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AbstractCities are natural laboratories for studying vegetation responses to global environmental changes because of their climate, atmospheric, and biogeochemical conditions. However, few holistic studies have been conducted on the impact of urbanization on vegetation growth. We decomposed the overall impacts of urbanization on vegetation growth into direct (replacement of original land surfaces by impervious built‐up) and indirect (urban environments) components, using a conceptual framework and remotely sensed data for 377 metropolitan statistical areas (MSAs) in the conterminous United States (CONUS) in 2001, 2006, and 2011. Results showed that urban pixels are often greener than expected given the amount of paved surface they contain. The vegetation growth enhancement due to indirect effects occurred in 88.4%, 90.8%, and 92.9% of urban bins in 2001, 2006, and 2011, respectively. By defining offset value as the ratio of the absolute indirect and direct impact, we obtained that growth enhancement due to indirect effects compensated for about 29.2%, 29.5%, and 31.0% of the reduced productivity due to loss of vegetated surface area on average in 2001, 2006, and 2011, respectively. Vegetation growth responses to urbanization showed little temporal variation but large regional differences with higher offset value in the western CONUS than in the eastern CONUS. Our study highlights the prevalence of vegetation growth enhancement in urban environments and the necessity of differentiating various impacts of urbanization on vegetation growth, and calls for tailored field experiments to understand the relative contributions of various driving forces to vegetation growth and predict vegetation responses to future global change using cities as harbingers.

AbstractUrban land‐use change has the potential to affect local to global biogeochemical carbon (C) and nitrogen (N) cycles and associated greenhouse gas (GHG) fluxes. We conducted a meta‐analysis to (1) assess the effects of urbanization‐induced land‐use conversion on soil nitrous oxide (N2O) and methane (CH4) fluxes, (2) quantify direct N2O emission factors (EFd) of fertilized urban soils used, for example, as lawns or forests, and (3) identify the key drivers leading to flux changes associated with urbanization. On average, urbanization increases soil N2O emissions by 153%, to 3.0 kg N ha−1 year−1, while rates of soil CH4 uptake are reduced by 50%, to 2.0 kg C ha−1 year−1. The global mean annual N2O EFd of fertilized lawns and urban forests is 1.4%, suggesting that urban soils can be regional hotspots of N2O emissions. On a global basis, conversion of land to urban greenspaces has increased soil N2O emission by 0.46 Tg N2O‐N year−1 and decreased soil CH4 uptake by 0.58 Tg CH4‐C year−1. Urbanization driven changes in soil N2O emission and CH4 uptake are associated with changes in soil properties (bulk density, pH, total N content, and C/N ratio), increased temperature, and management practices, especially fertilizer use. Overall, our meta‐analysis shows that urbanization increases soil N2O emissions and reduces the role of soils as a sink for atmospheric CH4. These effects can be mitigated by avoiding soil compaction, reducing fertilization of lawns, and by restoring native ecosystems in urban landscapes.

AbstractThe joint and relative effects of future land‐use and climate change on fire occurrence in the Amazon, as well its seasonal variation, are still poorly understood, despite its recognized importance. Using the maximum entropy method (MaxEnt), we combined regional land‐use projections and climatic data from the CMIP5 multimodel ensemble to investigate the monthly probability of fire occurrence in the mid (2041–2070) and late (2071–2100) 21st century in the Brazilian Amazon. We found striking spatial variation in the fire relative probability (FRP) change along the months, with October showing the highest overall change. Considering climate only, the area with FRP ≥ 0.3 (a threshold chosen based on the literature) in October increases 6.9% by 2071–2100 compared to the baseline period under the representative concentration pathway (RCP) 4.5 and 27.7% under the RCP 8.5. The best‐case land‐use scenario (“Sustainability”) alone causes a 10.6% increase in the area with FRP ≥ 0.3, while the worse‐case land‐use scenario (“Fragmentation”) causes a 73.2% increase. The optimistic climate‐land‐use projection (Sustainability and RCP 4.5) causes a 21.3% increase in the area with FRP ≥ 0.3 in October by 2071–2100 compared to the baseline period. In contrast, the most pessimistic climate‐land‐use projection (Fragmentation and RCP 8.5) causes a widespread increase in FRP (113.5% increase in the area with FRP ≥ 0.3), and prolongs the fire season, displacing its peak. Combining the Sustainability land‐use and RCP 8.5 scenarios causes a 39.1% increase in the area with FRP ≥ 0.3. We conclude that avoiding the regress on land‐use governance in the Brazilian Amazon (i.e., decrease in the extension and level of conservation of the protected areas, reduced environmental laws enforcement, extensive road paving, and increased deforestation) would substantially mitigate the effects of climate change on fire probability, even under the most pessimistic RCP 8.5 scenario.

AbstractRising temperature shifts plant phenology. Chinese cities, experiencing extensive expansion and intensive warming, spanning a wide latitudinal range, might provide ideal experimental opportunities for observing and predicting phenological responses to warming temperature. Using the urban–rural gradient approach, we explored urbanization imprint on land surface phenology across the entire urbanization intensity (UI) gradient ranging from 0% to 100% in 343 Chinese cities using the VIIRS Land Surface Phenology along with MODIS Land Surface Temperature (LST) products. We found prevalent advancing and delaying trends for the start of the growing season (SOS) and the end of the growing season (EOS) with increasing UI across 343 Chinese cities, respectively. Overall, the phenology shifted earlier by 8.6 ± 0.54 days for SOS, later by 1.3 ± 0.51 days for EOS, and lengthened by 9.9 ± 0.77 days for the growing season length (GSL) in urban core areas (UI above 50%) relative to their rural counterparts (UI lower than 1%). The temperature sensitivity of SOS and EOS was 10.5 ± 0.25 days earlier and 2.9 ± 0.16 days later per 1°C LST increase in spring and autumn, respectively. Moreover, the northern cities witnessed higher temperature sensitivity for SOS and EOS than the southern ones. Both spring and autumn temperature sensitivity across these 343 cities would likely decrease with future urban warming, suggesting any projections of future phenological responses to continued warming must be approached with caution.

AbstractLand‐use change (LUC) in Brazil has important implications on global climate change, ecosystem services and biodiversity, and agricultural expansion plays a critical role in this process. Concerns over these issues have led to the need for estimating the magnitude and impacts associated with that, which are increasingly reported in the environmental assessment of products. Currently, there is an extensive debate on which methods are more appropriate for estimating LUC and related emissions and regionalized estimates are lacking for Brazil, which is a world leader in agricultural production (e.g. food, fibres and bioenergy). We developed a method for estimating scenarios of past 20‐year LUC and derived CO2 emission rates associated with 64 crops, pasture and forestry in Brazil as whole and in each of its 27 states, based on time‐series statistics and in accordance with most used carbon‐footprinting standards. The scenarios adopted provide a range between minimum and maximum rates of CO2 emissions from LUC according to different possibilities of land‐use transitions, which can have large impacts in the results. Specificities of Brazil, like multiple cropping and highly heterogeneous carbon stocks, are also addressed. The highest CO2 emission rates are observed in the Amazon biome states and crops with the highest rates are those that have undergone expansion in this region. Some states and crops showing large agricultural areas have low emissions associated, especially in southern and eastern Brazil. Native carbon stocks and time of agricultural expansion are the most decisive factors to the patterns of emissions. Some implications on LUC estimation methods and standards and on agri‐environmental policies are discussed.

AbstractFragmented tropical forest landscapes preserve much of the remaining biodiversity and carbon stocks. Climate change is expected to intensify droughts and increase fire hazard and fire intensities, thereby causing habitat deterioration, and losses of biodiversity and carbon stock losses. Understanding the trajectories that these landscapes may follow under increased climate pressure is imperative for establishing strategies for conservation of biodiversity and ecosystem services. Here, we used a quantitative predictive modelling approach to project the spatial distribution of the aboveground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest (AF) domain. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report. Our AGB models had a satisfactory performance (area under the curve > 0.75 and p value < .05). The models projected a significant increase of 8.5% in the total carbon stock. Overall, the projections indicated that 76.9% of the AF domain would have suitable climatic conditions for increasing biomass by 2100 considering the RCP 4.5 scenario, in the absence of deforestation. Of the existing forest fragments, 34.7% are projected to increase their AGB, while 2.6% are projected to have their AGB reduced by 2100. The regions likely to lose most AGB—up to 40% compared to the baseline—are found between latitudes 13° and 20° south. Overall, although climate change effects on AGB vary latitudinally for the 2071–2100 period under the RCP 4.5 scenario, our model indicates that AGB stocks can potentially increase across a large fraction of the AF. The patterns found here are recommended to be taken into consideration during the planning of restoration efforts, as part of climate change mitigation strategies in the AF and elsewhere in Brazil.

AbstractApproximately 17% of the land worldwide is considered highly vulnerable to non‐native plant invasion, which can dramatically alter nutrient cycles and influence greenhouse gas (GHG) emissions in terrestrial and wetland ecosystems. However, a systematic investigation of the impact of non‐native plant invasion on GHG dynamics at a global scale has not yet been conducted, making it impossible to predict the exact biological feedback of non‐native plant invasion to global climate change. Here, we compiled 273 paired observational cases from 94 peer‐reviewed articles to evaluate the effects of plant invasion on GHG emissions and to identify the associated key drivers. Non‐native plant invasion significantly increased methane (CH4) emissions from 129 kg CH4 ha−1 year−1 in natural wetlands to 217 kg CH4 ha−1 year−1 in invaded wetlands. Plant invasion showed a significant tendency to increase CH4 uptakes from 2.95 to 3.64 kg CH4 ha−1 year−1 in terrestrial ecosystems. Invasive plant species also significantly increased nitrous oxide (N2O) emissions in grasslands from an average of 0.76 kg N2O ha−1 year−1 in native sites to 1.35 kg N2O ha−1 year−1 but did not affect N2O emissions in forests or wetlands. Soil organic carbon, mean annual air temperature (MAT), and nitrogenous deposition (N_DEP) were the key factors responsible for the changes in wetland CH4 emissions due to plant invasion. The responses of terrestrial CH4 uptake rates to plant invasion were mainly driven by MAT, soil NH4+, and soil moisture. Soil NO3−, mean annual precipitation, and N_DEP affected terrestrial N2O emissions in response to plant invasion. Our meta‐analysis not only sheds light on the stimulatory effects of plant invasion on GHG emissions from wetland and terrestrial ecosystems but also improves our current understanding of the mechanisms underlying the responses of GHG emissions to plant invasion.

AbstractIncreasing frequency of extreme climate events is likely to impose increased stress on ecosystems and to jeopardize the services that ecosystems provide. Therefore, it is of major importance to assess the effects of extreme climate events on the temporal stability (i.e., the resistance, the resilience, and the variance) of ecosystem properties. Most time series of ecosystem properties are, however, affected by varying data characteristics, uncertainties, and noise, which complicate the comparison of ecosystem stability metrics (ESMs) between locations. Therefore, there is a strong need for a more comprehensive understanding regarding the reliability of stability metrics and how they can be used to compare ecosystem stability globally. The objective of this study was to evaluate the performance of temporal ESMs based on time series of the Moderate Resolution Imaging Spectroradiometer derived Normalized Difference Vegetation Index of 15 global land‐cover types. We provide a framework (i) to assess the reliability of ESMs in function of data characteristics, uncertainties and noise and (ii) to integrate reliability estimates in future global ecosystem stability studies against climate disturbances. The performance of our framework was tested through (i) a global ecosystem comparison and (ii) an comparison of ecosystem stability in response to the 2003 drought. The results show the influence of data quality on the accuracy of ecosystem stability. White noise, biased noise, and trends have a stronger effect on the accuracy of stability metrics than the length of the time series, temporal resolution, or amount of missing values. Moreover, we demonstrate the importance of integrating reliability estimates to interpret stability metrics within confidence limits. Based on these confidence limits, other studies dealing with specific ecosystem types or locations can be put into context, and a more reliable assessment of ecosystem stability against environmental disturbances can be obtained.