NPP and its driving factors in Shandong Province from 2000 to 2019. The following results were attained. (1) Between 2000 and 2019, Shandong’s yearly average NPP showed an upward trend. The first main cycle of the NPP’s interannual cycle lasted 14 years. NPP was predicted to experience a short-term decline in the near future. (2) In Shandong, the NPP displayed a geographical pattern with high values in the east and low values in the west, high values in the south and low values in the north, and progressively declining values from the coast to the interior. The growth rate and increment of NPP in 2000–2019 were the largest in the northeast direction in the Southwest Plain, while those in the Jiaodong Hills, the Central and Southern Mountains, and the Northwest Plain were the largest in the northwest direction. Hurst index analysis showed that NPP would show weak anti-continuity changes in the Central and Southern Mountains and the Jiaodong Hills in the future. (3) Shandong’s NPP exhibited a “three zones and two lines” pattern of the effect of climate change and human activities. This research offers a scientific theoretical foundation for environmental protection and carbon management.

Fluctuations of continental chemical weathering control primary productivity and redox conditions during the Earliest Cambrian

The Ediacaran – Cambrian transition witnessed dramatic changes in the marine environment that are thought to have strongly influenced biological evolution. Previous studies have linked biological innovations to palaeo‐redox changes and suggested that increased productivity is the primary driver of the marine environment. However, the primary productivity and redox control mechanism still need to be clarified. Here, we present new organic carbon isotopes, total organic carbon contents, major elements, and trace and rare earth elements in drill‐core ZK107 from South China to reveal the continental chemical weathering, primary productivity, and redox control mechanism during the Earliest Cambrian (E‐C). Our studies have shown intense continental weathering, and primary productivity fluctuations that were supported by weathering (CIA, Rb/Al) and productivity (Babio, P) indicators. Additionally, the quantitative reconstructed productivity proxies (PP18, PP21, PP23) indicate a high primary productivity level in the E‐C ocean, surpassing that of the modern oceans. The strengthening of continental weathering mainly causes high primary productivity because it can alter the nutrient supply to the oceans. We suggest that changes in primary productivity closely associated with continental weathering are the leading cause of marine anoxia during the E‐C. This study establish a linkage between the land and marine system during the E‐C, confirming the terrestrial controls on the marine chemical changes and providing new insights into understanding the evolution of the Earth's surface changes during the Ediacaran – Cambrian transition.

Evolution of spatiotemporal patterns in vegetation net primary productivity and the driving forces on the Loess Plateau

In this study, we determined whether changes in vegetation net primary productivity (NPP) can be used to characterize the quality of terrestrial ecosystems, which is critical for global change and carbon balance. We first explored the spatial correlation of NPP and its impact on vegetation restoration. MOD17A3 remote sensing products were used to analyze the temporal and spatial changes in NPP on the Loess Plateau (LP) over the last two decades (2000–2020). The resulting spatial autocorrelation indices identified cold and hot spots in the spatial clustering patterns. The effects of climate change and human activities on the anomalous clustering of NPP were assessed using Pearson correlation analysis and multi-temporal land use land cover data. The results indicate that i) Temporally, from 2000 to 2020, the NPP of the LP increased significantly by 6.88 gCm−2yr−1 and so did the proportion of revegetated land area >400 gCm−2yr−1 from 4% to 37%. Spatially, NPP showed an increasing trend from northwest to southeast. ii) The vegetation NPP on the LP showed a strong positive global spatial autocorrelation (p< 0.01). The hot and cold regions were polarized; the cold spots were clustered in the northwest, while the hot spots in the south and east. The spatial clustering patterns were dominated by high-high (HH) and low-low (LL) clusters. Abnormal patterns existed mainly in the transition areas between HH and LL clusters and insignificant regions, which were jointly affected by human activities and climate change. iii) Precipitation was the dominant climatic factor (86%) affecting the NPP variation in the LP, with the annual minimum precipitation showing a significantly positive relationship with the interannual variability in NPP, while the maximum precipitations greatly influenced the variation in local spatial anomaly patterns. This suggests that climatic extremes affect vegetation. Our study helps to facilitate green ecological management and high-quality development in the LP.

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.

Changes in Net Primary Productivity and Factor Detection in China’s Yellow River Basin from 2000 to 2019

Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China’s Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB.

Changes in dryland areas and net primary productivity in China from 1980 to 2020

Changes in dryland areas and net primary productivity in China from 1980 to 2020

Increasing precipitation promoted vegetation growth in the Mongolian Plateau during 2001–2018

Taking the Mongolian Plateau as the research area, this paper studied the vegetation growth from 2001 to 2018. We quantified the vegetation growth changes based on changes in gross primary productivity (GPP) and leaf area index (LAI) and their relationships to climate variables using correlation analysis, partial correlation analysis and multiple correlation analysis. The results showed that from 2001 to 2018 both GPP and LAI showed an increasing trend, with great heterogeneities among different areas and land cover types. The largest increase of GPP and LAI occurred in the northeast plateau with the land cover types of forest and cropland. The main driving factor of vegetation growth was precipitation, while temperature was significantly negatively correlated with vegetation growth. The CO2 concentration had a significant impact on the GPP in farmland, and the increase of solar radiation had a significant impact on tundra. Our study highlights the importance of precipitation in regulating vegetation growth in the Mongolian Plateau, challenging the prevailing views that the temperature dominates the vegetation growth in the northern ecosystems.

Density of Tree Wood and Bark in Climatic Gradients of Eurasia

Under the conditions of climate changing, the biospheric role of forest cover is increasing, as well as the relevance of research on the carbon depositing capacity of the world’s forests. These studies include an assessment of the trees’ and stands’ biological productivity, which includes not only phytomass, but also the basic density (BD) of stem wood and bark. In our study, allometric models of the BD of wood and bark of 9 forest-forming tree species of Northern Eurasia have been developed, including such independent variables as the tree age, the stem diameter, as well as the average temperature of January and average annual precipitation. The structure of a mixed-effects model is applied, in which the affiliation of the source data to each of the tree species is encoded by a set of dummy variables. Based on the space-for-time substitution principle, the obtained patterns of BD changes in spatial climatic gradients are used to predict their changes in temporal gradients. The effect of Liebig’s law of limiting factor in predicting BD in spatial and temporal climatic gradients has been confirmed. The revealed patterns of changes in the BD of wood and bark in temperature and precipitation gradients completely repeat the previously established patterns of changes in phytomass and net primary production of trees and stands of Eurasia in the same gradients. This means that the climatic conditionality of the studied indicators of biological productivity has a common nature for both quantitative and qualitative indicators of trees and stands.

Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau

In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (NPP) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest > meadow steppe > coniferous forest > cropland > shrub > typical steppe > sandy land > alpine steppe > desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) > precipitation (0.33) > soil moisture (0.16) > temperature (0.14) > solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.

Coastal upwelling drives ecosystem temporal variability from the surface to the abyssal seafloor

Long-term biological time series that monitor ecosystems across the ocean's full water column are extremely rare. As a result, classic paradigms are yet to be tested. One such paradigm is that variations in coastal upwelling drive changes in marine ecosystems throughout the water column. We examine this hypothesis by using data from three multidecadal time series spanning surface (0 m), midwater (200 to 1,000 m), and benthic (~4,000 m) habitats in the central California Current Upwelling System. Data include microscopic counts of surface plankton, video quantification of midwater animals, and imaging of benthic seafloor invertebrates. Taxon-specific plankton biomass and midwater and benthic animal densities were separately analyzed with principal component analysis. Within each community, the first mode of variability corresponds to most taxa increasing and decreasing over time, capturing seasonal surface blooms and lower-frequency midwater and benthic variability. When compared to local wind-driven upwelling variability, each community correlates to changes in upwelling damped over distinct timescales. This suggests that periods of high upwelling favor increase in organism biomass or density from the surface ocean through the midwater down to the abyssal seafloor. These connections most likely occur directly via changes in primary production and vertical carbon flux, and to a lesser extent indirectly via other oceanic changes. The timescales over which species respond to upwelling are taxon-specific and are likely linked to the longevity of phytoplankton blooms (surface) and of animal life (midwater and benthos), which dictate how long upwelling-driven changes persist within each community.

Atmospheric Input and Seasonal Inventory of Dissolved Iron in the Sargasso Sea: Implications for Iron Dynamics in Surface Waters of the Subtropical Ocean

AbstractConstraining the role of dust deposition in regulating the concentration of the essential micronutrient iron in surface ocean waters requires knowledge of the flux of seawater‐soluble iron in aerosols and the replacement time of dissolved iron (DFe) in the euphotic zone. Here we estimate these quantities using seasonally resolved DFe data from the Bermuda Atlantic Time‐series Study region and weekly‐scale measurements of iron in aerosols and rain from Bermuda during 2019. In response to seasonal changes in vertical mixing, primary production and dust deposition, surface DFe concentrations vary from ∼0.2 nM in early spring to >1 nM in late summer, with DFe inventories ranging from ∼30 to ∼80 μmol/m2, respectively, over the upper 200 m. Assuming the upper ocean approximates steady state for DFe on an annual basis, our aerosol and rainwater data require a mean euphotic‐zone residence time of ∼0.8–1.9 years for DFe with respect to aeolian input.

Sedimentary DNA and pigments show increasing abundance and toxicity of cyanoHABs during the Anthropocene

Abstract Cyanobacterial harmful algal blooms (cyanoHABs) are assumed to be increasing in abundance and toxicity, but comprehensive analysis of change through time is limited, in part, because some key taxa (e.g., Microcystis) leave ambiguous evidence of historical abundance and toxicity. Sedimentary DNA (sedDNA) can allow the reconstruction of the cyanobacteria community as well as the frequency of genes specific to cyanotoxin production, enabling us to determine which taxa are present and their potential for toxin‐production. Using a combination of droplet digital polymerase chain reaction (ddPCR) and high‐throughput sequencing (HTS), we quantified the abundance of cyanobacterial genes of known function and changes in cyanobacteria taxa from sedDNA over the last century in nine lakes along a gradient of lake size, depth and trophic state in Minnesota, U.S.A. Using ddPCR, we quantified genes associated with microcystin toxin‐producing potential (mcyE), total cyanobacteria (CYA, 16S rRNA) and the genus Microcystis (MICR, 16S rRNA). Using HTS on a subset of lakes, we investigated how the abundance of this toxin‐producing gene covaried with the cyanobacteria community composition. We also compared ddPCR and HTS data to fossil pigments, a well‐established palaeolimnological method used to track changes in primary producers over time. Our results showed a significant correlation between MICR and the quantity of mcyE gene and cyanobacterial taxa with known toxin‐production potential. The abundance of both genes likewise increased concomitantly through time. Community analyses of HTS data showed significant change in cyanobacterial communities commencing c. 1950 when major land‐use change in this region led to increased lake productivity, and c. 1990 when Dolichospermum and Microcystis genera increased in abundance, and the subtropical exotic cyanobacteria Raphidiopsis raciborskii and Sphaerospermopsis aphanizomenoides became abundant. Cyanobacteria pigment data reflected these changes only in deeper lakes, suggesting issues related to benthic production or biomarker preservation in shallower systems. This study provides evidence for historical development of increasingly toxic cyanoHABs across a diverse set of lakes and illustrates how sedDNA may help link changes in the cyanobacteria community to the expression of potentially toxic genes.

Temporal changes in biomass production across different plant communities in alpine meadows of Tungnath, Garhwal Himalaya

We studied temporal changes in biomass and net primary productivity of alpine grasslands at Tungnath in Garhwal Himalaya. Five different communities with similar elevations were taken for investigation. The above-ground biomass samples were harvested as close to the ground as possible. Similarly, below-ground biomass samples were collected from harvesting monoliths (25 x 25 x 30 cm). Aboveground net primary production was determined as the sum of positive changes in biomass and belowground net primary production was calculated as the difference between peak belowground biomass and preceding minimum biomass. The live shoot biomass increased from May till September in all communities except Trachydium roylei and Polygonum, which peaked in August. The dead shoot and litter biomass first decreased and peaked in all communities in October. Belowground biomass first decreased and then increased towards the end of the growing season. The belowground to aboveground biomass ratio ranged between 0.61-3.08. The aboveground net productivity ranged between 454.8-294.3 g m-2, and the belowground net productivity ranged between 346.3-102.4 g m-2.

Effects of water depth on the biomass of two dominant submerged macrophyte species in floodplain lakes during flood and dry seasons

Floodplain lakes share characteristics of both deep and shallow lakes throughout any given year. Seasonal fluctuations in their water depth drive changes in nutrients and total primary productivity, which directly and indirectly affect submerged macrophyte biomass. To investigate how water depth and environmental variables affect submerged macrophyte biomass, we surveyed six sub-lakes in the Poyang Lake floodplain, China, during the flood and dry seasons of 2021. Dominant submerged macrophytes include Vallisneria spinulosa and Hydrilla verticillata. The effect of water depth on the biomass of these macrophytes varied between the flood and dry seasons. In the flood season, there was a direct effect of water depth on biomass, while in the dry season only an indirect effect was observed. During the flood season, the direct effect of water depth on the biomass of V. spinulosa was less than the indirect effect, with water depth primarily affecting the total nitrogen, total phosphorus and water column transparency. Water depth directly, positively affected H. verticillata biomass, with this effect being greater than the indirect effect by affecting the carbon, nitrogen and phosphorus content in the water column and sediment. During the dry season, water depth affected H. verticillata biomass indirectly through sediment carbon and nitrogen content, while for V. spinulosa, the effect on biomass was indirect through carbon content of the sediment and water column. The main environmental variables affecting submerged macrophyte biomass in the Poyang Lake floodplain during the flood and dry seasons, and the mechanisms through which water depth affects dominant submerged macrophyte biomass, are identified. An understanding of these variables and mechanisms will enable improved management and restoration of wetland.

Analysis of Changes in Vegetation Carbon Storage and Net Primary Productivity as Influenced by Land-Cover Change in Inner Mongolia, China

Exploring the influence of vegetation carbon stocks over land-cover change and the relationship between land-cover change and net primary production (NPP) is of great practical importance for achieving China’s carbon neutrality target. Therefore, this paper analyses the spatio-temporal distribution of land-cover change and NPP change in Inner Mongolia from 2000 to 2020 and explores the vegetation carbon stock change caused by land-cover change, the relationship between land cover and NPP, and the relationship between land-cover change and NPP change. The results show that the main land-cover change in Inner Mongolia during 2000–2020 was the conversion from other land-cover types to grassland, with the conversion of bare land to grassland from 2000 to 2010 covering an area of 20,634.87 km2. During the period of 2000–2020, the high values of NPP were mainly located in northeastern Inner Mongolia, and the low values were mainly distributed in southwestern Inner Mongolia. The total carbon storage changes in vegetation affected by land-cover change during 2000–2010 and 2010–2020 were 10,736,986.11 t and 3,894,272.41 t. The mean values of NPP for different land covers were, in descending order, forest > cultivated land > wetland > grassland > artificial surfaces > shrubland > water bodies > bare land. Between 2000 and 2010, the change in land-cover type to water bodies was the main cause of the decrease in NPP, and the increase in NPP was mainly due to the conversion of other land-cover types to cultivated land, forest, and grassland. The implementation of appropriate conservation and management measures (e.g., the construction of forest and grass ecological protection), planting, and the protection of high-vegetation carbon storage plants and other means can improve the level of vegetation carbon sequestration and protect the ecological environment of Inner Mongolia.

Simulation and Key Physical Drivers of Primary Productivity in a Temperate Lake during the Ice-Covered Period: Based on the VGPM Model

The primary productivity of seasonal ice-covered water bodies is an important variable for understanding how temperate lake ecosystems are changing due to global warming. But there have been few studies on the complete change process of primary productivity during the ice-covered period, and the connection between ice physical and associated biological production has not been fully understood. In this study, a Vertically Generalized Production Model (VGPM) suitable for the ice-covered period was used to calculate the primary productivity of a temperate lake, and the key physical controlling factor was analyzed in the process of primary productivity change in the ice-covered period. The results showed that there was a high level of primary productivity, (189.1 ± 112.6) mg C·m−2·d−1, under the ice in the study site, Hanzhang Lake. The phytoplankton production under the ice was not as severely restricted by light as commonly thought. The water temperature played a more crucial role in the changes of primary productivity than the light beneath the ice. The study highlighted the variability in primary productivity covering the whole ice-covered age, and provided a better understanding of how the aquatic environment of lakes in seasonal ice-covered areas was affected by warmer temperatures.

Quantifying the contributions of climate factors and human activities to variations of net primary productivity in China from 2000 to 2020

Net primary productivity (NPP) plays a vital role in the globe carbon cycle. Quantitative assessment of the effects of climate changes and human activities on net primary productivity dynamics is vital for understanding the driving mechanisms of vegetation change and sustainable development of ecosystems. This study investigates the contributions of climatic factors and human activities to vegetation productivity changes in China from 2000 to 2020 based on the residual trend analysis (RESTREND) method. The results showed that the annual average net primary productivity in China was 325.11 g C/m2/year from 2000 to 2020 and net primary productivity showed a significantly increasing trend (p<0.05) at a rate of 2.32 g C/m2/year. Net primary productivity increased significantly (p<0.05) across 40.90% of China over the study period, while only 1.79% showed a significantly declining trend (p<0.05). The contributions of climatic factors and human activities to net primary productivity increase were 1.169 g C/m2/year and 1.142 g C/m2/year, respectively. Climate factors contributed positively mainly in Sichuan Basin, the Loess Plateau, the Mongolian Plateau, and Northeast China Plain. Positive contributions of human activities to net primary productivity mainly occurred in the Loess Plateau, Central China, and the Greater Khingan Mountains. The effects of climatic factors and human activities on net primary productivity changes varied among sub-regions. In Tropical Monsoon Climate Region and Subtropical Monsoon Climate Region, human activities had greater impacts on net primary productivity increase than climate factors, while climate factors were the dominant factor for net primary productivity recovery in other sub-regions. In addition, during 2000–2020, net primary productivity was dominated by both climate factors and human activities in 49.84% of China, while areas dominated solely by climate factors and human activities accounted for 13.67% and 10.92%, respectively. Compared to changed land cover types, the total net primary productivity as well as the increase of total net primary productivity in China was mostly contributed by unchanged land cover types, which contributed more than 90%.