Contributions of Nature-Based Solutions to Reduce Peoples’ Vulnerabilities to Climate Change across the Rural Global South

Nature-based solutions (NbS) —i.e. working with and enhancing nature to address societal challenges— feature with increasing prominence in responses to climate change, including in the adaptation plans of the most vulnerable nations. Although evidence for the effectiveness of NbS for adaptation is growing, there is less evidence on whether and how NbS reduce vulnerability to climate change in the Global South, despite this region being home to most of the world’s most climate-vulnerable people. To address this, we analysed the vulnerability-reduction outcomes of 85 nature-based interventions in rural areas across the Global South, and factors mediating their effectiveness, based on a systematic map of peer-reviewed studies encompassing a wide diversity of ecosystems, climate impacts, intervention types and institutions. We applied an analytical framework based on social-ecological systems and climate change vulnerability, coding studies with respect to six pathways of vulnerability reduction: social and ecological exposure, sensitivity, and adaptive capacity. We find widespread effectiveness of NbS in the dataset with 95% providing positive outcomes for climate change adaptation. Overall, nature-based interventions reduced vulnerability primarily by lowering ecosystem sensitivity to climate impacts (73% of interventions), followed by reducing social sensitivity (43%), reducing ecological exposure (37%), and/or increasing social adaptive capacity (34%), ecological adaptive capacity (18%) and reducing social exposure (12%). With an analysis of mediating factors, we show that vulnerability-reduction effectiveness was affected as much by social and political factors as by technical considerations. Indeed configurations of existing and introduced formal and informal institutions appear central to the efficacy and distributive effects of the studied interventions. We conclude that attention to the distinct pathways through which vulnerability is reduced can help maximise the benefits of NbS and that to be successful, careful consideration is required on their applicability to particular circumstances as well as their social dimensions.

Root foraging alters global patterns of ecosystem legacy from climate perturbations

The response of terrestrial ecosystems to climate perturbations typically persist longer than the timescale of the forcing, a phenomenon that is broadly referred to as ecosystem legacy. Understanding the strength of legacy is critical for predicting ecosystem sensitivity to climate extremes and the extent to which persistent changes in land surface-atmosphere exchange might feedback onto the climate, for example, extending drought. The cause of ecosystem legacy has been tied to numerous factors such as changes in leaf area index, however, few studies have tested how changes in root profiles in response to stress might alter an ecosystem's recovery time. We utilize an Earth System Model that includes a dynamic root module where vegetation can forage for water and nutrients by altering their root profiles. As expected, the simulations show that in response to water stress events most ecosystems deepen their root profiles. In semi-arid ecosystems, this response expedites recovery (i.e. less legacy) relative to simulations without dynamics roots because access to deeper water pools after the initial event remains favorable. In wetter ecosystems, the development of deeper root profiles slows down the recovery timescale (i.e. more legacy) because the deeper root profile reduces access to nutrients. The recovery of hyperarid systems is also delayed presumably to the loss of shallow roots and ability to access water from smaller rain events. The results show that the response of root profiles to external forcing is a critical component of global patterns of legacy that is not typically represented in Earth System Models.

Period doubling as an indicator for ecosystem sensitivity to climate extremes

The predictions for a warmer and drier climate and for increased likelihood of climate extremes raise high concerns about the possible collapse of dryland ecosystems, and about the formation of new drylands where native species are less tolerant to water stress. Using a dryland-vegetation model for plant species that display different tradeoffs between fast growth and tolerance to droughts, we find that ecosystems subjected to strong seasonal variability, typical for drylands, exhibit a temporal period-doubling route to chaos that results in early collapse to bare soil. We further find that fast-growing plants go through period doubling sooner and span wider chaotic ranges than stress-tolerant plants. We propose the detection of period-doubling signatures in power spectra as early indicators of ecosystem collapse that outperform existing indicators in their ability to warn against climate extremes and capture the heightened vulnerability of newly-formed drylands. The proposed indicator is expected to apply to other types of ecosystems, such as consumer–resource and predator–prey systems. We conclude by delineating the conditions ecosystems should meet in order for the proposed indicator to apply.

Unifying ecosystem resistance, resilience, and recovery from extreme stress into a single statistical framework

Natural communities and ecosystems are currently experiencing unprecedented rates of environmental and biotic change. While gradual shifts in average conditions, such as rising mean air temperatures, can significantly alter ecosystem function, ecologists recently acknowledged that the most damaging consequences of global change will probably emanate from both a higher prevalence and increased intensity of extreme climatic stress events. Given the potential ecological and societal ramifications of more frequent disturbances, it is imperative that we identify which ecosystems are most vulnerable to global change by accurately quantifying ecosystem responses to extreme stress. Unfortunately, the lack of a standardized method for estimating ecosystem sensitivity to drought makes drawing general conclusions difficult. There is a need for estimates of resistance/resilience/legacy effects that are free of observation error, not biased by stochasticity in production or rainfall, and standardizes stress magnitude among many disparate ecosystems relative to normal interannual variability. Here, I propose a statistical framework that estimates all three components of ecosystem response to stress using standardized language (resistance, resilience, recovery, and legacy effects) while resolving all of the issues described above. Coupling autoregressive time series with exogenous predictors (ARX) models with impulse response functions (IRFs) allows researchers to statistically subject all ecosystems to similar levels of stress, estimate legacy effects, and obtain a standardized estimate of ecosystem resistance and resilience to drought free from observation error and stochastic processes inherent in raw data. This method will enable researchers to rigorously compare resistance and resilience among locations using long-term time series, thereby improving our knowledge of ecosystem responses to extreme stress.