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Abstract Vegetation restoration often has a significant effect on the supply of an ecosystem service (ES). Assessment of this effect is crucial for informed decision-making in sustainable ecosystem management. In this respect, this study analyses three regulating, two provisioning, and a single cultural ES over a 30-year period (1985 to 2015, with 15 years pre-restoration and 15 years post-restoration) in the Loess Plateau, China, using data from a combination of modelling and statistical yearbooks. On applying a suite of standard statistical tools, results indicate: (1) regional scale restoration promotes the increase of vegetation cover as the coverage increased faster between 2000 and 2015 than between 1985 and 2000; (2) vegetation restoration changes the temporal trend of regulating ESs, and enhances the supply of provisioning and cultural ESs; (3) the 40 municipalities of the Loess Plateau can be divided into four ES categories where areas with poor ES delivery account for about 30% of the Loess Plateau; (4) vegetation restoration changes the interaction among ESs, resulting in synergistic relationships between provisioning and regulating ESs; (5) precipitation has a significant impact on regulating ESs, while population density is critical for provisioning and cultural ESs. This study demonstrates that ESs, their interactions and their groupings can change across both time and space following the implementation of a vegetation restoration programme, which makes understanding ES dynamics complicated. Recommendations are provided for improved and coherent ecosystem management. Graphical abstract Unlabelled Image Highlights • Study assessed the impact of vegetation restoration on six ecosystem services. • 30% of the Loess Plateau was weak in ecosystem service delivery. • Vegetation restoration changed interaction and grouping among ecosystem services. • Social-natural factors were found to drive ecosystem service variation. [ABSTRACT FROM AUTHOR]

Although the patterns and drivers of soil microbial community composition are well studied, little is known about the effects of plant–soil interactions and soil depth on soil microbial distribution at a regional scale. We examined 195 soil samples from 13 sites along a climatic transect in the temperate grasslands of northern China to measure the composition of and factors influencing soil microbial communities within a 1-m soil profile. Soil microbial community composition was measured using phospholipid fatty acids (PLFA) analysis. Fungi predominated in topsoil (0–10 cm) and bacteria and actinomycetes in deep soils (40–100 cm), independent of steppe types. This variation was explained by contemporary environmental factors (including above- and below-ground plant biomass, soil physicochemical and climatic factors) >58% in the 0–40 cm of soil depth, but <45% in deep soils. Interestingly, when we considered the interactive effects between plant traits (above ground biomass and root biomass) and soil factors (pH, clay content, and soil total carbon, nitrogen, phosphorous), we observed a significant interaction effect occurring at depths of 10–20 cm soil layer, due to different internal and external factors of the plant–soil system along the soil profile. These results improve understanding of the drivers of soil microbial community composition at regional scales. [ABSTRACT FROM AUTHOR]

Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270 km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature ( T max ) ranging from 3.0 °C (upstream) to 1.3 °C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of T max simulated by the T-NET model by 0.62 °C on average between April and September. However, difference between the shading routines reaches up to 2 °C (monthly average) at the upstream-most station. Standard deviation of errors on T max is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale. [ABSTRACT FROM AUTHOR]

Significant soil pH decrease has been reported in Chinese croplands in response to enhanced chemical fertilizer application and crop yields. However, the temporal and spatial variation of soil acidification rates across Chinese croplands is still unclear. We therefore assessed trends in soil acidification rates across provincial China for the period 1980–2010 by calculating inputs-outputs of major cations and anions in cropland systems. Nitrogen (N) induced proton production increased from 4.7 keq H + /ha/yr in 1980 to a peak of 11.0 keq H + /ha/yr in 1996 and remained nearly constant after 2000 at a rate of approximately 8.6 keq H + /ha/yr. The proton production induced by crop removal increased from 1.2 to 2.3 keq H + /ha/yr. The total proton production thus increased from 5.9 to 10.9 keq H + /ha/yr in the 30 years. As a result, the actual acidification rate, reflected by (base) cation losses, accelerated from 2.3 to 6.2 keq H + /ha/yr and the potential acidification rate, reflected by phosphorus accumulation accelerated from 0.2 to 1.3 keq H + /ha/yr. The national averaged total acidification rates were thus estimated to increase from 2.6 to 7.6 keq H + /ha/yr in the past 30 years. The highest soil acidification rate occurred in the Jiangsu Province with a rate of 17.9 keq H + /ha/yr, which was due to both high N application rates and high base cation removals by crops and crop residues. The combination of elevated N inputs and decreased N use efficiency (NUE) in response to those N inputs, thus enhancing the nitrate discharge, were the main reasons for the accelerated acidification in Chinese croplands. Considering the expected growth of food demand in the future, and the linkage between grain production and fertilizer N consumption, a further acceleration of soil acidification can thus be expected, unless the N inputs is reduced and/or the NUE is increased substantially. [ABSTRACT FROM AUTHOR]

Climate change and emissions rates of contaminants are expected to affect distribution and fate of Persistent Organic Pollutants (POPs) in the environment, however, studies on these combined factors are rare. In this study, Perfluorooctane Sulfonate (PFOS) is used as an example to assess how those two factors synthetically affect fate and disposition of POPs in the Bohai Rim of China by using the Berkeley-Trent-Urban-Rural (BETR-Urban-Rural) model. We set up three climate change scenarios and four emission scenarios to conduct the simulations. The results show that climate change could have significant effects on the transport and fate of PFOS mainly including advection, inter-compartmental transfer under the “worst case” emission scenario. For most grids, a remarkable decrease in concentrations of PFOS are predicted for fresh water and urban soil in the future, with precipitation and temperature being predominant factors, whilst for coastal water and rural soil, an increasing trend is predicted. Additionally, predicted sum of sources to the Bohai Sea increases greater than removals from the Bohai Sea in the future, adding evidence that concentrations of PFOS in coastal water will increase more in the future. Under scenarios of reduced emissions and climate change, concentrations of PFOS in each compartment decreased more rapidly over time. We suggest that assessment of future climate change impacts on fate of PFOS could take emission reductions into consideration. [ABSTRACT FROM AUTHOR]

This paper aims to determine the main factors that shape the spatial patterns of alien plant species occurrence across Natura 2000 Special Areas of Conservation (SACs) in Greece, and quantify their influence. A series of spatial analysis techniques for the development of a spatial database of the factors involved, followed by a boosted negative binomial Generalised Additive Model for location scale and shape, were implemented. Native plant species richness, topography and hydrography, human population density, and a spatial preference to the northern-western sites are the key factors that explain the variation in the occurrence of alien plant species. Native plant species richness and human population density have a positive effect on alien plant species presence, while topography aspects, such as elevation and slope, and the distance from the hydrographical network a negative one. All factors are indirectly linked to propagule pressure emphasizing the importance of human activities for the efforts on managing protected areas. [ABSTRACT FROM AUTHOR]

The detection of critical source areas (CSAs) is a key step in managing soil phosphorus (P) loss and preventing the long-term eutrophication of water bodies at regional scale. Most related studies, however, focus on a local scale, which prevents a clear understanding of the spatial distribution of CSAs for soil P loss at regional scale. Moreover, the continual, long-term variation in CSAs was scarcely reported. It is impossible to identify the factors driving the variation in CSAs, or to collect land surface information essential for CSAs detection, by merely using the conventional methodologies at regional scale. This study proposes a new regional-scale approach, based on three satellite sensors (ASTER, TM/ETM and MODIS), that were implemented successfully to detect CSAs at regional scale over 15 years (2000–2014). The approach incorporated five factors (precipitation, slope, soil erosion, land use, soil total phosphorus) that drive soil P loss from CSAs. Results show that the average area of critical phosphorus source areas (CPSAs) was 15,056 km 2 over the 15-year period, and it occupied 13.8% of the total area, with a range varying from 1.2% to 23.0%, in a representative, intensive agricultural area of China. In contrast to previous studies, we found that the locations of CSAs with P loss are spatially variable, and are more dispersed in their distribution over the long term. We also found that precipitation acts as a key driving factor in the variation of CSAs at regional scale. The regional-scale method can provide scientific guidance for managing soil phosphorus loss and preventing the long-term eutrophication of water bodies at regional scale, and shows great potential for exploring factors that drive the variation in CSAs at global scale. [ABSTRACT FROM AUTHOR]

Achieving sustainable management of phosphorus (P) is crucial for both global food security and global environmental protection. In order to formulate informed policy measures to overcome existing barriers of achieving sustainable P management, there is need for a sound understanding of the nature and magnitude of P flow through various systems at different geographical and temporal scales. So far, there is a limited understanding on the nature and magnitude of P flow over multiple years at the regional scale. In this study, we have developed a novel substance flow analysis (SFA) model in the MATLAB/Simulink® software platform that can be effectively utilized to analyse the nature and magnitude of multi-year P flow at the regional scale. The model is inclusive of all P flows and storage relating to all key systems, subsystems, processes or components, and the associated interactions of P flow required to represent a typical P flow system at the regional scale. In an annual time step, this model can analyse P flow and storage over as many as years required at a time, and therefore, can indicate the trends and changes in P flow and storage over many years, which is not offered by the existing regional scale SFA models of P. The model is flexible enough to allow any modification or the inclusion of any degree of complexity, and therefore, can be utilized for analysing P flow in any region around the world. The application of the model in the case of Gippsland region, Australia has revealed that the model generates essential information about the nature and magnitude of P flow at the regional scale which can be utilized for making improved management decisions towards attaining P sustainability. A systematic reliability check on the findings of model application also indicates that the model produces reliable results. [ABSTRACT FROM AUTHOR]

Farm system and nutrient budget models are increasingly being used in analysis to inform on farm decision making and evaluate land use policy options at regional scales. These analyses are generally based on the use of average annual pasture yields. In New Zealand (NZ), like in many countries, there is considerable inter-annual variation in pasture growth rates, due to climate. In this study a modelling approach was used to (i) include inter-annual variability as an integral part of the analysis and (ii) test the approach in an economic analysis of irrigation in a case study within the Hawkes Bay Region of New Zealand. The Agricultural Production Systems Simulator (APSIM) was used to generate pasture dry matter yields (DMY) for 20 different years and under both dryland and irrigation. The generated DMY were linked to outputs from farm-scale modelling for both Sheep and Beef Systems (Farmaxx Pro) and Dairy Systems (Farmax® Dairy Pro) to calculate farm production over 20 different years. Variation in DMY and associated livestock production due to inter-annual variation in climate was large, with a coefficient of variations up to 20%. Irrigation decreased this inter-annual variation. On average irrigation, with unlimited available water, increased income by $831 to 1195/ha, but when irrigation was limited to 250 mm/ha/year income only increased by $525 to 883/ha. Using pasture responses in individual years to capturing the inter-annual variation, rather than the pasture response averaged over 20 years resulted in lower financial benefits. In the case study income from irrigation based on an average year were 10 to > 20% higher compared with those obtained from individual years. [ABSTRACT FROM AUTHOR]

Soil C sequestration in croplands is deemed to be one of the most promising greenhouse gas mitigation options for agriculture. We have used crop-level yields, modeled heterotrophic respiration (Rh) and land use data to estimate spatio-temporal changes in regional scale net primary productivity (NPP), plant C inputs, and net biome productivity (NBP) in northern Japan's arable croplands and grasslands for the period of 1959–2011. We compared the changes in C stocks derived from estimated NBP and using repeated inventory datasets for each individual land use type from 2005 to 2011. For the entire study region of 2193 ha, overall annual plant C inputs to the soil constituted 37% of total region NPP. Plant C inputs in upland areas (excluding bush/fallow) could be predicted by climate variables. Overall NBP for all land use types increased from − 1.26 Mg C ha − 1 yr − 1 in 1959–0.26 Mg C ha − 1 yr − 1 in 2011. However, upland and paddy fields showed a decreased in NBP over the period of 1959–2011, under the current C input scenario. From 1988, an increase in agricultural abandonment (bush/fallow) and grassland cover caused a slow increase in the regional C pools. The comparison of carbon budgets using the NBP estimation method and the soil inventory method indicated no significant difference between the two methods. Our results showed C loss in upland crops, paddy fields and sites that underwent land use change from paddy field to upland sites. We also show C gain in grassland from 2005 to 2011. An underestimation of NBP or an overestimation of repeated C inventories cannot be excluded, but either method may be suitable for tracking absolute changes in soil C, considering the uncertainty associated with these methods. [ABSTRACT FROM AUTHOR]

The availability of detailed input data frequently limits the application of process-based models at large scale. In this study, we produced simplified meta-models of the simulated nitrous oxide (N 2 O) emission factors (EF) using NZ-DNDC. Monte Carlo simulations were performed and the results investigated using multiple regression analysis to produce simplified meta-models of EF. These meta-models were then used to estimate direct N 2 O emissions from grazed pastures in New Zealand. New Zealand EF maps were generated using the meta-models with data from national scale soil maps. Direct emissions of N 2 O from grazed pasture were calculated by multiplying the EF map with a nitrogen (N) input map. Three meta-models were considered. Model 1 included only the soil organic carbon in the top 30 cm (SOC30), Model 2 also included a clay content factor, and Model 3 added the interaction between SOC30 and clay. The median annual national direct N 2 O emissions from grazed pastures estimated using each model (assuming model errors were purely random) were: 9.6 Gg N (Model 1), 13.6 Gg N (Model 2), and 11.9 Gg N (Model 3). These values corresponded to an average EF of 0.53%, 0.75% and 0.63% respectively, while the corresponding average EF using New Zealand national inventory values was 0.67%. If the model error can be assumed to be independent for each pixel then the 95% confidence interval for the N 2 O emissions was of the order of ± 0.4–0.7%, which is much lower than existing methods. However, spatial correlations in the model errors could invalidate this assumption. Under the extreme assumption that the model error for each pixel was identical the 95% confidence interval was approximately ± 100–200%. Therefore further work is needed to assess the degree of spatial correlation in the model errors. [ABSTRACT FROM AUTHOR]

Streams provide ecosystem services to humans that depend on ecosystem functions, such as organic matter breakdown (OMB). OMB can be affected by land use-related disturbance. We measured OMB in 29 low-order streams in a region of contrasting land use in south-west Germany to quantify land use effects on OMB. We deployed fine and coarse mesh leaf bags in streams of forest, agricultural, vinicultural and urban catchments to determine the microbial and invertebrate-mediated OMB, respectively. Furthermore, we monitored physicochemical, geographical and habitat parameters to explain potential differences in OMB among land use types and sites. Only microbial OMB differed between land use types. Microbial OMB was negatively correlated with pH and invertebrate-mediated OMB was positively correlated with tree cover. Generally, OMB responded to stressor gradients rather than directly to land use. Therefore, the monitoring of specific stressors may be more relevant than land use to detect effects on ecosystem functions, and to extrapolate effects on functions, e.g. in the context of assessing ecosystem services. [ABSTRACT FROM AUTHOR]

Climate change is not only likely to improve conditions for crop production in Sweden, but also to increase weed pressure and the need for herbicides. This study aimed at assessing and contrasting the direct and indirect effects of climate change on herbicide leaching to groundwater in a major crop production region in south-west Sweden with the help of the regional pesticide fate and transport model MACRO-SE. We simulated 37 out of the 41 herbicides that are currently approved for use in Sweden on eight major crop types for the 24 most common soil types in the region. The results were aggregated accounting for the fractional coverage of the crop and the area sprayed with a particular herbicide. For simulations of the future, we used projections of five different climate models as model driving data and assessed three different future scenarios: (A) only changes in climate, (B) changes in climate and land-use (altered crop distribution), and (C) changes in climate, land-use, and an increase in herbicide use. The model successfully distinguished between leachable and non-leachable compounds (88% correctly classified) in a qualitative comparison against regional-scale monitoring data. Leaching was dominated by only a few herbicides and crops under current climate and agronomic conditions. The model simulations suggest that the direct effects of an increase in temperature, which enhances degradation, and precipitation which promotes leaching, cancel each other at a regional scale, resulting in a slight decrease in leachate concentrations in a future climate. However, the area at risk of groundwater contamination doubled when indirect effects of changes in land-use and herbicide use, were considered. We therefore concluded that it is important to consider the indirect effects of climate change alongside the direct effects and that effective mitigation strategies and strict regulation are required to secure future (drinking) water resources. [ABSTRACT FROM AUTHOR]

Abstract: The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power — VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM10, NOx and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM10, CO and O3. For NO2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM10, CO and O3. [Copyright &y& Elsevier]

Analysis of trade-offs and synergies between ecosystem services (ES) and their underlying drivers is a main issue in ES research. The analysis is complex and requires innovative analytical approaches. To address this complexity, we used an original approach that combines a multivariate regression tree (MRT), data analysis, and spatial mapping. We applied this approach to the main cropping region in France (mainly the Paris basin of production) using an existing dataset (i.e. soil, climate, crop sequences and management) from the French National Ecosystem Assessment to determine relationships between agricultural production, two services to farmers – nitrogen provision to crops and water provision to crops – and three services to society – blue water provision, water quality regulation, and climate regulation. To support land managers and decision-makers, we also analyzed the extent to which manageable soil properties and agricultural practices (crop rotation and management) are major drivers of trade-offs or synergies. We demonstrated that water quality regulation, nitrogen provision to crops, and climate regulation have synergistic relationships in production situations in the northeastern region of the study area due to the types of crop rotation, frequency of cover crops in the crop rotation, the soil pH, and the soil available water capacity. We also identified that cover crops, while promoting these three ES, can drive a trade-off between two key water-related services: water provision to crops and blue water provision (i.e. between a service to farmers and one to society). By capturing non-linear relationships and threshold effects, our MRT-based approach overcomes the main limitations of classic statistical approaches. The approach is also spatially explicit and simple and intuitive to interpret, especially for non-scientists; our results thus provide researchers and ecosystem managers (e.g. agricultural policy makers) with key information to design ecosystem management strategies that promote a balanced bundle of ES. Unlabelled Image • We analyzed the relationships between ecosystem services (ES) in agroecosystems. • We implemented a multivariate regression tree (MRT)-based approach. • We show a synergy between regulating ES and trade-off with agricultural production. • Cover crops and soil properties can drive a trade-off between two water-related ES. • Our results provide key information to design ecosystem management strategies. [ABSTRACT FROM AUTHOR]

Steppe soils are important biological sinks for atmospheric methane (CH 4), but the strength of CH 4 uptake remains uncertain due to large spatiotemporal variation and the lack of in situ measurements at regional scale. Here, we report the seasonal and spatial patterns of CH 4 uptake across a 1200 km transect in arid and semi-arid steppe ecosystems in Inner Mongolia, ranging from meadow steppe in the east plain to typical and desert steppes on the west plateau. In general, seasonal patterns of CH 4 uptake were site specific, with unimodal seasonal curves in meadow and typical steppes and a decreasing seasonal trend in desert steppe. Soil moisture was the dominant factor explaining the seasonal patterns of CH 4 uptake, and CH 4 uptake rate decreased with an increase in soil moisture. Across the transect, CH 4 uptake showed a skewed unimodal spatial pattern, with the peak rate observed in the typical steppe sites and with generally higher uptake rates in the west plateau than in the east plain. Soil moisture, together with soil temperature, soil total carbon, and aboveground plant biomass, were the main drivers of the regional patterns of CH 4 uptake rate. These findings are important for model development to more precisely estimate the soil CH 4 sink capacity in arid and semi-arid regions. Unlabelled Image • Seasonal patterns of CH 4 uptake varied among Inner Mongolia steppe types. • Typical steppe showed the highest CH 4 uptake rates across a regional climate transect. • Soil moisture was the most dominant factor influencing the spatio-temporal patterns of CH 4 uptake. [ABSTRACT FROM AUTHOR]

Understanding soil moisture spatiotemporal variability at regional scales is of great importance for studying various ecohydrological and land surface processes. In this study, long-term soil moisture data (5 years) were obtained from three regional monitoring networks across the continental United States with contrasting climatic conditions, including the Enviro-weather Automated Weather Station Network in Michigan, the Nebraska Mesonet, and the Soil Climate Analysis Network in Utah. Both soil moisture spatial variance and temporal variance were decomposed into time-invariant and time-variant components. To evaluate the impacts of different environmental factors on soil moisture spatiotemporal variability and its contribution components, static (e.g., soil properties) and non-static (e.g., climatic variables) environmental factors were also compiled for the stations of each network. The results showed that the time-invariant component was the leading factor for controlling the soil moisture spatial variance in all study regions with marked seasonal variations due to changes in soil moisture wetness conditions. More importantly, the soil moisture spatial variance and its contribution components (in absolute values and relative contributions) were shown to be affected by both soil properties (e.g., soil texture) and climatic conditions (e.g., precipitation) with varying degrees of impacts among the study regions. Meanwhile, the results further revealed that depending on the region under consideration, static and non-static environmental factors could play important roles in determining soil moisture temporal dynamics and its contribution components at regional scales. Overall, this study provided additional observational evidence, which underscored the importance of local factors (e.g., soil properties) in determining soil moisture spatiotemporal variability at regional scales. Unlabelled Image • Spatial variance and temporal variance are decomposed into time-invariant and -variant components. • Time-invariant component controls regional soil moisture spatial variability. • Soil properties and climatic conditions affect time-variant components. • Static and non-static factors determine soil moisture temporal dynamics at regional scales. [ABSTRACT FROM AUTHOR]