Ecosystem Net Primary Productivity Research Articles

Short-term effect of the harvesting method on ecosystem carbon budget in hemiboreal Scots pine forest: Shelterwood cutting versus clear-cut

Shelterwood cutting (SC) has been highlighted as an alternative method to clear-cut (CC)-based even-aged forest management. However, compared to CC, the effect of SC on stand carbon (C) balance is still poorly understood at the ecosystem level. We examined the prompt effect of SC versus CC on ecosystem net primary production (NEP) on a short-term scale, using the C budgeting method combined with eddy covariance (EC) measurements in hemiboreal mature Scots pine stands.The early effect of SC on annual C budget after removing 30–40 % of the growing stock revealed diverse patterns in the studied stands; NEP varied from 0.62 to −1.3 t C ha−1 yr−1 for the C sink and the C source, respectively. However, C loss decreased already in the second post-thinning year and levelled out attaining −0.41 t C ha−1 yr−1, which is close to balance.Furthermore, C loss declined in the second post-harvesting year at the CC sites as a result of the increased production of the ground vegetation and the decreased soil heterotrophic respiration (Rh) flux. However, C loss from dry mesotrophic clear-cuts was almost −1.8 t C ha−1 yr−1 for both study sites. Since estimation of the individual C fluxes for C budgeting is associated with variability-induced errors, the estimated values of NEP contain uncertainties of various levels.The estimated annual cumulative Rh flux was significantly higher in the SC versus CC areas and the mean annual Rh values across the study sites were 3.57 ± 0.25 and 2.59 ± 0.09 t C ha−1 yr−1, respectively.Annual estimated NEE after SC was 1.8 ± 0.52 t C ha−1 yr−1, gross primary ecosystem production was 9.28 ± 0.97 and total ecosystem respiration was 7.47± 0.28 t C ha−1 yr−1. The discrepancy between the estimated values of NEE and NEP was 1.2 t C ha−1 yr−1.In the short term SC demonstrated some advantage over CC from the perspective of the C cycle, but the difference in NEP values between the SC and CC treatments was not convincingly overwhelming. Hence, SC allows to maintain the forest cover for a longer period and to avoid drastic changes in the landscape; still, after SC, C budget varied between the C sink and the C source.

Predicting Ecosystem Net Primary Productivity by Percolation Theory and Optimality Principle

AbstractThe basic partitioning of precipitation P into evapotranspiration ET and run‐off Q is known as the “central problem of hydrology.” ET depends primarily on precipitation, P, and potential evapotranspiration, PET, which are connected by the biological process of photosynthesis. Photosynthesis is the fundamental step underlying the productivity of plant ecosystems. An important measure of plant productivity is the creation of plant biomass, which is quantified by net primary productivity, NPP. NPP is most parsimoniously related simply to the evapotranspiration, ET. The dependence of NPP (ET) appears to be in the form of a power law with an upper limit derived from the maximum plant‐available solar energy accessible on Earth. However, theoretical, rather than phenomenological, treatments relating ET and its complementary variable, run‐off (P − ET ≡ Q) to climate variables that measure the available energy (PET) and available water (P) (known as the water balance) are, at best, scarce, and their continued application to predicting NPP (P, PET) even more so. One theory developed recently to predict the water balance is based on determining how to divide P into Q and ET in such a way as to maximize NPP. Substitution of the resulting optimized ET (P, PET) into the function NPP (ET) then yields NPP (P, PET). We investigate the possibility that this new theoretical framework, which yields ET(P, PET) based on ecological optimality and percolation theory, can predict the dependence of NPP (P, PET). If the prediction of NPP (P, PET) is verified, this result may lead to significant progress in other areas, including the problem of the chief causes of geographical variability of species richness.

Trade-off of ecosystem productivity and water use related to afforestation in southcentral USA under climate change

The increase of tree canopy cover due to woody plant encroachment and tree plantations modifies both carbon and water dynamics. The tradeoffs between ecosystem net primary productivity (NPP) and water use with increasing tree cover in different climate conditions, particularly under future climate scenarios, are not well understood. Within the climate transition zone of the southern Great Plains, USA, we used the Soil and Water Assessment Tool+ (SWAT+) to investigate the combined impacts of increasing tree cover and climate change on carbon and water dynamics in three watersheds representing semiarid, subhumid, and humid climates. Model simulations incorporated two land use modifications (Baseline: existing tree cover; Forest +: increasing evergreen tree cover), in conjunction with two climate change projections (the RCP45 and the RCP85), spanning two time periods (historic: 1991–2020; future: 2070–2099). With climate change, the subhumid and humid watersheds exhibited a greater increase in evapotranspiration (ET) and a corresponding reduction in runoff compared to the semi-arid watershed, while the semi-arid and subhumid watersheds encountered pronounced losses in water availability for streams (>200 mm/year) due to increasing tree cover and climate change. With every 1 % increase in tree cover, both NPP and water use efficiency were projected to increase in all three watersheds under both climate change scenarios, with the subhumid watershed demonstrating the largest increases (>0.16 Mg/ha/year and 170 %, respectively). Increasing tree cover within grasslands, either through woody plant expansion or afforestation, boosts ecosystem NPP, particularly in subhumid regions. Nevertheless, this comes with a notable decrease in water resources, a concern made worse by future climate change. While afforestation offers the potential for greater NPP, it also brings heightened water scarcity concerns, highlighting the importance of tailoring carbon sequestration strategies within specific regions to mitigate unintended repercussions on water availability.

NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)

The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.

Assessment of ecosystem quality changes based on optimizing key indicators in Nanwenghe National Nature Reserve, Heilongjiang, China.

Net primary productivity (NPP) is an indicator to reflect the production capacity of terrestrial ecosystems, as well as a key indicator for ecological quality. NPP at large scale is difficult to be measured. At present, most of the assessment of ecosystem quality uses NPP products with low resolution, which cannot capture the detailed characteristics of the ecosystem and is not conducive to the assessment of ecosystem quality at small-scale. The establishment of a rapid and efficient assessment method for small-scale ecosystem quality will greatly promote the protection and restoration of ecosystems in China. We focused on the calculation method of ecosystem quality assessment and NPP, and optimized the calculation process of the NPP, and obtained NPP by remote sensing without ground observation data. We established a regression model for remote sensing inversion of leaf area index, and estimated the vegetation coverage by using dimidiate pixel model, developed a set of methods for rapid assessment of ecosystem quality by remote sensing. Taking Nanwenghe National Nature Reserve as an example, we evaluated the change of ecosystem quality from 2001 to 2022. The results showed that from 2001 to 2022, the ecosystem qua-lity of the reserve was good as a whole, and that the area with good and excellent quality accounted for more than 85% in 2022. High vegetation coverage was the backbone of the sustainable good ecosystem quality of the reserve. From 2001 to 2022, ecosystem quality of the reserve showed a trend of first decreasing and then increasing, with the lowest point of ecosystem quality in 2013. This method had achieved good results in the evaluation of ecosystem quality in Nanwenghe National Nature Reserve. The index optimization method proposed in this study could facilitate the rapid and accurate assessment of ecosystem quality in small-scale regions, and thus provides technical reference for the precise improvement of ecosystem quality.

Dynamic subcanopy leaf traits drive resistance of net primary production across a disturbance severity gradient

Across the globe, the forest carbon sink is increasingly vulnerable to an expanding array of low- to moderate-severity disturbances. However, some forest ecosystems exhibit functional resistance (i.e., the capacity of ecosystems to continue functioning as usual) following disturbances such as extreme weather events and insect or fungal pathogen outbreaks. Unlike severe disturbances (e.g., stand-replacing wildfires), moderate severity disturbances do not always result in near-term declines in forest production because of the potential for compensatory growth, including enhanced subcanopy production. Community-wide shifts in subcanopy plant functional traits, prompted by disturbance-driven environmental change, may play a key mechanistic role in resisting declines in net primary production (NPP) up to thresholds of canopy loss. However, the temporal dynamics of these shifts, as well as the upper limits of disturbance for which subcanopy production can compensate, remain poorly characterized. In this study, we leverage a 4-year dataset from an experimental forest disturbance in northern Michigan to assess subcanopy community trait shifts as well as their utility in predicting ecosystem NPP resistance across a wide range of implemented disturbance severities. Through mechanical girdling of stems, we achieved a gradient of severity from 0% (i.e., control) to 45, 65, and 85% targeted gross canopy defoliation, replicated across four landscape ecosystems broadly representative of the Upper Great Lakes ecoregion. We found that three of four examined subcanopy community weighted mean (CWM) traits including leaf photosynthetic rate (p = 0.04), stomatal conductance (p = 0.07), and the red edge normalized difference vegetation index (p < 0.0001) shifted rapidly following disturbance but before widespread changes in subcanopy light environment triggered by canopy tree mortality. Surprisingly, stimulated subcanopy production fully compensated for upper canopy losses across our gradient of experimental severities, achieving complete resistance (i.e., no significant interannual differences from control) of whole ecosystem NPP even in the 85% disturbance treatment. Additionally, we identified a probable mechanistic switch from nutrient-driven to light-driven trait shifts as disturbance progressed. Our findings suggest that remotely sensed traits such as the red edge normalized difference vegetation index (reNDVI) could be particularly sensitive and robust predictors of production response to disturbance, even across compositionally diverse forests. The potential of leaf spectral indices to predict post-disturbance functional resistance is promising given the capabilities of airborne to satellite remote sensing. We conclude that dynamic functional trait shifts following disturbance can be used to predict production response across a wide range of disturbance severities.

Net Primary Productivity Estimation of Terrestrial Ecosystems in China with Regard to Saturation Effects and Its Spatiotemporal Evolutionary Impact Factors

The net primary productivity (NPP) of vegetation holds a pivotal character for the global carbon balance as a key parameter for characterizing terrestrial ecological processes. The most commonly used indices for estimating vegetation NPP, for instance, the normalized difference vegetation index (NDVI), often suffer from saturation issues that can compromise the accuracy of NPP estimation. This research utilizes a new vegetation index based on the radial basis function (RBF) to estimate vegetation NPP in Chinese terrestrial ecosystems over the past two decades (2001–2020) and investigates the spatiotemporal variation characteristics of NPP and the driving mechanisms. The results indicate that the kernel vegetation index (kNDVI) can effectively alleviate the saturation problem and significantly improve the accuracy of NPP estimation compared to NDVI. Over the past two decades, the NPP of Chinese terrestrial vegetation ranged from 64.13 to 79.72 g C/m2, with a mean value of 72.75 g C/m2, showing a fluctuating upward trend. Changes in the NPP of terrestrial ecosystems in China are mainly affected by precipitation. The dominant factors influencing NPP changes varied over time and had different impacts. For instance, in the period of 2001–2005 the climate had a positive effect on NPP changes, with the dominant factors being evaporation and precipitation. However, in the period of 2010–2015 the dominant climate factors shifted to evaporation and temperature, and their effect on NPP changes became negative. The outcomes of this research aim to serve as a foundation for carbon cycle research and ecosystem environment construction in China.

Oxygen productivity of urban forests of Kyiv city as a constituent of its sustainable development

The oxygen-producing function of urban forests is an integral factor in creating favourable living conditions for the population in an urbanized environment. Managerial decisions aimed at the development of environmental potential in urban forests shall be based on sustainable development goals and ensure the balanced use of their ecosystem functions. As a result of the research, quantitative values of the oxygen productivity of urban forests in Kyiv have been computed. Also, we have analysed the influence of the main biometric characteristics of stands on the intensity of their oxygen production capacity. The informational basis for evaluating the oxygen productivity of urban forests consists of research data that reflects the silvicultural characteristics of more than 14,000 forested plots featuring different species composition, age, and site index class, growing in different types of conditions. As a methodological basis, we have applied the semi-empirical method, which involves assessing the net primary production of ecosystems. The proposed quantitative values of oxygen productivity in Kyiv’s urban forests serve as an information component in forming a sustainable development strategy for the preservation and extended restoration of forest ecosystems in the region.

Impact of climate change and land use change on ecosystem net primary productivity in the Yangtze River and Yellow River Source Region, China

Net ecosystem productivity (NEP) is an important indicator of the capacity of terrestrial ecosystems to absorb carbon. For the purpose of developing adaptation and mitigation strategies, it is essential to investigate the effect of climatic changes and anthropogenic activities on NEP. This study analyzed the spatiotemporal properties of NEP in the Yangtze River and Yellow River Source Region (SRYY) using remote sensing images, climate variable data, and land use data. Then it was examined at how climatic change affected carbon sinks and sources and how changes in land use affected carbon storage. According to the results, the SRYY contributed a carbon sink worth 35.26 Tg C annually from 2001 to 2019 (1 Tg = 1012 g). Influenced by climate, soil and vegetation, NEP displayed regional differences in space. The spatial distribution of NEP gradually increased from Northwest to Southeast. In recent twenty years, the NEP of SRYY slightly increased with a rate of 3.24 Tg C per decade. The NEP with an increasing trend accounted for 78.9% of the whole study area, which were widely distributed and especially significant in the west, such as Tuotuo River Basin. The correlation analysis between NEP and climate variables suggested that temperature was the dominant control of NEP in the SRYY. About 54.4% of the NEP variance across the entire SRYY was contributed by temperature. However, there was no significant linear relationship between NEP and annual precipitation. In recent two decades, built-up land expansion and ecological degradation reduced carbon sink by 28.61 Gg C (1 Gg = 109 g) and 1.94 Tg C, respectively. Grassland degradation caused by rodent infestation and grazing was the main factor of carbon sink reduction in some areas. The restoration of vegetation and wetland areas resulted in a 0.75 Tg C increase in the carbon sink. It might be benefited from warmer climate condition and ecological protection measures.

Elevated Wildfire and Ecosystem Carbon Loss Risks Due to Plant Hydraulic Stress Functions: A Global Modeling Perspective

Wildfire risks are increasing due to the atmospheric and vegetation aridity under global warming. Plant hydraulic stress (PHS) functions regulate water transport along the soil–plant–atmosphere continuum under water stress conditions, which probably results in shifts in ecosystem wildfire regimes. Currently, how the PHS functions affect wildfire occurrence and subsequently the ecosystem carbon cycle via carbon loss at a global scale remains unclear. Here, we conducted global simulations during 1850–2010 using Community Land Model version 5 with and without the PHS configuration and quantified the PHS-induced changes. From the global perspective, the PHS functions increased plant transpiration, induced hydraulic redistribution (HR) of soil water by root, and decreased soil moisture; then, the functions increased fire occurrence (count), fire induced carbon loss, and ecosystem net primary productivity by 72%, 49%, and 15%, respectively. Spatially, the PHS functions greatly promoted fire occurrence and the consequent carbon loss in circumboreal forests and tropical savannas; whereas, the fire occurrence was limitedly affected or even decreased in equatorial rainforests. The strong downward HR process in the humid rainforests transported rainwater into deep soil layers, and strict stomatal regulation of the tropical trees restricted transpiration increment under atmospheric aridity, both of which helped to buffer the rainforests against drought and thus decreased fire risk. In contrast, dry savannas showed substantial upward HR, which increased water loss via soil evaporation and transpiration of the grasses with shallow roots. The tree–grass competition for limited soil moisture in the savannas benefited soil evaporation, which could aggravate plant hydraulic failure and increase wildfire risk.

Allometry, Biomass and Productivity in Pinus massoniana Forests of China: An Updated Review of Published Data

Field measurements of forest biomass are labor-consuming and usually destructive, but such direct datasets are the foundation for further developing indirect estimation such as biomass equation and biomass expansion factors that were used to scale biomass estimation or carbon storage from site or local surveys up to regional or national level. Pinus massoniana is widely distributed throughout 17 provinces of China and has a high economic value. This study is an updated review of the allometric equations, biomass allocation data and productivity estimations in Pinus massoniana forests of China published in articles during the period from 1982 to 2019. Different allometric equations of Pinus massoniana showed site-specific dependences. The aboveground and total tree biomass were expressed as power equations of mean age for Pinus massoniana forests. The mean value of aboveground tree biomass, total tree biomass and ecosystem biomass across all ages for Masson pine forests was 109.81, 127.65 and 138.76 Mg/ha, respectively. The mean proportion of stem, branch and foliage to aboveground tree biomass for Pinus massoniana was 77.1%, 15.9% and 7%, respectively. The tree root biomass continuously increased with the aboveground tree biomass and a power relationship was found between the tree root biomass and aboveground tree biomass for Pinus massoniana forests. The tree net primary productivity increased with aboveground tree biomass for Pinus massoniana forests, and the relationship was expressed as a linear equation. The mean tree, litter and ecosystem net primary productivity was 7.04, 4.88 and 8.30 Mg/ha/year, respectively. These findings provide key parameters for the biomass estimation and carbon accounting studies of widely planted Pinus massoniana forests in China.

Net Primary Productivity of Forest Ecosystems in the Southwest Karst Region from the Perspective of Carbon Neutralization

The net primary productivity (NPP) of forest ecosystems is an important factor for understanding the carbon budget of forest ecosystems. There have been many studies on the spatialtemporal characteristics and change trends of the NPP in southwest areas based on observation and remote sensing models. A comprehensive view of the overall state of the research on the carbon budget and a deeper exploration of its laws and effects are needed to reach a clear conclusion and accurately assess the NPP of forests in southwest China. Therefore, in this paper, we aim to highlight the relevant achievements in the study of the NPP of forest ecosystems, to summarize the research progress and spatial–temporal distribution law of the estimation of the NPPs of forest ecosystems, to analyse the relationship between the NPP and climate factors (such as temperature, precipitation and VPD), and to identify the main unsolved issues and suggest directions for future research. Our summary is expected to serve as a reference for forest ecosystem carbon budget management in the southwest and as a starting place for further research, and to promote the realization of the “double carbon” goal.

The climate change mitigation potential of annual grasslands under future climates.

Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process-based biogeochemical model (DayCent) at a daily time step to explore the long-term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long-term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site-specific daily climate data from two Earth system models (CanESM2 and HadGEM-ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one-time amendment of compost to rangeland acted as a slow-release organic fertilizer and increased NPP by up to 390-814 kg C ha-1 year-1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha-1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N2 O) emissions, the cumulative net emissions (in CO2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO2 e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal-scale climate change mitigation goals alongside emissions reductions.

Leaf N:P ratio does not predict productivity trends across natural terrestrial ecosystems.

Nitrogen (N) and phosphorus (P) are crucial nutrients for regulating plant growth. The classic growth rate hypothesis (GRH) proposes that fast-growing organisms have lower N:P ratios, and it is promising to predict net primary productivity (NPP) using the leaf N:P ratio at the community level (N:PCom ). However, whether leaf N:P ratio can predict NPP in natural ecosystems on a large scale remains nebulous. Here, we systematically calculated leaf N:PCom (community biomass-weighted mean and species arithmetic mean) using the consistently measured data of 2192 plant species-site combinations and productivity (biomass-based aboveground NPP and flux-based NPP) in 66 natural ecosystems in China. Unexpectedly, leaf N:PCom hardly predicted productivity in natural ecosystems due to their weak correlation, although significantly negative or positive relationships across different ecosystems were observed. The ambiguous relationship between leaf N:P and species dominance reflected a luxury consumption of N and P in turnover and structure in natural communities, unlike what GRH suggests. Climate, soil, and leaf nutrients (rather than N:P) influenced productivity, which highlighted the importance of external environment and nutrient constrains. Our findings pose a major challenge for leaf N:PCom as a direct parameter in productivity models and further question the direct application of classic hypotheses in short-term experiments or model species to long-term and complex natural ecosystems.

Impact of Changing Winds on the Mauna Loa CO2 Seasonal Cycle in Relation to the Pacific Decadal Oscillation.

Long‐term measurements at the Mauna Loa Observatory (MLO) show that the CO2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22 ± 0.034 ppm decade−1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO2 fluxes from the Jena CarboScope CO2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of −0.10 ± 0.022 ppm decade−1 from 1959 to 2019, partly offsetting the observed long‐term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC‐ACTM simulations, the shorter‐term variability of MLO SCA is nearly equally driven by varying ecological CO2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U‐PDO). Since 1980, 44% of the wind‐driven SCA decrease has been tied to a secular trend in the U‐PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low‐latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO2 fluxes using observations from low latitudes due to the complexity of circulation changes.

Impact of climatic changes and anthropogenic activities on ecosystem net primary productivity in India during 2001–2019

Climate change and anthropogenic activities have altered the terrestrial ecosystem dynamics around the globe. Due to the complex ecosystem-atmosphere interactions at different scales, these impacts are difficult to quantify and are poorly understood, especially in developing countries with limited ground-based observations. This study analyzed the impact of climatic changes and anthropogenic activities on ecosystem net primary productivity (NPP) in India using remote sensing-based observations, correlation analysis, and Residual Trend analysis (RESTREND). Using different climate variables such as precipitation, temperature, and solar radiation, along with Land Use and Land Cover (LULC) and NPP maps, we first classified the ecosystems (ES) into two categories: natural ecosystems – influenced only by climate change (ESc), covering about 19.7% of the area, and human-influenced ecosystems – influenced by both climate change and anthropogenic activities (ESc+a), covering about 80.3% of the area. RESTREND analysis was performed on both ESc and ESc+a to analyze the relative contributions of climate change and human activities to changes in NPP. The correlation analysis between NPP and climate variables suggested that precipitation was the dominant control of NPP in about 72% area, whereas temperature and solar radiation controlled NPP in Himalayan and forest-dominated regions, respectively. The human-influenced ecosystems (ESc+a) experienced an increasing trend in NPP, whereas natural ecosystems (ESc) experienced a decreasing trend, particularly in forest-dominated regions. Overall, NPP increased in the country during the study duration. The contributions of climatic changes and anthropogenic activities varied spatially and temporally. In general, climatic factors enhanced the NPP, whereas human activities contributed to a slight decline in NPP. These findings improve our understanding of how ecosystems in India are influenced by climate change and anthropogenic activities in recent decades. The results from this study will aid in identifying ecological hotspots and key drivers for better ecosystem management strategies.

Elevational Gradient of Climate-Driving Effects on Cropland Ecosystem Net Primary Productivity in Alpine Region of the Southwest China

Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.

Change Trend and Restoration Potential of Vegetation Net Primary Productivity in China over the Past 20 Years

As an important vegetation parameter and ecological index, vegetation net primary productivity (NPP) can intuitively reflect changes in the ecological environment and the level of the carbon budget. However, the change trend of NPP and its recovery potential in China over the past 20 years remain unclear. Here, we used trend analysis, multiple regression analysis and residual analysis methods to analyse the change trend in the NPP of China’s terrestrial ecosystems from 2000 to 2019, as well as the climax background, restoration status and restoration potential of the NPP of forest, grassland and desert ecosystems. The results showed that (1) the change in vegetation NPP in China from 2000 to 2019 showed a continuous upward trend, with a change slope of 2.39 gC/m2/a2, and the area with a positive slope of change accounted for 68.10% of the country’s land area. The contribution rates of meteorological conditions and human activities to vegetation NPP changes were 85.41% and 14.59%, respectively. (2) The results obtained by the regression analysis method of meteorological conditions based on nature reserves could reflect the zonal climax vegetation status to a large extent, and the obtained values had a smooth transition within each ecogeographical division and between each ecogeographical division, which truly reflected the law of gradual change in climate, vegetation and natural conditions. The annual total NPP of the climax background vegetation in China’s forest, grassland and desert ecosystems was approximately 2.76 ± 0.28 PgC, and the annual total NPP of the three ecosystems was 1.90 ± 0.2 PgC, 0.80 ± 0.07 PgC and 0.009 ± 0.0005 PgC, respectively. (3) The annual total vegetation NPP of the restoration status of China’s forest, grassland and desert ecosystems was 2.24 PgC, and the annual total vegetation NPP of the three was 1.54 PgC, 0.65 PgC and 0.007 PgC, respectively. Benefiting from the effective implementation of climate warming and humidification and ecological engineering, the agro-pastoral zone, the Loess Plateau, the eastern Sichuan Basin and the Greater Khingan Range had the most significant increases in the past 20 years. (4) The annual total vegetation NPP of China’s forest, grassland and desert ecosystem restoration potential was approximately 0.52 ± 0.28 PgC, which accounted for approximately 19.05% of the annual total NPP of the climax background vegetation. The annual total vegetation NPP of forest, grassland and desert ecosystems restoration status was 0.36 ± 0.2 PgC, 0.16 ± 0.07 PgC and 0.002 ± 0.0005 PgC, respectively; the restoration potential accounted for 18.80%, 9.67% and 23.95% of the climax background vegetation NPP, respectively. The deployment of ecological projects should fully consider the restrictive climate conditions for decision makers and ecological scholars, and the benefits and costs of the projects should be considered comprehensively.

Assessing the impact of land surface temperature on urban net primary productivity increment based on geographically weighted regression model

Urbanization had a huge impact on the regional ecosystem net primary productivity (NPP). Although the urban heat island (UHI) caused by urbanization has been found to have a certain promoting effect on urban vegetation NPP, the factors on the impact still are not identified. In this study, the impact of urbanization on NPP was divided into direct impact (NPPdir) and indirect impact (NPPind), taking Kunming city as a case study area. Then, the spatial heterogeneity impact of land surface temperature (LST) on NPPind was analyzed based on the geographically weighted regression (GWR) model. The results indicated that NPP, LST, NPPdir and NPPind in 2001, 2009 and 2018 had significant spatial autocorrelation in Kunming based on spatial analytical model. LST had a positive impact on NPPind in the central area of Kunming. The positively correlation areas of LST on NPPind increased by 4.56%, and the NPPind caused by the UHI effect increased by an average of 4.423 gC m−2 from 2009 to 2018. GWR model can reveal significant spatial heterogeneity in the impacts of LST on NPPind. Overall, our findings indicated that LST has a certain role in promoting urban NPP.

Towards a Single Integrative Metric on the Dynamics of Social-Environmental Systems

Integrating the dynamics and interconnections of natural and human system properties into a single measure would make it simpler to reliably and repeatedly assess and compare different social-environmental systems (SES). We propose a novel metric to assess the magnitudes and variations in SES dynamics by integrating longitudinal gross domestic product, population, and ecosystem net primary production. We use annual public data across the Asian Drylands Belt (ADB) from 1992 through 2016 for 18 political entities as our testbed for assessing the efficacy of the metric. We perform cross-comparisons with existing natural and social science metrics to demonstrate the validity of the proposed metric, including the Human Development Index and the Palmer Drought Severity Index. The new metric demonstrates notable and meaningful differences in trends among the political entities that reflect major social, economic and environmental events over the 25-year period. It provides unique perspectives about the three pillar components (social, economic and environmental systems) in each of the 18 political entities (PE) of the ADB. The metric also shows meaningful associations with key economic and environmental indicators and great potential for broader application and evaluation, given additional testing in other countries, regions, and biomes.