Total Net Primary Production Research Articles

Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models

Earth system models (ESMs) have been widely used to simulate global terrestrial carbon fluxes, including gross primary production (GPP) and net primary production (NPP). Assessment of such GPP and NPP products can be valuable for understanding the efficacy of certain ESMs in simulating the global carbon cycle and future climate impacts. In this work, we studied the model performance of 22 ESMs participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) by comparing historical GPP and NPP simulations with satellite data from MODIS and further evaluating potential model improvement from CMIP5 to CMIP6. In CMIP6, the average global total GPP and NPP estimated by the 22 ESMs are 16% and 13% higher than MODIS data, respectively. The multi-model ensembles (MME) of the 22 ESMs can fairly reproduce the spatial distribution, zonal distribution and seasonal variations of both GPP and NPP from MODIS. They perform much better in simulating GPP and NPP for grasslands, wetlands, croplands and other biomes than forests. However, there are noticeable differences among individual ESM simulations in terms of overall fluxes, temporal and spatial flux distributions, and fluxes by biome and region. The MME consistently outperforms all individual models in nearly every respect. Even though several ESMs have been improved in CMIP6 relative to CMIP5, there is still much work to be done to improve individual ESM and overall CMIP performance. Future work needs to focus on more comprehensive model mechanisms and parametrizations, higher resolution and more reasonable coupling of land surface schemes and atmospheric/oceanic schemes.

The response of coarse root biomass to long-term CO2 enrichment and nitrogen application in a maturing Pinus taeda stand with a large broadleaved component.

Elevated atmospheric CO2 (eCO2 ) typically increases aboveground growth in both growth chamber and free-air carbon enrichment (FACE) studies. Here we report on the impacts of eCO2 and nitrogen amendment on coarse root biomass and net primary productivity (NPP) at the Duke FACE study, where half of the eight plots in a 30-year-old loblolly pine (Pinus taeda, L.) plantation, including competing naturally regenerated broadleaved species, were subjected to eCO2 (ambient, aCO2 plus 200ppm) for 15-17years, combined with annual nitrogen amendments (11.2gNm-2 ) for 6years. Allometric equations were developed following harvest to estimate coarse root (>2mm diameter) biomass. Pine root biomass under eCO2 increased 32%, 1.80kgm-2 above the 5.66kgm-2 observed in aCO2 , largely accumulating in the top 30cm of soil. In contrast, eCO2 increased broadleaved root biomass more than twofold (aCO2 : 0.81, eCO2 : 2.07kgm-2 ), primarily accumulating in the 30-60cm soil depth. Combined, pine and broadleaved root biomass increased 3.08kgm-2 over aCO2 of 6.46kgm-2 , a 48% increase. Elevated CO2 did not increase pine root:shoot ratio (average 0.24) but increased the ratio from 0.57 to 1.12 in broadleaved species. Averaged over the study (1997-2010), eCO2 increased pine, broadleaved and total coarse root NPP by 49%, 373% and 86% respectively. Nitrogen amendment had smaller effects on any component, singly or interacting with eCO2 . A sustained increase in root NPP under eCO2 over the study period indicates that soil nutrients were sufficient to maintain root growth response to eCO2 . These responses must be considered in computing coarse root carbon sequestration of the extensive southern pine and similar forests, and in modelling the responses of coarse root biomass of pine-broadleaved forests to CO2 concentration over a range of soil N availability.

LPDynR: A new tool to calculate the land productivity dynamics indicator

The United Nations Sustainable Development Goal 15 (Life on Land), adopted the indicator 15.3.1 to measure the Land Degradation Neutrality. This indicator is based on three sub-indicators: (1) Trends in Land Cover, (2) Land Productivity and (3) Carbon Stocks. The Land Productivity sub-indicator refers to the total above-ground Net Primary Production and reflects changes in health and productive capacity of the land. It can be calculated using the Land Productivity Dynamics approach, which performs a combined assessment of the long term tendency of change of land productivity and its current level relative to homogeneous land areas.Here, we present the R-based tool LPDynR, which implements the Land Productivity Dynamics approach for the calculation of the Land Productivity sub-indicator. LPDynR ingests vegetation-related indices derived from time series of remote sensed imagery. The final indicator is a 5-class map, ranging from declining to increasing land productivity. As an example of LPDynR functionalities and applicability, we present a case study for Europe. First, we show the general way to calculate the indicator for the entire time series (2000–2019), explained in a step by step process. Secondly, we show how to alternatively calculate the indicator based only on the long term tendency of change, but we evidence the added value of including the current level of productivity to refine the final indicator. Finally, we present some code for the calculation of “partial indicators” in terms of time scale along the observation period, which may help the user to understand the land productivity dynamics within the time series, as well as to assess the stability of the final product. While the indicator shows a general positive dynamics across Europe during the period 2000–2019, some of the partial maps show more negative trends, demonstrating the highly fluctuating character of vegetation.

Field‐Based Estimation of Net Primary Productivity and Its Above‐ and Belowground Partitioning in Global Grasslands

AbstractNet primary productivity (NPP) in global grasslands is a critical component of terrestrial carbon cycling and the primary source of food for herbivores. However, the size and spatial distribution of NPP across global grasslands remain unclear, especially for belowground NPP (BNPP), which limits our understanding of above‐ and belowground carbon cycling and the assessments of herbivore food security. Here, we compiled a comprehensive grassland NPP database with 1,467 field measurements to estimate the spatial distributions of aboveground NPP (ANPP), BNPP, total NPP (TNPP), and the fraction of BNPP (fBNPP) using the random forest (RF) model. The global mean grassland ANPP, BNPP, TNPP, and fBNPP were 433 ± 31 g m−2 yr−1, 593 ± 47 g m−2 yr−1, 979 ± 78 g m−2 yr−1, and 0.54 ± 0.02, respectively. The total ANPP, BNPP, and TNPP over global grasslands were 6.84 ± 0.49, 9.36 ± 0.74, and 15.46 ± 1.23 Pg C yr−1, respectively. ANPP, BNPP, and TNPP exhibited decreasing trends from low latitudes toward the poles. The spatial pattern of fBNPP was almost opposite to that of ANPP. Climate was a major determinant in shaping the spatial distribution of ANPP and TNPP, while soil and vegetation had significant impacts on that of BNPP and fBNPP. Our findings suggest that field data‐driven estimation of NPP using the RF model could be a useful approach for obtaining spatially explicit NPP of grasslands, particularly BNPP products, and that more attention should be paid on belowground and non‐climatic factors to better assess the carbon cycle in global grasslands.

Impacts of landscape change on net primary productivity by integrating remote sensing data and ecosystem model in a rapidly urbanizing region in China

Land use and land cover patterns have been substantially changed under the intense global urbanization process. With the growing concern on the effects of land use/land cover change (LUCC) on the terrestrial carbon cycle, particularly net primary productivity (NPP), understanding the changing trends of LUCC and NPP and their correlation is essential for sustainable landscape planning. Using the integrated geographic information system, remote sensing, Carnegie–Ames–Stanford approach model, and statistical methods, the spatial-temporal variations in landscape and NPP in the Beijing–Tianjin–Hebei (BTH) region from 2005 to 2015 were analyzed. The results showed that the built-up areas increased by 36.7% from 2005 to 2015, 80.5% of which were in lowland areas and 19.5% in relatively high elevation areas. The urban area all showed a tendency to be aggregated and complete in the entire region. The NPP of urban area was the lowest and was 31% lower than the highest value of forest land. Instead of the most urbanized city center, the peripheral area outside the metropolitan city center was facing the most intense NPP loss of 17.13 g C∙m−2∙year−1 in average. In addition to the climatic factors, the LUCC accounted for 70% of the total NPP change in urban area, while approximately 20% in forest and grassland, showing that human constructions undoubtedly affect the land productivity. Through a spatial clustering analysis, the 206 counties in the BTH region could be classified into five categories based on their temporal urbanization degree and their ecological functions. Planning specific development models for different types of areas can give consideration to both urban development and ecological protection in the urbanization process. Our study highlights the necessity of evaluating various impacts of LUCC on NPP in an integrated region of complex ecosystem composition, which contributes to the future localized landscape planning in the global urbanization process.

Spatio-temporal variation of net primary productivity in a rapidly expanding artificial woodland area based on remote-sensing data

The expansion of artificial woodlands in China has contributed significantly to regional land-cover changes and changes in the regional net primary productivity (NPP). This study used Ximeng County in the Yunnan Province as a case study to investigate the overall changes, associated amplitude, and spatio-temporal distribution of NPP from 2000–2015.The Carnegie-Ames-Stanford approach was used in the rapidly expanding artificial woodland area based on MODIS-NDVI data, meteorological data, and Landsat 5 TM data to calculate the NPP. The results show that (1) artificial woodlands experience a 10fold increase and account for 93 % of the land cover transfer, which was mainly from woodland areas. (2) The NPP was 906.2×109 gC·yr-1 in 2000 and 972.0×109 gC·yr-1 in 2015, presenting a total increase of 65.8×109 gC·yr-1 and a mean increase of 52.4 gC·m-2·yr-1 in Ximeng County. (3) The most notable NPP changes take place in the central and the western border regions, with the increasing NPP of artificial woodlands and arable land offsetting the negative effects of the decrease in woodland NPP. (4) The total NPP in the study area kept increasing, primarily due to the growing area of artificial woodlands as well as the stand age of the woods, whereas the mean value change of the NPP is mostly related to the increasing stand age. (5) The artificial woodlands increase the NPP value more than natural woodlands. While protecting and promoting ecologically valuable natural forests at the same time, it seems quite advantageous to establish regional plantations and coordinate their development on a scientific basis with a view to increasing NPP, economic development, but also the ecological stability of this mountain region. Our study reveals the changes in NPP and its distribution in a rapidly expanding area of artificial woodland in southwest China based on remote-sensing data and the CASA model, providing a decision-making basis for rational land-use management, the optimal utilization of land resources, and a county-scale assessment approach.

Untangling the effects of management measures, climate and land use cover change on grassland dynamics in the Qinghai–Tibet Plateau, China

AbstractMultiple large‐scale ecological conservation programmes have been launched by the Chinese Government to combat grassland degradation across the Qinghai–Tibet Plateau (QTP) since 1999. However, the mechanisms by which these ecological initiatives might affect grassland changes remains unknown. Here, we examined the land use cover change (LUCC) and net primary productivity (NPP) of the grassland in the QTP in 2001–2016. The effects of LUCC, management measures, and climate variation on grassland NPP were quantitatively evaluated. The grassland area had a net increase of 96,397 km2, which could be attributed to the conversion of desert and forests. The total NPP of grassland increased by 154,073.7 GgC·yr−1 in 2001–2016, of which the contribution of climate and human factors occupied 35.58% and 64.42%, respectively. Specifically, climate variation caused an overall increase of 54,823.24 GgC·yr−1in grassland total NPP. The climate‐dominated NPP decrease was widely found in the eastern and southern QTP. The contribution of LUCC and management measures to the increase of grassland NPP was 62.78% and 37.22%, respectively. This increase was mostly found in the mid‐east region of the QTP, which could sufficiently counteract the negative effect of climate variation on NPP. Overall, these findings seem to demonstrate that these ecological restoration programs have substantially promote the increase of grassland NPP and contribute to understanding the degree to which the grassland ecosystem may respond to the LUCC and management measures under the ecological restoration projects, which are essential to developing more effective ecological measures for future grassland restoration in the QTP.

Habitat Adaptation Mediates the Influence of Leaf Traits on Canopy Productivity: Evidence from a Tropical Freshwater Swamp Forest

Functional traits offer generalizability to the prediction of ecosystem processes such as production, and community-weighted mean trait values are increasingly used for such predictions. However, the underlying causal direction between traits and ecosystem processes are often indirect and sometimes even tenuous. In this study, we aimed to uncover underlying causal mechanisms between traits, habitat adaptation and canopy productivity. We used canopy production data estimated from leaf litter traps, and trait and habitat association data obtained from 40 permanent vegetation plots in the Nee Soon catchment in Singapore, which contains a heterogeneous mix of freshwater swamp and dry-land tropical forests. Mean canopy production across the catchment was estimated to be 768 g m−2 year−1, which is similar to other tropical dry-land forests in the region. Biweekly per-basal-area canopy production was found to be consistently lower in swamp than non-swamp plots, and positively correlated with monthly mean temperature. Structural equation models fitted to data of canopy production, leaf traits, plot type (swamp versus non-swamp), basal areas and habitat adaptations of 69 tree species–plot combinations suggested that tree species possessing leaf traits associated with more conservative resource acquisition strategies, viz. low specific leaf area, high leaf C:N ratio, and thicker leaves, are better adapted to stressful, waterlogged swamp conditions, but that this adaptation also reduces canopy—and likely total—net primary productivity. These observations suggest that the stressful conditions of waterlogged, anoxic swamp habitats significantly reduce the rate at which nutrients are cycled by communities found in such environments.

Changes in Under‐Ice Primary Production in the Chukchi Sea From 1988 to 2018

AbstractChanges in sea ice thickness and extent have corresponded with substantial changes in net primary production (NPP) in the Arctic Ocean. In recent years, observations of massive phytoplankton blooms under sea ice have upended the previous paradigm that Arctic NPP was driven largely by growth in the marginal ice zone and open water periods. Here, a new 1‐D biogeochemical model capable of simulating ice algal and phytoplankton dynamics both under the ice and in open waters is applied in the northern Chukchi Sea for the years 1988–2018. Over this period, substantial under‐ice (UI) blooms were produced in all but four years and were the primary drivers of interannual variation in total NPP. While NPP in the UI period was highly variable interannually due to fluctuations in ice thickness and the length of the UI period, UI NPP accounted for nearly half of total NPP between 1988 and 2018. Further, years with high UI NPP had reduced annual zooplankton grazing, indicating an intensification in the mismatch between phytoplankton and zooplankton populations and possibly altering the partitioning of food between benthic and pelagic ecosystems. These results demonstrate that the often‐overlooked ice covered period can be highly productive in the Arctic Ocean, and that the northern Chukchi Sea has been amenable to UIB formation since at least 1988.

Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China

AbstractUnderstanding the spatiotemporal characteristics of drought events and their impacts on terrestrial net primary productivity (NPP) is crucial for drought mitigation and environmental protection. This study, by taking the Pearl River basin as the case region, investigated drought duration, severity, intensity, affected area, and centroids during 1960–2015 based on the Standardized Evapotranspiration Deficit Index and three-dimensional clustering algorithm and then revealed how these drought characteristics have affected NPP. Results showed that there were altogether 32 severe drought events lasting at least 3 months in the basin, with half lasting longer than 6 months. The total NPP loss significantly correlated with drought severity and intensity. Most drought events caused a reduction in NPP across more than half of the drought-affected area; specifically, the February–December drought in 2011 has cut NPP by 31.85 Tg C, accounting for 11.7% of the regional annual mean NPP, while the September 2009–September 2010 drought caused a decrease of 20.26 Tg C in NPP. Our research improves the insight into the relationship between NPP and drought, which helps decision-makers manage droughts and provides guidance for drought-related studies across other regions.

Assessment of the impact of LUCC on NPP and its influencing factors in the Yangtze River basin, China

Land use and land cover change (LUCC) will directly affect the types, structures and functions of ecosystems, and then have important impact on vegetation net primary productivity (NPP). The evaluation of the spatio-temporal dynamic changes of LUCC and regional NPP, and assessing the impact of LUCC on NPP can facilitate the corresponding management of different environmental types of regions and provide scientific guidance for scientific development of ecological resources, and better restore and control the ecological environment, thus promoting rapid economic development. Using MODIS normalized difference vegetation index (NDVI) data and meteorological data from 2001 to 2018, combined with Carnegie-Ames-Stanford model (CASA), the regional NPP was estimated, and the influence of LUCC on NPP in Yangtze River basin from 2001 to 2018 and its causes were analyzed. The results showed that the area with a land cover and land use change account for 10.98% of the total study area in the past 18 years, and the most important change is the transformation from grassland to forest and grassland to crop/natural vegetation mosaic (NVM). Forests, wetlands, crop/natural vegetation mosaic, low vegetation cover (LVC) land, urban, and water bodies are growing. The forest land area increased the most and that of grassland decreased the most. Total NPP has increased by 53887.51GgC in the past 18 years, which was due to the combination effect of LUCC change and climate change.

Fine root dynamics across pantropical rainforest ecosystems.

Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than aboveground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old-growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi-deciduous, and deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n=47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water-stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting that root productivity is a particularly strong driver of NPP allocation in tropical mountain regions.

Analyzing NPP Response of Different Rangeland Types to Climatic Parameters over Mongolia

Global warming threatens ecosystem functions, biodiversity, and rangeland productivity in Mongolia. The study analyzes the spatial and temporal distributions of the Net Primary Production (NPP) and its response to climatic parameters. The study also highlights how various land cover types respond to climatic fluctuations from 2003 to 2018. The Boreal Ecosystem Productivity Simulator (BEPS) model was used to simulate the rangeland NPP of the last 16 years. Satellite remote sensing data products were mainly used as input for the model, where ground-based and MODIS NPP were used to validate the model result. The results indicated that the BEPS model was moderately effective (R2 = 0.59, the Root Mean Square Error (RMSE) = 13.22 g C m−2) to estimate NPP for Mongolian rangelands (e.g., grassland and sparse vegetation). The validation results also showed good agreement between the BEPS and MODIS estimates for all vegetation types, including forest, shrubland, and wetland (R2 = 0.65). The annual total NPP of Mongolia showed a slight increment with an annual increase of 0.0007 Pg (0.68 g C per meter square) from 2003 to 2018 (p = 0.82) due to the changes in climatic parameters and land cover change. Likewise, high increments per unit area found in forest NPP, while decreased NPP trend was observed in the shrubland. In conclusion, among the three climatic parameters, temperature was the factor with the largest influence on NPP variations (r = 0.917) followed precipitation (r = 0.825), and net radiation (r = 0.787). Forest and wetland NPP had a low response to precipitation, while inter-annual NPP variation shows grassland, shrubland, and sparse vegetation were highly sensitive rangeland types to climate fluctuations.

Technical Note: A Rapid Method to Estimate Root Production in Grasslands, Shrublands, and Forests

Net primary production (NPP) is a critical ecosystem property that researchers and land managers attempt to quantify across ecosystems globally. Although the belowground NPP (BNPP) component of total NPP often contributes 50% or more to total NPP, it is infrequently measured due to the amount of labor involved. Here, I present a rapid method to estimate BNPP or fine root production in rangelands using sequential or root ingrowth cores and modifications to existing root-washing methods. The details provided should allow anyone to estimate BNPP in rangelands worldwide with minimal investment in materials and labor.

Forest Thinning in Ponderosa Pines Increases Carbon Use Efficiency and Energy Flow From Primary Producers to Primary Consumers

AbstractA better understanding of carbon use efficiency (CUE) and carbon allocation during disturbance is critical to improve simulations of the global carbon cycle and understanding future climate impacts. Forest thinning of high‐stem density, high elevation dry western US forests is becoming more common to reduce severe fire danger but there are uncertainties about how forest thinning may impact forest CUE, carbon allocation and energy flow through the food chain. In three, quarter ha stands with similar soils, elevation and climate along a forest thinning gradient near Flagstaff (AZ), we measured total net primary production (NPP of wood, fine root, and leaves), total autotrophic respiration (Ra of wood, rhizosphere, and canopy respiration), gross primary production (GPP = NPP + Ra) and large mammal herbivory (with camera traps and dung counts) over a ∼2‐year period. We found strong seasonality in all carbon cycling variables and herbivory, peaking during the warm, wet monsoon period. Carbon was produced more efficiently in the thinned stands, with carbon use efficiency (CUE = NPP/GPP) of ∼0.63, versus the unthinned stand with CUE of 0.38. GPP was similar in the two thinned stands (∼3.5 Mg C ha−1 yr−1) but was about 30% greater in the unthinned stands (5.0 Mg C ha−1 yr−1). Finally, the thinned stand had higher understory NPP, large herbivore consumption and had triple the total energy going into primary consumers. Overall, the thinning, and the return to a more natural prefire suppression forest structure, increased the CUE and energy flow from primary producers to primary consumers.

Climate implications on forest above- and belowground carbon allocation patterns along a tropical elevation gradient on Mt. Kilimanjaro (Tanzania)

Tropical forests represent the largest store of terrestrial biomass carbon (C) on earth and contribute over-proportionally to global terrestrial net primary productivity (NPP). How climate change is affecting NPP and C allocation to tree components in forests is not well understood. This is true for tropical forests, but particularly for African tropical forests. Studying forest ecosystems along elevation and related temperature and moisture gradients is one possible approach to address this question. However, the inclusion of belowground productivity data in such studies is scarce. On Mt. Kilimanjaro (Tanzania), we studied aboveground (wood increment, litter fall) and belowground (fine and coarse root) NPP along three elevation transects (c. 1800–3900 m a.s.l.) across four tropical montane forest types to derive C allocation to the major tree components. Total NPP declined continuously with elevation from 8.5 to 2.8 Mg C ha−1 year−1 due to significant decline in aboveground NPP, while fine root productivity (sequential coring approach) remained unvaried with around 2 Mg C ha−1 year−1, indicating a marked shift in C allocation to belowground components with elevation. The C and N fluxes to the soil via root litter were far more important than leaf litter inputs in the subalpine Erica forest. Thus, the shift of C allocation to belowground organs with elevation at Mt. Kilimanjaro and other tropical forests suggests increasing nitrogen limitation of aboveground tree growth at higher elevations. Our results show that studying fine root productivity is crucial to understand climate effects on the carbon cycle in tropical forests.

Modeling net primary productivity of wetland with a satellite-based light use efficiency model

Wetland, an important carbon pool on the earth, is of great significance for human beings and the environment. In this study, we modeled the wetland NPP using Carnegie–Ames–Stanford Approach (CASA) and time series with high spatial and temporal resolution generated by Landsat 8 and Sentinel-2 images. Firstly, the downscaled Landsat 8 data (10 m) combined with Sentinel-2 data were utilized to produce time series with high spatial and temporal resolution. Subsequently, all Sentinel-1 and Sentinel-2 data within each stage (five stages.) are employed to obtain monthly wetland maps in these stages by an adaptive Stacking algorithm, respectively. Then, monthly fraction cover maps (mainly sedge, reed and poplar) were derived from the time series reflectance product using fully constrained least squares (FCLS) in these five Stages, respectively. Finally, monthly normalized difference vegetation index (NDVI), land surface water index (LSWI), temperature, solar radiation, as well as wetland maps and were combined to estimate monthly and total NPP in the Dongting Lake wetland by CASA model. The high correlation (R2 = 0.8445) and low (RMSE = 20.30 g C/m2) between the estimated NPP using proposed method and measured NPP demonstrated a significant linear relationship and the estimated NPP based on Sentinel-2 data using the CASA model with the above-described input parameters is creditable. The NPP estimation method in this paper is expected to provide scientific data support for quantitative research of regional wetland carbon reserves and sustainable development.

Spatiotemporal Characteristics of Vegetation Net Primary Productivity on an Intensively-Used Estuarine Alluvial Island

Net Primary Productivity (NPP) can effectively reflect the characteristics and strength of the response to external disturbances on estuarine alluvial island ecosystems, which can provide evidence for regulating human development and utilization activities and improving blue carbon capacity. However, there are a few studies on NPP of estuarine alluvial islands. We established a model based on a Carnegie–Ames–Stanford Approach (CASA) to estimate NPP on Chongming Island, a typical estuarine alluvial island, by considering the actual ecological characteristics of the island. The NPP of different land-cover types and protected areas in different years and seasons were estimated using Remote Sensing and Geographic Information System as the main tools. Correlations between NPP and Remote Sensing-based spatially heterogeneous factors were then conducted. In the last 30 years, the mean NPP of Chongming Island initially increased and then slowly decreased, while total NPP gradually increased. In 2016–2017, Chongming Island total NPP was 422.32 Gg C·a−1, and mean NPP was 287.84 g C·m−2·a−1, showing significant seasonal differences. NPP showed obvious spatial differentiation in both land-cover and protected area types, resulting from joint influences of natural and human activities. Chongming Island vegetation growth status and cover were the main factors that positively affected NPP. Soil surface humidity increased NPP, while soil salinity, surface temperature, and surface aridity were important NPP limiting factors.

Effects of the Application of Biochar to Plant Growth and Net Primary Production in an Oak Forest

Few studies have evaluated the application of biochar to forest ecosystems and their responses under field conditions. We manually spread grounded biochar on the forest floor, at rates of 0 (control), 5, and 10 Mg ha−1 (C0, C5 and C10, respectively), of an oak forest in central Japan to test the effects of biochar on tree growth and productivity. The relative growth rate of the diameter at breast height (dbh) of canopy oak trees (dbh > 20 cm) significantly increased in C10 compared with that of the control (C0), but not in C5, in the second to third years after application. Despite the increasing growth rate of canopy trees, foliage production (NPPF) and woody production (NPPW) did not respond to biochar application. Conversely, the production of reproductive organs (NPPR, mainly oak acorns) increased in line with the biochar application rate gradients (1.04 ± 0.09 Mg ha−1 yr−1 in C0, 1.30 ± 0.08 Mg ha−1 yr−1 in C5, and 1.47 ± 0.13 Mg ha−1 yr−1 in C10). Since the contribution of NPPR to total NPP was fairly small, there were no significant differences in total NPP (=NPPW + NPPF + NPPR) for C5 (14.57 ± 0.20 Mg ha−1 yr−1) or C10 (16.11 ± 0.73 Mg ha−1 yr−1) compared with the control (15.07 ± 0.48 Mg ha−1 yr−1).

Effects of climatic factors on the net primary productivity in the source region of Yangtze River, China

The ecosystem of the Source Region of Yangtze River (SRYR) is highly susceptible to climate change. In this study, the spatial–temporal variation of NPP from 2000 to 2014 was analyzed, using outputs of Carnegie–Ames–Stanford Approach model. Then the correlation characteristics of NPP and climatic factors were evaluated. The results indicate that: (1) The average NPP in the SRYR is 100.0 gC/m2 from 2000 to 2014, and it shows an increasing trend from northwest to southeast. The responses of NPP to altitude varied among the regions with the altitude below 3500 m, between 3500 to 4500 m and above 4500 m, which could be attributed to the altitude associated variations of climatic factors and vegetation types; (2) The total NPP of SRYR increased by 0.18 TgC per year in the context of the warmer and wetter climate during 2000–2014. The NPP was significantly and positively correlated with annual temperature and precipitation at interannual time scales. Temperature in February, March, May and September make greater contribution to NPP than that in other months. And precipitation in July played a more crucial role in influencing NPP than that in other months; (3) Climatic factors caused the NPP to increase in most of the SRYR. Impacts of human activities were concentrated mainly in downstream region and is the primary reason for declines in NPP.