Variation In Aboveground Net Primary Production Research Articles

Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems

CONTEXTMonitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVETo develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the “virtual entity” of a digital twin support decision system where the “physical entity” is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODSFortnight ANPP data were calculated from MODIS EVI for the 2001–2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONSThe sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCEBy integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production.

Plant functional traits are dynamic predictors of ecosystem functioning in variable environments

Abstract A central goal at the interface of ecology and conservation is understanding how the relationship between biodiversity and ecosystem function (B–EF) will shift with changing climate. Despite recent theoretical advances, studies which examine temporal variation in the functional traits and mechanisms (mass ratio effects and niche complementarity effects) that underpin the B–EF relationship are lacking. Here, we use 13 years of data on plant species composition, plant traits, local‐scale abiotic variables, above‐ground net primary productivity (ANPP), and climate from the alpine tundra of Colorado (USA) to investigate temporal dynamics in the B–EF relationship. To assess how changing climatic conditions may alter the B–EF relationship, we built structural equation models (SEMs) for 11 traits across 13 years and evaluated the power of different trait SEMs to predict ANPP, as well as the relative contributions of mass ratio effects (community‐weighted mean trait values; CWM), niche complementarity effects (functional dispersion; FDis) and local abiotic variables. Additionally, we coupled linear mixed effects models with Multimodel inference methods to assess how inclusion of trait–climate interactions might improve our ability to predict ANPP through time. In every year, at least one SEM exhibited good fit, explaining between 19.6% and 57.2% of the variation in ANPP. However, the identity of the trait which best explained ANPP changed depending on winter precipitation, with leaf area, plant height and foliar nitrogen isotope content (δ15N) SEMs performing best in high, middle and low precipitation years, respectively. Regardless of trait identity, CWMs exerted a stronger influence on ANPP than FDis and total biotic effects were always greater than total abiotic effects. Multimodel inference reinforced the results of SEM analysis, with the inclusion of climate–trait interactions marginally improving our ability to predict ANPP through time. Synthesis. Our results suggest that temporal variation in climatic conditions influences which traits, mechanisms and abiotic variables were most responsible for driving the B–EF relationship. Importantly, our findings suggest that future research should consider temporal variability in the B–EF relationship, particularly how the predictive power of individual functional traits and abiotic variables may fluctuate as conditions shift due to climate change.

Grassland productivity responds unexpectedly to dynamic light and soil water environments induced by photovoltaic arrays

AbstractAgrivoltaic (AV) systems are designed to coproduce photovoltaic (PV) energy on lands simultaneously supporting agriculture (food/forage production). PV infrastructure in agroecosystems alters resources critical for plant growth, and water‐limited agroecosystems such as grasslands are likely to be particularly sensitive to the unique spatial and temporal patterns of incident sunlight and soil water inherent within AV systems. However, the impact of resource alteration on forage production, the primary ecosystem service from managed grasslands, is poorly resolved. Here, we evaluated seasonal patterns of soil moisture (SM) and diurnal variation in incident sunlight (photosynthetic photon flux density [PPFD]) in a single‐axis‐tracking AV system established in a formerly managed semiarid C3 grassland in Colorado. Our goals were to (1) quantify dynamic patterns of PPFD and SM within a 1.2 MW PV array in a perennial grassland, and (2) determine how aboveground net primary production (ANPP) and photosynthetic parameters responded to the resource patterns created by the PV array. We hypothesized that spatial variability in ANPP would be strongly related to SM patterns, typical of most grasslands. We measured significant reductions in ANPP directly beneath PV panels, where SM and PPFD were both low. However, in locations with significantly increased SM from the shedding and redistribution of precipitation by PV panels, ANPP was not increased. Instead, ANPP was greatest in locations where plants were shaded in the afternoon but received high levels of PPFD in the morning hours, when air temperatures and vapor pressure deficits were relatively low. Thus, contrary to expectations, we found relatively weak relationships between SM and ANPP despite significant spatial variability in both. Further, there was little evidence that light‐saturated photosynthesis (Asat) and quantum yield of CO2 assimilation (ϕCO2) differed for plants growing directly beneath (lowest PPFD) versus between (highest PPFD) PV panels. Overall, the AV system established in this semiarid managed grassland did not alter patterns of ANPP in ways predictable from past studies of controls of ANPP in open grasslands. However, our results suggest that the diurnal timing of low versus high periods of PPFD incident on plants is an important determinant of productivity patterns in grasslands.

Land use intensification alters the relative contributions of plant functional diversity and soil properties on grassland productivity.

Understanding the mechanisms of grassland productivity variation is critical for global carbon cycling and climate change mitigation. Heretofore, it is unknown how different environmental factors drive small-scale spatial variation in productivity, and whether land use intensification, one of the most important global changes, can regulate the processes that drive productivity change. Here we performed an 18-year exclosure experiment across six sites with high-intensity mowing/grazing history in northern China to examine the effects of land use intensification on plant functional diversity, soil properties, and their relative contributions to above-ground net primary productivity (ANPP). We found that plant functional diversity and soil properties contributed to the variation in ANPP both independently and equally in enclosed grasslands (plant diversity: 20.6%; soil properties: 19.5%). Intensive land use significantly decreased the Rao's quadratic entropy (RaoQ) and community-weighted mean value (CWM) of plant height, and further suppressed the contributions of plant functional diversity to ANPP. In contrast, intensive land use increased soil available N, P, pH, electrical conductivity, and homogeneity of soil available P, and strengthened their contributions to ANPP (31.5%). Our results indicate that high-intensity land use practices in grasslands decrease the role of plant functional diversity, but strengthen the effects of soil properties on productivity. We, therefore, suggest that plant functional diversity can be used effectively to boost productivity in undisturbed grasslands, while soil properties might be a more critical consideration for grassland management in an areas with increased land use.

The validity of ecological hypotheses concerning aboveground organisms for soil microbial biomass and diversity across soil depths on the Mongolian Plateau

Although many empirical hypotheses have been proposed and tested to explain the biomass and diversity of aboveground organisms, these hypotheses have not been tested for soil microorganisms across soil depths. Here, we determined the ability of hypotheses concerning physiological tolerance, plant energy, soil energy, and habitat heterogeneity to explain the variance in microbial biomass (as determined by phospholipid fatty acid) and diversity (as determined by high-throughput sequencing) in surface (0–20 cm) and deep (40–60 cm) soil layers on the Mongolian Plateau. We found that aboveground net primary production (ANPP) and plant species richness steeply declined as aridity values increased. Physiological tolerance and habitat heterogeneity explained the spatial variation in ANPP while physiological tolerance and soil energy explained the spatial variation in plant diversity. The relationships between aridity and microbial variables differed between soil layers or microbial groups. Aridity values had hump-shaped relationships with surface-soil bacterial biomass or diversity but had linear and negative relationships with surface-soil fungal biomass or diversity. Aridity values were linearly and negatively related with deep-soil bacterial or fungal biomass but were unrelated with deep-soil bacterial or fungal diversity. We also found the ability of the ecological hypotheses to explain the variances in microbial variables (biomass and diversity) diffed between soil layers or microbial groups on the Mongolia Plateau. The surface-soil bacterial biomass was mainly associated with plant energy and physiological tolerance while surface-soil bacterial diversity was mainly associated with habitat heterogeneity and physiological tolerance; surface-soil fungal biomass and diversity were mainly associated with habitat heterogeneity and physiological tolerance. The deep-soil bacterial biomass and diversity were mainly associated with physiological tolerance and soil energy; deep-soil fungal biomass was mainly associated with plant energy and physiological tolerance while deep-soil fungal diversity was mainly associated with soil energy and habitat heterogeneity. Our results showed that the major ecological hypotheses traditionally applied to plant communities can explain much of the variation in soil microbial biomass and diversity. These findings that identifying ability of ecological hypotheses to explain the variance in both plant and microbial biomass and diversity will be crucial importance to understand biodiversity assembly under ongoing environmental changes.

Nitrogen enrichment alters climate sensitivity of biodiversity and productivity differentially and reverses the relationship between them in an alpine meadow

Biodiversity and productivity that highly determine ecosystem services are varying largely under global change. However, the climate sensitivity of them and their relationship are not well understood, especially in the context of increasing nitrogen (N) deposition. Here, based on a six-year N manipulation experiment in an alpine meadow, we quantified interannual climate sensitivity of species richness (SR) and above-ground net primary productivity (ANPP) as well as SR-ANPP relationship as affected by six N addition rate (Nrate) gradients. We found that interannual variations in ANPP and SR were mainly driven by temperature instead of precipitation. In the plots without N addition, higher temperature substantially increased ANPP but reduced SR across years, thus resulting in a negative SR-ANPP relationship. However, the negative and positive responses of SR and ANPP to temperature increased and declined significantly with increasing Nrate, respectively, leading to a shift of the negative relationship between SR and ANPP into a positive one under high Nrate. Moreover, the adverse influence of drought on SR and ANPP would be aggravated by N fertilization, as indicated by the increased positive effect of precipitation on them under N enrichment. Our findings indicate that climate sensitivity of productivity and biodiversity may be misestimated if the impact of N deposition is not considered, and the importance of biodiversity to maintain productivity would enhance as N deposition increases. This study provides a new insight to explain variation of biodiversity-productivity relationship along with environmental changes.

Phosphorus is the key soil indicator controlling productivity in planted Masson pine forests across subtropical China

Soil physiochemical properties are critical to understanding forest productivity and carbon (C) finance schemes in terrestrial ecosystems. However, few studies have focused on the effects of the soil physiochemical properties on the productivity in planted forests. This study was therefore conducted at 113 sampling plots located in planted Masson pine forests across subtropical China to test what and how the aboveground net primary productivity (ANPP) would be explained by the soil physiochemical properties, stand attributes, and functional traits using regression analysis and structural equation modelling (SEM). Across subtropical China, the ANPP ranged from 1.79 to 14.04 Mg ha−1 year−1 among the plots, with an average value of 6.05 Mg ha−1 year−1. The variations in ANPP were positively related to the stand density, root phosphorus (P) content and soil total P content but were negatively related to the stand age, root C:P and N:P ratios. Among these factors, the combined effects of stand density, stand age and soil total P content explained 35% of the ANPP variations. The SEM results showed the indirect effect of the soil total P content via the root P content and C:P ratio on the ANPP and indirect effects of other soil properties (e.g., pH, clay, and bulk density) via the soil total P content and root functional traits (e.g., root P, C:P, and N:P) on the ANPP. By considering all possible variables and paths, the best-fitting SEM explained only 11–13% of the ANPP variations, which suggested that other factors may be more important in determining the productivity in planted forests. Overall, this study highlights that soil total P content should be used as a key soil indicator for determining the ANPP in planted Masson pine forests across subtropical China, and suggests that the root functional traits mediate the effects of soil properties on the ANPP.

Trends and variations in the activity of global vegetation in response to climate variability between 1987 and 1997

Eleven-year (1987-1997) time series data of remotely sensed vegetation index (NDVI) and meteorological observations (temperature, precipitation, cloud cover and relative humidity) provided a powerful tool to illuminate the response of global terrestrial vegetation to short-and long-term climate variability. NDVI being a sensitive estimator of the amount of photosynthetic active radiation intercepted by the canopy has been treated as a proxy for above ground net primary production (ANPP). Analyses of trends, multiple regression and correlation analyses were employed. The main result indicates a considerable increase (0.7~1.9%/year) of monthly vegetation production in all ecosystems over the investigated period, allied with an analogous increase (0.9~15%/year) in precipitation. Additionally, several direct relationships were also observed on the intra- and inter-annual time scales suggesting that the increase and variation of ANPP in most biomes could be mainly linked to the corresponding increase and variation in precipitation. Overall, the four climate variables play a considerable role in the inter-annual variability of ANPP of global vegetation.

Assessing precipitation, evapotranspiration, and NDVI as controls of U.S. Great Plains plant production

AbstractProductivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation‐related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi‐year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite‐derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April‐to‐July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April‐to‐July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May‐to‐September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county‐level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county‐level NRCS ANPP. Accounting for how different precipitation‐related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision‐making by land managers and use of grassland ANPP for biofuels.

Modelling aboveground net primary production (ANPP) of an Atlantic mountain grassland based on time series approach

Because primary productivity is related both with the energy that sustains food webs and with species diversity, it is usually considered a key ecosystem property and a reliable indicator of available forage. In this work the aboveground net primary production (ANPP) of an Atlantic mountain grassland system was modelled in order to attempt producing short-term forecasts. Since grazing influences productivity, two treatment levels (grazing and exclusion) were experimentally applied in each of three field sites. Monthly ANPP data were then collected over three consecutive vegetative periods (2006-2008), thereby obtaining six time series (one per plot). Since no significant differences among sites (within treatments) were found, these six series were later reduced through averaging to only two series (one per treatment level). Two kinds of statistical models were then used to attempt monthly ANPP forecasting: exponential smoothing methods and ARIMA models. Both methodologies turned out to produce inadequate forecasts due to the presence of marked local features (innovative outliers) in our relatively short time-series data. Nonetheless, useful information for a more innovative shepherding management was revealed (e.g. the presence of within-year variation in ANPP, and differences between the grazing and exclusion treatments). Longer data series, which would require a more demanding effort in sampling investment, are likely necessary in order to obtain adequate forecasts using these time series methodologies.

Variability in community productivity—mediating effects of vegetation attributes

Abstract Plant productivity varies through time in response to environmental fluctuations. Reducing variability in productivity requires an improved understanding of how plant community attributes interact with environmental fluctuations to influence plant growth dynamics. We evaluated links between two community attributes, species diversity and abundance‐weighted values of specific leaf area (SLA), and temporal variability in grassland productivity at patch (local) and aggregate (multipatch) spatial scales. Aggregate communities were created by combining patches of spatially distinct communities of perennial plant species from grassland biodiversity experiments in Texas, USA. Interannual variability in above‐ground net primary productivity (ANPP) of aggregate communities was analysed as a function of two multiplicative components, mean temporal variability in the ANPP of patches and temporal synchrony in ANPP dynamics among patches. We used regression analyses to determine whether temporal variability in aggregate ANPP and its components were correlated with either species diversity or community‐weighted SLA over 5 years. Temporal variability in ANPP of aggregate communities was strongly correlated with temporal variability in patch ANPP. Increasing mean SLA reduced ANPP variability of aggregate communities by increasing mean productivity. Increased temporal changes in patch‐scale SLA further reduced temporal variability in aggregate ANPP by reducing effects of precipitation fluctuations on productivity. Conversely, increasing species diversity over the narrow range measured increased temporal variability in aggregate ANPP. High diversity was associated with reduced dominance of temporally stable C4 grasses. Our results implicate means and patch‐scale temporal dynamics in community SLA as potential indicators of variability in grassland primary productivity through time. A plain language summary is available for this article.

Regional grassland productivity responses to precipitation during multiyear above- and below-average rainfall periods.

There is considerable uncertainty in the magnitude and direction of changes in precipitation associated with climate change, and ecosystem responses are also uncertain. Multiyear periods of above- and below-average rainfall may foretell consequences of changes in rainfall regime. We compiled long-term aboveground net primary productivity (ANPP) and precipitation (PPT) data for eight North American grasslands, and quantified relationships between ANPP and PPT at each site, and in 1-3year periods of above- and below-average rainfall for mesic, semiarid cool, and semiarid warm grassland types. Our objective was to improve understanding of ANPP dynamics associated with changing climatic conditions by contrasting PPT-ANPP relationships in above- and below-average PPT years to those that occurred during sequences of multiple above- and below-average years. We found differences in PPT-ANPP relationships in above- and below-average years compared to long-term site averages, and variation in ANPP not explained by PPT totals that likely are attributed to legacy effects. The correlation between ANPP and current- and prior-year conditions changed from year to year throughout multiyear periods, with some legacy effects declining, and new responses emerging. Thus, ANPP in a given year was influenced by sequences of conditions that varied across grassland types and climates. Most importantly, the influence of prior-year ANPP often increased with the length of multiyear periods, whereas the influence of the amount of current-year PPT declined. Although the mechanisms by which a directional change in the frequency of above- and below-average years imposes a persistent change in grassland ANPP require further investigation, our results emphasize the importance of legacy effects on productivity for sequences of above- vs. below-average years, and illustrate the utility of long-term data to examine these patterns.

Aboveground net primary productivity of vegetation along a climate-related gradient in a Eurasian temperate grassland: spatiotemporal patterns and their relationships with climate factors

Accurate assessments of spatiotemporal patterns in net primary productivity and their links to climate are important to obtain a deeper understanding of the function, stability and sustainability of grassland ecosystems. We combined a satellite-derived NDVI time-series dataset and field-based samples to investigate spatiotemporal patterns in aboveground net primary productivity (ANPP), and we examined the effect of growing season air temperate (GST) and precipitation (GSP) on these patterns along a climate-related gradient in an eastern Eurasian grassland. Our results indicated that the ANPP fluctuated with no significant trend during 2001–2012. The spatial distribution of ANPP was heterogeneous and decreased from northeast to southwest. The interannual changes in ANPP were mainly controlled by year-to-year GSP; a strong correlation of interannual variability between ANPP and GSP was observed. Similarly, GSP strongly influenced spatial variations in ANPP, and the slopes of fitted linear functions of the GSP–ANPP relationship increased from arid temperate desert grassland to humid meadow grassland. An exponential function could be used to fit the GSP–ANPP relationship for the entire region. An improved moisture index that combines the effects of GST and GSP better explained the variations in ANPP compared with GSP alone. In comparisons with the previous studies, we found that the relationships between spatiotemporal variations in ANPP and climate factors were probably scale dependent. We imply that the quantity and spatial range of analyzed samples contribute to these different results. Multi-scale studies are necessary to improve our knowledge of the response of grassland ANPP to climate change.

Impacts of climate change and human activities on the aboveground production in alpine grasslands: a case study of the source region of the Yellow River, China

Changes in aboveground net primary productivity (ANPP) in alpine grasslands are the consequence of climate change and human activities, but it is difficult to disentangle their relative contributions. Based on monthly remote-sensed vegetation index and meteorological data during the period 1982–2010, we analysed the long-term variation of annual ANPP in the source region of the Yellow River and quantified the effects of climate and human activities including grazing on ANPP variability, using the Carnegie–Ames–Stanford Approach (CASA) model and the ANPP-based residual trend analysis method. Our results suggested that ANPP increased in 80% of alpine grasslands. Areas with negative changes in ANPP were found mainly in the eastern portion of the region, accounting for 0.3% of alpine grasslands. ANPP was positively correlated with the monthly mean temperature from June to September and the sunshine duration in September at a regional scale. Moreover, ANPP was negatively correlated with the total livestock numbers. Using the residual trend analysis method, we demonstrated that climate and human activities accounted for 76.6 and 23.4%, respectively, of the variability in ANPP for the entire study region in 1982–2010. We concluded that climate change alleviated climatic constraints, in particular temperature limitations and sunshine duration, resulting in a significant increase in ANPP. Overgrazing was supposed to be the primary driver for grassland degradation in the eastern region. Our study has implications for grassland management and its sustainability to minimize the risk of grassland degradation and desertification processes in geo-ecologically and socially important regions such as the study region in China.

Plant functional trait diversity regulates the nonlinear response of productivity to regional climate change in Tibetan alpine grasslands.

The biodiversity-productivity relationship is still under debate for alpine grasslands on the Tibetan Plateau. We know little about direct and indirect effects of biotic and abiotic drivers on this relationship, especially in regard to plant functional trait diversity. Here, we examine how aboveground net primary productivity (ANPP) and precipitation use efficiency (PUE) respond to climate, soil and community structure across alpine grasslands on the Northern Tibetan Plateau. We found that both ANPP and PUE showed nonlinear patterns along water availability and site altitude variation, which together accounted for 80.3% and 68.8% of variation in ANPP and PUE, respectively, by optimal generalized additive models. Functional trait divergence (FTD) and community weighted mean (CWM) of plant functional traits were as important as plant species diversity (PSD) for explaining the nonlinear productivity-climate relationship. These findings were confirmed by results from principal component analyses and structural equation models. We also found that FTD was negatively correlated with PSD across different alpine grasslands. Our results implicate: first, the combinatorial influences of temperature and precipitation gradients are important for predicting alpine grassland dynamics; second, the convergence and divergence of plant functional traits may have the potential to elucidate the effect of plant diversity on ecosystem functionality.

Drivers of Variation in Aboveground Net Primary Productivity and Plant Community Composition Differ Across a Broad Precipitation Gradient

Aboveground net primary production (ANPP) is a key integrator of C uptake and energy flow in many terrestrial ecosystems. As such, ecologists have long sought to understand the factors driving variation in this important ecosystem process. Although total annual precipitation has been shown to be a strong predictor of ANPP in grasslands across broad spatial scales, it is often a poor predictor at local scales. Here we examine the amount of variation in ANPP that can be explained by total annual precipitation versus precipitation during specific periods of the year (precipitation periods) and nutrient availability at three sites representing the major grassland types (shortgrass steppe, mixed-grass prairie, and tallgrass prairie) spanning the broad precipitation gradient of the U.S. Central Great Plains. Using observational data, we found that precipitation periods and nutrient availability were much stronger predictors of site-level ANPP than total annual precipitation. However, the specific nutrients and precipitation periods that best predicted ANPP differed among the three sites. These effects were mirrored experimentally at the shortgrass and tallgrass sites, with precipitation and nutrient availability co-limiting ANPP, but not at the mixed-grass site, where nutrient availability determined ANPP exclusive of precipitation effects. Dominant grasses drove the ANPP response to increased nutrient availability at all three sites. However, the relative responses of rare grasses and forbs were greater than those of the dominant grasses to experimental nutrient additions, thus potentially driving species turnover with chronic nutrient additions. This improved understanding of the factors driving variation in ANPP within ecosystems spanning the broad precipitation gradient of the Great Plains will aid predictions of alterations in ANPP under future global change scenarios.

Aboveground Net Primary Production of tree cover at the post-disturbance area in the Tatra National Park, Slovakia

Abstract Large-scale disturbances under the conditions of Slovakia, caused especially by storm and bark beetle, bring dramatic decline in carbon budget of the country, besides other negative consequences. The largest disturbance in modern history of the Slovak forestry was the storm damage that occurred in November 2004. The Tatra National Park (TNP) was one of the most affected regions. Thus, in this territory, two transects (T1 – the Danielov dom site and T2 – near the Horný Smokovec village) were established to survey basic dendrometric properties of trees in young stands established after the disaster. The standing stock of aboveground biomass in tree cover for the spring and autumn 2014 was calculated using the recorded variables, i.e. tree height and diameter measured at the stem base, together with the region-specific allometric relations. Then, the Aboveground Net Primary Production (ANPP) in tree cover was estimated with respect to its components (stem, branches and foliage). ANPP was 315 g m−2 per year (Transect T1), and 391 g m−2 per year (Transect T2). The differences in the structure of ANPP, i.e. contribution of tree components, were found between transects T1 and T2. They were caused by the contrasting tree species composition, specifically the ratios between Norway spruce and broadleaved species. Broadleaves allocated more biomass production to foliage than spruce. This phenomenon together with higher turnover (once a year) of foliage caused that broadleaves manifest higher share of fast-cycling carbon in comparison to the amount of carbon sequestrated in woody parts (stem and branches). High variability of ANPP was found within the transects, i.e. among the plots (microsites). As for the representative estimation of the standing stock of aboveground part of tree cover as well as ANPP at the post-disturbance area in the TNP territory, the survey should be performed on a net of research plots. Only this approach enables reliable estimates of carbon amount sequestrated in woody parts, eventually carbon yearly absorbed by young forest stands.

Primary Productivity and Precipitation-Use Efficiency in Temperate Grassland in the Loess Plateau of China.

Clarifying spatial variations in aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of grasslands is critical for effective prediction of the response of terrestrial ecosystem carbon and water cycle to future climate change. Though the combination use of remote sensing products and in situ ANPP measurements, we quantified the effects of climatic [mean annual precipitation (MAP) and precipitation seasonal distribution (PSD)], biotic [leaf area index (LAI)] and abiotic [slope gradient, aspect, soil water storage (SWS) and other soil physical properties] factors on the spatial variations in ANPP and PUE across different grassland types (i.e., meadow steppe, typical steppe and desert steppe) in the Loess Plateau. Based on the study, ANPP increased exponentially with MAP for the entire temperate grassland; suggesting that PUE increased with increasing MAP. Also PSD had a significant effect on ANPP and PUE; where more even PSD favored higher ANPP and PUE. Then MAP, more than PSD, explained spatial variations in typical steppe and desert steppe. However, PSD was the dominant driving factor of spatial variations in ANPP of meadow steppe. This suggested that in terms of spatial variations in ANPP of meadow steppe, change in PSD due to climate change was more important than that in total annual precipitation. LAI explained 78% of spatial PUE in the entire Loess Plateau temperate grassland. As such, LAI was the primary driving factor of spatial variations in PUE. Although the effect of SWS on ANPP and PUE was significant, it was nonetheless less than that of precipitation and vegetation. We therefore concluded that changes in vegetation structure and consequently in LAI and/or altered pattern of seasonal distribution of rainfall due to global climate change could significantly influence ecosystem carbon and water cycle in temperate grasslands.

Anthropogenic nitrogen deposition predicts local grassland primary production worldwide

Humans dominate many important Earth system processes including the nitrogen (N) cycle. Atmospheric N deposition affects fundamental processes such as carbon cycling, climate regulation, and biodiversity, and could result in changes to fundamental Earth system processes such as primary production. Both modelling and experimentation have suggested a role for anthropogenically altered N deposition in increasing productivity, nevertheless, current understanding of the relative strength of N deposition with respect to other controls on production such as edaphic conditions and climate is limited. Here we use an international multiscale data set to show that atmospheric N deposition is positively correlated to aboveground net primary production (ANPP) observed at the 1‐m2 level across a wide range of herbaceous ecosystems. N deposition was a better predictor than climatic drivers and local soil conditions, explaining 16% of observed variation in ANPP globally with an increase of 1 kg N·ha−1·yr−1 increasing ANPP by 3%. Soil pH explained 8% of observed variation in ANPP while climatic drivers showed no significant relationship. Our results illustrate that the incorporation of global N deposition patterns in Earth system models are likely to substantially improve estimates of primary production in herbaceous systems. In herbaceous systems across the world, humans appear to be partially driving local ANPP through impacts on the N cycle.

Climate change scenarios of herbaceous production along an aridity gradient: vulnerability increases with aridity.

Climate change is expected to reduce annual precipitation by 20% and increase its standard deviation by 20% in the eastern Mediterranean. We have examined how these changes may affect herbaceous aboveground net primary production (ANPP) and its inter-annual coefficient of variation (CV) in natural rangelands along a desert-Mediterranean precipitation gradient, at five sites representing arid, semi-arid, and Mediterranean-type ecosystems, respectively, all showing positive linear relationships between herbaceous ANPP and annual precipitation. Scenarios of reduced annual precipitation and increased inter-annual precipitation variability were defined by manipulating mean annual precipitation (MAP) and its standard deviation. We simulated precipitation and calculated ANPP using current ANPP-precipitation relationships. Our model predicts that reduced precipitation will strongly reduce ANPP in arid and semi-arid sites. Moreover, the effect of reduced precipitation on the CV of ANPP along the entire gradient may be modified by changes in inter-annual variability in MAP. Reduced precipitation combined with increased precipitation variability was the scenario most relevant to the wet end of the gradient, due to the increased likelihood for both dry and rainy years. In contrast, the scenario most relevant to the arid end of the gradient combined reduced precipitation with decreased precipitation variability, due to the strong effect on mean ANPP. All scenarios increased variability of ANPP along the entire gradient. However, the higher sensitivity of vegetation at arid and semi-arid sites (i.e., lower forage production) to future changes in the precipitation regime emphasizes the need to adapt grazing management in these ecosystems to secure their long-term viability as sustainable rangelands.