High Net Primary Productivity Research Articles

Atmospheric temperature and humidity demonstrated strong correlation with productivity in tropical moist deciduous forests.

Tropical forests sequester six times higher carbon than that released by humans annually into the atmosphere. These biodiversity-rich tropical forests have high net primary productivity (NPP), which differs among constituent plant communities. Tropical moist deciduous forests occupy 179,335 km2 of India's geographical area and constitute 44% of the country's total protected area (PA) forests. The productivity of these forests has neither been estimated specifically nor precisely. We measured the annual NPP of three predominant distinct community types, viz., mixed (DM), sal (SM), and teak (TP), in a tropical moist deciduous forestin northern India. The NPP was estimated from tree biomass data collected from nine long-term ecological research (LTER) plots of 1ha each representing the above three community types. The estimatedannual NPP were 10.28, 6.25, and 9.79Mgha-1year-1 in DM; 8.93, 7.09, and 10.59Mgha-1year-1 in SM; and 14.57, 7.14, and 13.56Mgha-1year-1 in TP for the years 2010, 2011, and 2012, respectively. The NPP was correlated with tree density, height and DBH, species richness, diversity, microclimatic and edaphic variables, and leaf area index (LAI) using principal component analysis (PCA) and generalized linear modeling (GLM). Air temperature and humidity were strongly related to NPP in all the community types, while "complementarity" and "selection effects" contributed to the NPP in both the sal and mixed forestcommunities with equal importance, and the NPP in teak plantation ould point to "dominance effect."

Spatiotemporal variation of net primary productivity influenced by climatic variables in the karst area of China

Karst area is one of the most ecologically fragile regions. Monitoring spatial and temporal variation of net primary productivity (NPP) and its response to climate change are essential for vegetation restoration. In this study, we used the CASA (Carnegie-Ames-Stanford Approach) model to estimate the NPP in the karst area of China during 2001 to 2019. We analyzed the response of multi-scale NPP to climate change by using Theil-Sen Median trend analysis, partial correlation analysis and time lag analysis. The results showed that the average NPP was 399.06 gC·m−2, decreased from southeast to northwest, with an annual increase rate of 1.93 gC·m−2 from 2001 to 2019. The broadleaf forest had the highest NPP, and summer NPP accounted for 48.12% of annual NPP. Climate change significantly promotes the increase of NPP in the early growing season. Partial correlation analysis showed that NPP was more correlated with precipitation (0.20) than temperature (0.12), this phenomenon was more pronounced in some arid regions. Where precipitation caused a decrease in NPP during the rainy season. The time lag of NPP to temperature change was shorter than precipitation, indicating that NPP is more sensitive to temperature in most regions. The time lag of NPP to temperature changes was very short in spring. Whereas, even in arid regions, the time lag of NPP to precipitation changes was still long. This study can deepen our knowledge of the response of NPP to climate change in karst ecosystems.

The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil

Abstract. Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By controlling the turnover of soil organic matter, these organisms directly regulate the cycling of carbon between the soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi suggest that variation in the biogeography of relative abundance of soil fungi versus bacteria could drive striking differences in carbon decomposition and soil organic matter formation between different biomes. However, a lack of global and predictive information on the distribution of these organisms in terrestrial ecosystems has prevented the inclusion of relative abundance of soil fungi versus bacteria and the associated processes in global biogeochemical models. Here, we used a global-scale dataset of >3000 distinct observations of abundance of soil fungi versus bacteria in the surface topsoil (up to 15 cm) to generate the first quantitative and high-spatial-resolution (1 km2) explicit map of soil fungal proportion, defined as fungi/fungi + bacteria, across terrestrial ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high-latitude environments with large soil carbon stocks. There was a strong nonlinear response of fungal dominance to the environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi dominated in regions with low MAT and NPP and bacteria dominated in regions with high MAT and NPP, thus representing slow vs. fast soil energy channels, respectively, a concept with a long history in soil ecology. These high-resolution models provide the first steps towards representing the major soil microbial groups and their functional differences in global biogeochemical models to improve predictions of soil organic matter turnover under current and future climate scenarios. Raw datasets and global maps generated in this study are available at https://doi.org/10.6084/m9.figshare.19556419 (Yu, 2022).

Near‐Surface Hydrology and Soil Properties Drive Heterogeneity in Permafrost Distribution, Vegetation Dynamics, and Carbon Cycling in a Sub‐Arctic Watershed

AbstractDiscontinuous permafrost environments exhibit strong spatial heterogeneity at scales too small to be driven by weather forcing or captured by Earth System Models. Here we analyze effects of observed spatial heterogeneity in soil and vegetation properties, hydrology, and thermal dynamics on ecosystem carbon dynamics in a watershed on the Seward Peninsula in Alaska. We apply a Morris global sensitivity analysis to a process‐rich, successfully tested terrestrial ecosystem model (TEM), ecosys, varying soil properties, boundary conditions, and weather forcing. We show that landscape heterogeneity strongly impacts soil temperatures and vegetation composition. Snow depth, O‐horizon thickness, and near‐surface water content, which vary at scales of O(m), control the soil thermal regime more than an air temperature gradient corresponding to a 140 km north–south distance. High shrub productivity is simulated only in talik (perennially unfrozen) soils with high nitrogen availability. Through these effects on plant and permafrost dynamics, landscape heterogeneity impacts ecosystem productivity. Simulations with near‐surface taliks have higher microbial respiration (by 78.0 gC m−2 yr−1) and higher net primary productivity (by 104.9 gC m−2 yr−1) compared to runs with near‐surface permafrost, and simulations with high shrub productivity have outlying values of net carbon uptake. We explored the prediction uncertainty associated with ignoring observed landscape heterogeneity, and found that watershed net carbon uptake is 60% larger when heterogeneity is accounted for. Our results highlight the complexity inherent in discontinuous permafrost environments and demonstrate that missing representation of subgrid heterogeneity in TEMs could bias predictions of high‐latitude carbon budget.

Effects of built area on net primary productivity provided by different landscape characters and the promotion potential

Changes in natural and artificial landscapes due to rapid urbanization in recent decades have greatly altered the supply of net primary productivity (NPP) and its regulation mechanisms at the landscape scale, thus affecting the health of the whole ecosystem. Understanding the capacity and potential of NPP supply at the landscape scale based on landscape integrity is critical for regional ecosystem health and management. In this study, the NPP supply capacity of different types of landscape and the influence of urban built-up areas on it were assessed in Hubei Province, China. The optimal capacity criteria of NPP supply in different types of landscape under current conditions were identified, and the promotion potential of NPP was evaluated based on these criteria. The results show that the landscape with natural elements as the main components has a high NPP supply capacity, but it has been greatly influenced by urban development, and the closer the distance to the city is, the lower the NPP supply capacity will be. The plain landscape with construction land and farmland as the main components has weak NPP supply capacity and low sensitivity to urban development. The trend inflection points for the influence of urban development on the NPP supply capacity of different types of landscape can provide more realistic quantitative targets and spatial distribution of NPP improvement potential for decision making. The findings may help in the management of ecosystem health at the landscape scale.

Vertically migrating phytoplankton fuel high oceanic primary production

Marine net primary production (NPP) is remarkably high given the typical vertical separation of 50–150 m between the depth zones of light and nutrient sufficiency, respectively. Here we present evidence that many autotrophs bridge this gap through downward and upward migration, thereby facilitating biological nutrient pumping and high rates of oceanic NPP. Our model suggests that phytoplankton vertical migration (PVM) fuels up to 40% (>28 tg yr−1 N) of new production and directly contributes 25% of total oceanic NPP (herein estimated at 56 PgC yr−1). Confidence in these estimates is supported by good reproduction of seasonal, vertical and geographic variations in NPP. In contrast to common predictions, a sensitivity study of the PVM model indicates higher NPP under global warming when enhanced stratification reduces physical nutrient transport into the surface ocean. Our findings suggest that PVM is a key mechanism driving marine biogeochemistry and therefore requires consideration in global carbon budgets.

Vertical Structure in Phytoplankton Growth and Productivity Inferred From Biogeochemical‐Argo Floats and the Carbon‐Based Productivity Model

AbstractEstimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite‐based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon‐based productivity model (CbPM) to compute vertically resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float‐based, depth‐resolved, bio‐optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float‐based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth‐resolved structure in NPP from bio‐optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth‐reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float‐informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C, division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically integrated NPP, where the mean bias between model implementations in depth‐integrated productivity south of 30°S is reduced by 62% when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.

Stochastic Modelling to Assess External Environmental Drivers of Atlantic Chub Mackerel Population Dynamics

The population dynamics of small and middle-sized pelagic fish are subject to considerable interannual and interdecadal fluctuations in response to fishing pressure and natural factors. However, the impact of environmental forcing on these stocks is not well documented. The Moroccan Atlantic coast is characterized by high environmental variability due to the upwelling phenomenon, resulting in a significant abundance and variation in the catches of small and middle-sized pelagic species. Therefore, understanding the evolution of stock abundance and its relationship with different oceanographic conditions is a key issue for fisheries management. However, because of the limited availability of independent-fishery data along the Moroccan Atlantic coast, there is a lack of knowledge about the population dynamics. The main objective of this study is to test the correlation between the environment conditions and the stock fluctuations trends estimated by a stock assessment model that does not need biological information on growth, reproduction, and length or age structure as input. To achieve this objective, the fishery dynamics are analyzed with a stochastic surplus production model able to assimilate data from surveys and landings for a biomass trend estimation. Then, in a second step, the model outputs are correlated with different environmental (physical and biogeochemical) variables in order to assess the influence of different environmental drivers on population dynamics. This two-step procedure is applied for chub mackerel along the Moroccan coast, where all these available datasets have not been used together before. The analysis performed showed that larger biomass estimates are linked with periods of lower salinity, higher chlorophyll, higher net primary production, higher nutrients, and lower subsurface oxygen, i.e., with an enhanced strength of the upwelling. In particular, acute anomalies of these environmental variables are observed in the southern part presumably corresponding to the wintering area of the species in the region. The results indicate that this is a powerful procedure, although with important limitations, to deepen our understanding of the spatiotemporal relationships between the population and the environment in this area. Moreover, once these relationships have been identified, they could be used to generate a mathematical relationship to simulate future population trends in diverse environmental scenarios.

Physiological trait networks enhance understanding of crop growth and water use in contrasting environments.

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits, soil characteristics and their interactions (i.e., networks), has potential to improve our understanding of crop performance in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.

Modeling the daytime net primary productivity of maize at the canopy scale based on UAV multispectral imagery and machine learning

Net primary productivity (NPP) is an important index to evaluate the carbon absorption capacity in agricultural ecosystems, timely and accurate spatial–temporal variations of NPP play a significant role in guiding agricultural production. Currently, NPP observation at the canopy scale is primarily based on the chamber method. However, upscaling the spatial–temporal estimates of NPP at the canopy scale is still challenging. In this study, maize daytime NPP was measured by the chamber, and multispectral images of maize canopies were obtained via unmanned aerial vehicle (UAV) multispectral system. We explored the potential of multispectral images for estimating maize daytime NPP at the canopy scale. Four machine learning algorithms were employed to estimate daytime NPP using ground factors and vegetation indices (VIs) × photosynthetic active radiation (PAR) independently. NPP estimation models based on the gradient boosting regression (GBR), random forest (RF), and support vector regression (SVR) using ground factors outperformed the VIs × PAR-based model, barring the ridge regression (RR) model. Among them, the GBR-based models performed better, and the model using ground factors (R2 = 0.958) outperformed those using VIs × PAR (R2 = 0.899). However, the ground factor-based GBR model was complex and contained more input parameters, thus making it highly-time and labor-intensive, and highly destructive. Moreover, ground factor-based GBR model could not reflect the variation of NPP in maize canopy. The VIs × PAR-based GBR model could explain 89.9% of the daytime NPP, and it only required two parameters, including VIs and PAR. Therefore, the VIs × PAR-based GBR model can easily obtain high resolution and long time-series NPP information nondestructively for a large range. This study reveals that maize daytime NPP can be estimated using high-resolution UAV multispectral images and provides a good reference for promoting the monitoring and upscaling of NPP observations.

Carbon removals from nature restoration are no substitute for steep emission reductions

Carbon removals from nature restoration are no substitute for steep emission reductions

Spatiotemporal changes in net primary productivity before and after the development of unused land in the hilly areas of Hebei, China.

Net primary productivity (NPP) plays an important role in the carbon cycle of an ecosystem. To explore the impact of unused land development on NPP, this study adopted an improved Carnegie Ames Stanford Approach (CASA) model to analyze the changes in NPP before and after the development of unused land in Tang County, Hebei Province, in 2000, 2007, and 2018. The results showed that, due to the changes in land use types from unused land, forestland, arable land with high NPP values to urban and rural residential land, traffic land with low NPP values, and the changes in precipitation and temperature, the NPP in the study area showed an overall trend of decreasing first and then rising from 2000 to 2018. Before the development of unused land in 2000, the total NPP was 38.45×1010 g C. After the development in 2007 and 2018, the total NPP was 36.44×1010 g C and 41.05×1010 g C, respectively. The NPP of each land type in 2018 was arable land (1046.18 g C m-2) > forestland (464.42 g C m-2) > unused land (356.34 g C m-2) > grassland (343.77 g C m-2) > waters (182.56 g C m-2) > urban and rural settlements (120.86 g C m-2) > traffic land (120.70 g C m-2). The distribution of NPP was generally high in the north and low in the south before and after development. NPP was mainly concentrated in the interval of 300 g C m-2 yr-1–400 g C m-2 yr-1, and the range of NPP change was mostly within 100 g C m-2. The influence of elevation, temperature and precipitation on the spatial distribution of NPP was significant. Elevation and precipitation were positively correlated with NPP, while temperature was negatively correlated with NPP. The increase in NPP mainly originated from the conversion of unused land to forestland and arable land. The loss of NPP was mainly due to the conversion from forestland with high vegetation productivity to a land use type with low vegetation productivity, such as the conversion from forestland to urban and rural residential land. The results can provide references for making reasonable land planning decisions and ecological environment construction.

Vulnerability of Primary Productivity and Its Carbon Use Efficiency to Unfavorable Climatic Conditions in Jambi Province, Indonesia

Climatic conditions and land cover play crucial roles in influencing the process of carbon uptake through vegetation. This study aimed to analyze the effect of climate variability on carbon uptake of four different land covers in Jambi Province, Indonesia. The four land cover types studied were: forest, shrub, grass, and irrigated soybean, based on Community Land Model version 5. Forest was found to have the highest net primary production (NPP) compared to the other land covers. Seasonal climate variability showed no major effect on NPP and gross primary production (GPP). However, GPP and NPP experienced significant declines during El Niño Southern Oscillation (ENSO), particularly in 2015. Carbon use efficiency (CUE = NPP/GPP) was also affected by ENSO, where CUE decreased during El Niño, particularly in October and November with an increased number of days without rainfall. In addition, the difference between latent (LE) and sensible heat (H) flux, denoted as (LE-H), decreased from August to November. This difference was highly correlated with NPP. This result indicates that when water supply is low, stomata will close, thereby reducing photosynthesis and transpiration, and allocating more of the available energy to sensible heat flux rather than latent heat flux.

Variations and Environmental Controls of Primary Productivity in the Amundsen Sea

The Amundsen Sea is one of the regions with the highest primary productivity in the Antarctic. To better understand the role of the Southern Ocean in the global carbon cycle and in climate regulation, a better understanding of the variations and environmental controls of primary productivity is needed. Using cluster analysis, the Amundsen Sea was divided into nine bioregions. The biophysical differences among bioregions enhanced confidence to identify priorities and regions to study the temporal and spatial variations in primary production. Four nearshore bioregions with high net primary productivity or rapidly increasing rates were selected to analyze temporal and spatial variations in primary productivity in the Amundsen Sea. Due to changes in net solar radiation and sea ice, primary production had significant seasonal variation in these four bioregions. The phenology had changed at two bioregions (6 and 7), which has the third and fourth highest primary production, due to changes in the dissolved iron. Annual primary production showed increasing trends in these four bioregions, and it was significant at three bioregions. The variation in primary production in the bioregion (9), which has the highest primary production, was mainly affected by variations in sea surface temperatures. In the bioregion (8), which has the second-highest primary production, the primary production was significantly positively correlated with sea surface temperature and significantly negatively correlated with sea ice thickness. The long-term changes of primary productivity in bioregions 6 and 7 were thought to be related to changes in the dissolved iron, and dissolved iron was the limiting factor in these two bioregions. Bioregionalization not only disentangles multiple factors that control the spatial differences, but also disentangles limiting factors that affect the phenology, decadal and long-term changes in primary productivity.

Human activities have markedly altered the pattern and trend of net primary production in the Ili River basin of Northwest China under current climate change

AbstractThe Ili River basin (IRB), as an epitome of border areas of Northwest China and has experienced intensive human disturbance since the implementation of the Western Development Strategy in 2000. However, the ways that human activity affected the pattern and trend of net primary production (NPP) in this region under current rapid climate change remain unclear. Here, potential NPP time‐series data, driven solely by climate change, were simulated using a process‐based model IBISi, with an initial status that was already disturbed by human activity prior to the measurement period. The human impacts on NPP during the measurement period were derived by comparing potential NPP with remotely‐sensed ones. The results indicate that human activities have markedly altered the pattern and trend of NPP in the IRB during 2000–2017. Cropland reclamation and cultivation, and afforestation, have resulted in significant increases in NPP relative to their typically low values in the basin bottom areas. Associated with those changes grazing has caused negative deviations in most grasslands with potentially high NPP. As a consequence, the spatial heterogeneity of NPP was significantly lowered. A slight decreasing trend in NPP of the IRB was revealed by remote sensing, which contrasted the increasing trend that should have occurred due to climate change. This difference between the real and simulated trends was mainly attributed to a weakening of positive human impacts on NPP in croplands and strengthening of negative impacts in grasslands. These findings provide valuable information for current land‐use management and ecosystem restoration in Northwest China and Central Asia.

Highly Productive Ice Algal Mats in Arctic Melt Ponds: Primary Production and Carbon Turnover

Arctic summer sea ice extent is decreasing and thinning, forming melt ponds that cover more than 50% of the sea ice area during the peak of the melting season. Despite of this, ice algal communities in melt ponds are understudied and so are their contribution to the Arctic Ocean primary production and carbon turnover. While melt ponds have been considered as low productive, recent studies suggest that accumulated ice algal potentially facilitate high and yet overlooked rates of carbon turnover. Here we report on ice algal communities forming dense mats not previously described, collected from melt ponds in the northern Barents Sea in July. We document on distinct layered and brown colored mats with high carbon assimilation and net primary production rates compared to ice algal communities and aggregates, in fact comparable to benthic microalgae at temperate tidal flats. Rates of gross and net primary production, as well as community respiration rates were obtained from oxygen micro profiling, and carbon assimilation calculations were supported by 14C incubations, pigment analysis and light microscopy examinations. The melt pond algal mats consisted of distinct colored layers and differed from aggregates with a consisted layered structure. We accordingly propose the term melt pond algal mats, and further speculate that these dense ice algal mats may provide an important yet overlooked source of organic carbon in the Arctic food-web. A foodweb component likely very sensitive to climate driven changes in the Arctic Ocean and pan-Arctic seas.

Seasonal Dynamics of Algal Net Primary Production in Response to Phosphorus Input in a Mesotrophic Subtropical Plateau Lake, Southwestern China

A comprehensive 3-dimensional hydrodynamic and eutrophication model, the environmental fluid dynamics code model (EFDC) with three functional phytoplankton groups, was applied to simulate the algal dynamics in a mesotrophic P-limited subtropical plateau lake, Lake Erhai, Southwestern China. Field investigations revealed the seasonal patterns in external total phosphorus (TP) input and TP concentration, as well as the composition of the phytoplankton community. The model was calibrated to reproduce qualitative features and the succession of phytoplankton communities, and the net primary production was calculated. The modeled daily net primary production (NPP) ranged between −16.89 and 15.12 mg C/m2/d and exhibited significant seasonal variation. The competition for phosphorus and temperature was identified as the primary governing factor of NPP by analyzing the parameter sensitivity and limitation factors of the lake. The simulation of four nutrient loading reduction scenarios suggested high phytoplankton biomass and NPP sensitivity to the external TP reduction. A significant positive correlation was found among NPP, total phytoplankton biomass and TP concentration. Overall, this work offers an alternative approach to estimating lake NPP, which has the potential to improve sustainable lake management.

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

AbstractAn ecosystem is a complex system with a large number of dynamic variables, which poses challenges to the optimization of ecosystem services. However, traditional ecosystem services optimization methods do not take into account the complexity and uncertainty of variables. To address this complexity and uncertainty, we propose an innovative approach using a mixed‐cell cellular automata (MCCA) model and a Bayesian belief network (BBN) model for ecosystem service optimization. This approach was applied to the southern region of Sichuan Province, China, using an existing dataset to simulate land use patterns and predict ecosystem services in 2035 under different development scenarios. To achieve ecological restoration and conservation, we also determined the key factor combinations and key ecological regions at various ecosystem service levels. Results showed that ecological protection scenario design has important significance as a reference for maintaining and ameliorating regional ecosystem services and functions. We also identified that the highest level of ecosystem services was mainly located in the areas with the highest net primary productivity (NPP), the highest slope, the highest forestland area, and low ET. According to these findings, some suggestions for ecological restoration and conservation in key regions were put forward. This approach fully considers the uncertainty of factors; therefore, it can be used as an effective tool for designing ecosystem management strategies.

Valuing the Impact of Forest Disturbances on the Climate Regulation Service of Western U.S. Forests

The protection and expansion of forest carbon sinks are critical to achieving climate-change mitigation targets. Yet, the increasing frequency and severity of forest disturbances challenge the sustainable provision of forest services. We investigated patterns of forest disturbances’ impacts on carbon sinks by combining spatial datasets of forest carbon sequestration from biomass growth and emissions from fire and bark beetle damage in the western United States (U.S.) and valued the social costs of forest carbon losses. We also examined potential future trends of forest carbon sinks under two climate-change projections using a global vegetation model. We found that forest carbon losses from bark-beetle damage were larger than emissions from fires between 2003 and 2012. The cumulative social costs of forest carbon losses ranged from USD 7 billion to USD 72 billion, depending on the severity of global warming and the discount rate. Forest carbon stocks could increase around 5% under Representative Concentration Pathway (RCP) 4.5 or 7% under RCP 8.5 by 2091 relative to 2011 levels, mostly in forests with high net primary productivity. These results indicate that spatially explicit management of forest disturbances may increase forest carbon sinks, thereby improving opportunities to achieve critical climate-change mitigation goals.

Long-Term Spatiotemporal Variation of Net Primary Productivity and Its Correlation With the Urbanization: A Case Study in Hubei Province, China

Net primary productivity (NPP) is a critical component in terrestrial ecosystem carbon cycles. Thus, quantitatively estimating and monitoring the dynamics of NPP have become key aspects for exploring the carbon cycle of terrestrial ecosystems. Anthropogenic activity, such as urbanization, has significant effects on NPP and increases pressure on the natural resources of a specific region. However, to date, although many studies have focused on the relationship between NPP variation and urbanization, they usually ignored any differences at a long-term spatiotemporal variation of urbanization factors, which led to the insufficient understanding of the urbanization-induced impacts on NPP. As a result, this study effectively explored the spatiotemporal variation of NPP from 2001 to 2012 and its corresponding relationship with urbanization, taking the Hubei Province in China as a case study area. To clarify the degree of urbanization, the spatial distribution and temporal variation of population and gross domestic product (GDP) were simulated based on the elevation-adjusted human settlement index and nighttime lights data. The major results showed that high NPP areas were located in those highlands with widespread woodland, in which the NPP value continued to grow during the period. The low NPP areas were mainly distributed in urban areas, and the NPP value had a continued and visible loss. The population and GDP both had a strong correlation with NPP. The significant negative correlation was concentrated in the center of Hubei, with a dense population and developed economy. In order to further realize their complex relationship, the correlation coefficients between the annual NPP and the two factors from 2001 to 2012 were calculated, and the changing trends were investigated. Overall, the findings of this study may provide a reference for studies on the interaction between ecological environment and socioeconomic processes under the background of global rapid urbanization.