Securing landscape resilience

Abstract

Climate-induced perturbations are expected to increase in frequency and intensity and affect wetlands by altering its hydrology. An essential first step in comprehending the wetland hydrological and ecological resilience to future amplified climatic disturbances in coastal regions and beyond is this work, which enhances the approach for measuring wetland hydrologic resilience at a regional scale. Land use change, natural disturbance and climate change directly alter ecosystem productivity and resilience levels. The estimation of ecological resilience dynamics depends on the quality of land cover change data and the effectiveness of the ecosystem models that represent the vegetation growth processes and disturbance effects. We used different mathematical approaches determining resilience, land cover change data to examine landscape resilience of the ecological networks in terms of scale, and how resilience exhibits as an emergent behaviour in different types of ecosystems, with its influencing factors, suggesting application to other estuarine ecosystems. A set of quantitative metrics was developed including the variations of landscapes, utilizing spectral power to determine changes in autocorrelation and variance as measures of critically slowing down, detecting the early warning signals with the development of the Dynamic Linear Model (DLM). The coastal landscape, which includes coastal-herbaceous wetlands, was then subjected to this method detecting the influence of salinity intrusion on estuarine systems along geographical and temporal salinity gradients. We discovered that an excellent indication of resilience is the multiscale autocorrelation fluctuations of wetlands, affected by the temporal trends in data, under various climatic circumstances. Climate interannual variability was the key driving force for the large interannual changes of ecosystem state level while extreme weather events and drought were the dominant driving forces for resilience balances in several specific ecoregions. There is a need for techniques that may be utilised to recognise when a major transition is about to happen because these events might happen without warning and are challenging to handle. A number of "early warning signals" can be used to determine a system's near to a critical transition, according to recent theory, and successful empirical examples point to the possibility of practical use. Our study provides a practical toolbox that can be applied in a variety of sectors to help identify early warning signs of crucial transitions in time series data, in addition to a methodological guide.