Aim In order to mitigate the ecological, economical and social consequences of future climate change, we must understand and quantify the response of vegetation to short-term climate anomalies. There is currently no model that quantifies vegetation resistance and resilience at a global scale while simultaneously taking climate variability into account. The goals of this study were therefore to develop a standardized indicator of short-term vegetation resilience and resistance to drought and temperature anomalies, and to improve our understanding of vegetation resistance and resilience in drought-sensitive areas by linking metrics of vegetation stability to the percentage of tree cover, non-tree vegetation and bare soil.Location Global. MethodsThe deviation of vegetation behaviour from expectations was quantified using anomalies in the normalized difference vegetation index (NDVI) and modelled as a function of (1) past NDVI anomalies, (2) an instantaneous drought indicator and (3) temperature anomalies. Metrics of resistance and resilience were then extracted from the model and related to the percentages of bare soil, non-tree vegetation and tree cover. ResultsComparisons of the globally derived resilience and resistance metrics showed low resilience and low resistance to drought in semi-arid areas, low resistance to negative temperature anomalies in high-latitude areas, and low resistance to positive temperature anomalies in the Sahel and Australia. In drought-sensitive areas, resilience was highest for vegetation types with 3-20% bare soil and 5-15% tree cover.Main conclusions Our ARx model is the first to simultaneously derive vegetation resistance and resilience metrics at a global scale, explicitly taking into account the spatial variability of short-term climate anomalies and data reliability. Its results highlight the impact of tree cover, non-tree vegetation and bare soil on vegetation resilience.
T he impacts of climate change and anthropogenic activities on Earth's vegetation and ecosystems have been in the spotlight of science in the past decades  . With increasing climate variability and more frequent occurrences of extreme events expected in the future 7 , research has targeted the sensitivity of ecosystems 8,9 . At the same time, recent studies have shown globally increasing leaf area index (LAI; a proxy for green vegetation cover) 10 , and aboveground biomass carbon (ABC) 11 , also known as the greening Earth 10,12 . Dynamic vegetation models and Earth observation studies reveal climatic and atmospheric changes as the main drivers of large scale increases in LAI 10,13 . On the contrary, the anthropogenic footprint is usually found to cause land degradation and deforestation 13,14 , and only a few studies find a direct positive effect of management on vegetation cover and biomass trends 2,11,15 . Although ecological conservation projects aim at increasing biodiversity, carbon sequestration and vegetation cover 16,17 , the success of such conservation efforts is not easily quantifiable, and the spatial footprint of projects is not always commensurable with contemporary satellite-and modelling-based monitoring methods. Adaptation and mitigation strategies to climate change should be anchored in knowledge on how ecosystems respond to climatic and anthropogenic disturbances, but at present it is not known whether conservation projects impact on the ability of vegetation to alleviate the effects of climate change at large scales.China's ecological restoration projects (for example, the Natural Forest Protection Project, the Grain to Green Project, and the Karst Rocky Desertification Restoration Project) are considered 'megaengineering' activities and the most ambitious afforestation and conservation projects in human history  . The highly sensitive and vulnerable karst ecosystem in southwest China is one of the largest exposed carbonate rock areas (more than 0.54 million km 2 ) in the world. This area hosts 220 million people 20,21 and has been selected as a major target of restoration projects. Descriptions as early as the seventeenth century reported the rocky karst mountains as an area of sparse forest or vegetation cover 22 , and accelerating desertification has been reported during the past half century, caused by the increasing intensity of human exploitation of natural resources  . As a result, approximately 0.13 million km 2 of karst areas previously covered by vegetation and soil were turned into a rocky landscape. To combat this severe form of land degradation and to relieve poverty, more than 130 billion yuan (~19 billion USD) have been invested in mitigation initiatives since the end of the 1990s 24 . The largest programme implemented, the Grain to Green Project, offers grain, cash and free seedlings as compensation for rural households to re-establish forests, shrub and/or grassland 24 . The costs of ecological engineering projects as a clima...
Increasing 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.
1. The stable delivery of ecosystem services provided by grasslands is strongly dependent on the stability of grassland ecosystem functions such as biomass production. Biomass production is in turn strongly affected by the frequency and intensity of climate extremes. The aim of this study is to evaluate to what extent species-poor intensively managed agricultural grasslands can maintain their biomass productivity under climate anomalies, as compared to species-rich, semi-natural grasslands. Our hypothesis is that species richness stabilizes biomass production over time. 2. Biomass production stability was assessed in response to drought and temperature anomalies using 14 years of the Normalized Difference Vegetation Index (NDVI), temperature and drought index time series. More specifically, vegetation resistance (i.e. the ability to withstand the climate anomaly) and resilience (i.e. the recovery rate) were derived using an auto-regressive model with external input variables (ARx). The stability metrics for both grasslands were subsequently compared. 3. We found that semi-natural grasslands exhibited a higher resistance but lower resilience than agricultural grasslands in the Netherlands. Furthermore, the difference in stability between semi-natural and agricultural grasslands was dependent on the physical geography: the most significant differences in resistance were observed in coastal dunes and riverine areas, whereas the differences in resilience were the most significant in coastal dunes and fens. 4. Synthesis and applications. We conclude that semi-natural grasslands show a higher resistance to drought and temperature anomalies compared to agricultural grasslands. These results underline the need to reassess the ways agricultural practices are performed. More specifically, increasing the plant species richness of agricultural grasslands and lowering their mowing and grazing frequency may contribute to buffer their biomass production stability against climate extremes.
Aim: Changes in dryland ecosystem functioning are threatening the well-being of human populations worldwide, and land degradation, exacerbated by climate change, contributes to biodiversity loss and puts pressures on sustainable livelihoods. Here, abrupt changes in ecosystem functioning [so-called turning points (TPs)] were detected using time series of Earth observation data. Hotspot areas of high TP occurrence were identified, observed changes characterized and insights gained on potential drivers for these changes. Location: Arid and semi-arid regions. Time period: 1982-2015.Methods: We used a time series segmentation technique (breaks for additive season and trend) to detect breakpoints in rain-use efficiency as a means of analysing changes in ecosystem functioning. A new typology to characterize the detected changes was proposed and evaluated, at regional to local scales, for a set of case studies. Ancillary data on population and drought were used to provide insights on potential drivers of TP occurrence.Results: Turning points in ecosystem functioning were found in 13.6% (c. 2.1 × 10 6 km 2 ) of global drylands. Turning point hotspots were primarily observed in North America, the Sahel, Central Asia and Australia. In North America, the majority of TPs (62.6%) were characterized by a decreasing trend in ecosystem functioning, whereas for the other regions, a positive reversal in ecosystem functioning was prevalent. Further analysis showed that: (a) both climatic and anthropogenic pressure influenced the occurrence of TPs in North America; (b) Sahelian grasslands were primarily characterized by drought-induced TPs; and (c) high anthropogenic pressure coincided with the occurrence of TPs in Asia and Australia. Main conclusions: By developing a new typology targeting the categorization of abrupt and gradual changes in ecosystem functioning, we detected and characterized TPs in global drylands. This TP characterization is a first crucial step towards understanding the drivers of change and supporting better decision-making for ecosystem conservation and management in drylands. | 1231 BERNARDINO Et Al.
Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.
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