A model quantifying global vegetation resistance and resilience to short‐term climate anomalies and their relationship with vegetation cover

Abstract

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.

Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

“…First, the relatively large temporal variation in the stability metrics implies that vegetation response should not be considered as a constant, and this variability should be accounted for in vegetation stability assessment. Consequently, methods that are based on techniques implicitly assuming stationarity and using the whole time series period may not be optimal (e.g., [20]). Modification of these techniques, i.e., through the use of a moving window, the allowance of break-points or the explicit inclusion of changing response may be of interest.…”

“…These climatic changes, land cover changes and altered vegetation vulnerabilities are potential causes of non-stationary behavior [26][27][28], as illustrated in Figure 3. Furthermore, the limited and variable water availability, low NDVI saturation and relatively low cloud cover allow one to derive stability metrics with a relatively low model error from NDVI time series [20] and increase the likeliness that non-stationary behavior can be detected. Similarly, the case in (B) illustrates the conversion of forest to cropland, the case in (C) shrubland subjected to a weakening monsoon, the case in (D) conversion from rainfed to irrigated pasture and the case in (E) increased rainfall in arid areas.…”

“…The latter two metrics were obtained by modeling the NDVI anomaly in function of the drought index and the lagged NDVI anomaly, where all time series were standardized [20]. More information about these metrics can be found in Table 1.…”

“…In previous studies [5,20], the utility of stability metrics such as standard deviation of the NDVI anomaly (SD; indicator of variance), autocorrelation at lag one of the NDVI anomaly (AC; indicator of resilience) and the correlation of NDVI anomaly (CS) with SPEI (Standardized Precipitation and Evaporation Index; indicator related to resistance), has been explored at the global scale. These studies indicated the potential of such metrics for detecting changes in vegetation stability and trends in vegetation cover associated with climate and land use change.…”

Water deficit modifies the carbon isotopic composition of lipids, soluble sugars and leaves of Copaifera langsdorffii Desf. (Fabaceae)

“…Drought duration is the main determinant of the diff erent levels of resistance and resilience of tree species to water defi cit (Keersmaecker et al 2015), which aff ect growth (Pasho et al 2011;Lévesque et al 2013) and gain in biomass (Ivits et al 2016). Quantifying the duration, intensity, and spatial extent of water defi cit is a diffi cult task due to the numerous physical variables to be considered (Vicente-Serrano et al 2013).…”