• Energy Research

Abstract Manipulative experiments typically show a decrease in dryland biocrust cover and altered species composition under climate change. Biocrust‐forming lichens, such as the globally distributed Diploschistes diacapsis, are particularly affected and show a decrease in cover with simulated climate change. However, the underlying mechanisms are not fully understood, and long‐term interacting effects of different drivers are largely unknown due to the short‐term nature of the experimental studies conducted so far. We addressed this gap and successfully parameterised a process‐based model for D. diacapsis to quantify how changing atmospheric CO2, temperature, rainfall amount and relative humidity affect its photosynthetic activity and cover. We also mimicked a long‐term manipulative climate change experiment to understand the mechanisms underlying observed patterns in the field. The model reproduced observed experimental findings: warming reduced lichen cover, whereas less rainfall had no effect on lichen performance. This warming effect was caused by the associated decrease in relative humidity and non‐rainfall water inputs, which are major water sources for biocrust‐forming lichens. Warming alone, however, increased cover because higher temperatures promoted photosynthesis during early morning hours with high lichen activity. When combined, climate variables showed non‐additive effects on lichen cover, and effects of increased CO2 levelled off with decreasing levels of relative humidity. Synthesis. Our results show that a decrease in relative humidity, rather than an increase in temperature, may be the key factor for the survival of the lichen D. diacapsis under climate change and that effects of increased CO2 levels might be offset by a reduction in non‐rainfall water inputs in the future. Because of a global trend towards warmer and drier air and the widespread global distribution of D. diacapsis, this will affect lichen‐dominated dryland biocrust communities and their role in regulating ecosystem functions worldwide.

AbstractDrylands occur worldwide and are particularly vulnerable to climate change because dryland ecosystems depend directly on soil water availability that may become increasingly limited as temperatures rise. Climate change will both directly impact soil water availability and change plant biomass, with resulting indirect feedbacks on soil moisture. Thus, the net impact of direct and indirect climate change effects on soil moisture requires better understanding. We used the ecohydrological simulation model SOILWAT at sites from temperate dryland ecosystems around the globe to disentangle the contributions of direct climate change effects and of additional indirect, climate change‐induced changes in vegetation on soil water availability. We simulated current and future climate conditions projected by 16 GCMs under RCP 4.5 and RCP 8.5 for the end of the century. We determined shifts in water availability due to climate change alone and due to combined changes of climate and the growth form and biomass of vegetation. Vegetation change will mostly exacerbate low soil water availability in regions already expected to suffer from negative direct impacts of climate change (with the two RCP scenarios giving us qualitatively similar effects). By contrast, in regions that will likely experience increased water availability due to climate change alone, vegetation changes will counteract these increases due to increased water losses by interception. In only a small minority of locations, climate change‐induced vegetation changes may lead to a net increase in water availability. These results suggest that changes in vegetation in response to climate change may exacerbate drought conditions and may dampen the effects of increased precipitation, that is, leading to more ecological droughts despite higher precipitation in some regions. Our results underscore the value of considering indirect effects of climate change on vegetation when assessing future soil moisture conditions in water‐limited ecosystems.

Abstract Savannas are characterized by water scarcity and degradation, making them highly vulnerable to increased uncertainties in water availability resulting from climate change. This poses a significant threat to ecosystem services and rural livelihoods that depend on them. In addition, the lack of consensus among climate models on precipitation change makes it difficult for land managers to plan for the future. Therefore, Savanna rangeland management needs to develop strategies that can sustain Savanna resilience and avoid tipping points under an uncertain future climate. Our study aims to analyse the impacts of climate change and rangeland management on degradation in Savanna ecosystems of southern Africa, providing insights for the management of semi‐arid Savannas under uncertain conditions worldwide. To achieve this, we simulated the effects of projected changes in temperature and precipitation, as predicted by 10 global climate models, on water resources and vegetation (cover, functional diversity, tipping points (transition from grass‐dominated to shrub‐dominated vegetation)). We simulated three different rangeland management options (herbivore communities dominated by grazers, by browser and by mixed feeders), each with low and high animal densities, using the ecohydrological model EcoHyD. Our results identified intensive grazing as the primary contributor to the increased risk of degradation in response to changing climatic conditions across all climate change scenarios. This degradation encompassed a reduction in available water for plant growth within the context of predicted climate change. It also entails a decline in the overall vegetation cover, the loss of functionally important plant species and the inefficient utilization of available water resources, leading to earlier tipping points. Synthesis and applications. Our findings underscore that, in the face of climate uncertainty, farmers' most effective strategy for securing their livelihoods and ecosystem stability is to integrate browsers and apply management of mixed herbivore communities. This management approach not only significantly delays or averts tipping points but also maintained greater plant functional diversity, fostering a more robust and resilient ecosystem that acts as a vital buffer against adverse climatic conditions.

The biodiversity-productivity relationship is critical for better predicting ecosystem responses to climate change and human disturbance. However, it remains unclear about the effects of climate change, land use shifts, plant diversity, and their interactions on productivity partitioning above- and below-ground components in alpine grasslands on the Tibetan Plateau. To answer this question, we conducted field surveys at 33 grazed vs. fenced paired sites that are distributed across the alpine meadow, steppe, and desert-steppe zones on the northern Tibetan Plateau in early August of 2010-2013. Generalized additive models (GAMs) showed that aboveground net primary productivity (ANPP) linearly increased with growing season precipitation (GSP) while belowground net primary productivity (BNPP) decreased with growing season temperature (GST). Compared to grazed sites, short-term fencing did not alter the patterns of ANPP along climatic gradients but tended to decrease BNPP at moderate precipitation levels of 200 mm < GSP <450 mm. We also found that ANPP and BNPP linearly increased with species richness, ANPP decreased with Shannon diversity index, and BNPP did not correlate with the Shannon diversity index. Fencing did not alter the relationships between productivity components and plant diversity indices. Generalized additive mixed models furtherly confirmed that the interaction of localized plant diversity and climatic condition nonlinearly regulated productivity partitioning of alpine grasslands in this area. Finally, structural equation models (SEMs) revealed the direction and strength of causal links between biotic and abiotic variables within alpine grassland ecosystems. ANPP was controlled directly by GSP (0.53) and indirectly via species richness (0.41) and Shannon index (-0.12). In contrast, BNPP was influenced directly by GST (-0.43) and indirectly by GSP via species richness (0.05) and Shannon index (-0.02). Therefore, we recommend using a joint approach of GAMs and SEMs for better understanding mechanisms behind the relationship between biodiversity and ecosystem function under climate change and human disturbance.

Abstract Ecological restoration increasingly aims at improving ecosystem multifunctionality and making landscapes resilient to future threats, especially in biodiversity hotspots such as Mediterranean‐type ecosystems. Plants and their traits play a major role in the functioning of an ecosystem. Therefore, successful restoration towards long‐term multifunctionality requires a fundamental mechanistic understanding of this link under changing climate. An integrated approach of empirical research and simulation modelling with a focus on plant traits can allow this understanding. Based on empirical data from a large‐scale restoration project in a Mediterranean‐type ecosystem in Western Australia, we developed and validated the spatially explicit simulation model Modelling Ecosystem Functions and Services based on Traits (ModEST), which calculates coupled dynamics of nutrients, water and individual plants characterised by functional traits. We then simulated all possible combinations of eight plant species with different levels of diversity to assess the role of plant diversity and traits on multifunctionality, the provision of six ecosystem functions that can be linked to ecosystem services, as well as trade‐offs and synergies among the functions under current and future climatic conditions. Our results show that multifunctionality cannot fully be achieved because of trade‐offs among functions that are attributable to sets of traits that affect functions differently. Our measure of multifunctionality was increased by higher levels of planted species richness under current, but not future climatic conditions. In contrast, single functions were differently impacted by increased plant diversity and thus the choice and weighting of these functions affected multifunctionality. In addition, we found that trade‐offs and synergies among functions shifted with climate change due to different direct and indirect (mediated via community trait changes) effects of climate change on functions. Synthesis and application. With our simulation model Modelling Ecosystem Functions and Services based on Traits (ModEST), we show that restoration towards multifunctionality might be challenging not only under current conditions but also in the long‐term. However, once ModEST is parameterised and validated for a specific restoration site, managers can assess which target goals can be achieved given the set of available plant species and site‐specific conditions. It can also highlight which species combinations can best achieve long‐term improved multifunctionality due to their trait diversity.

Crop models are crucial in assessing the reliability and sustainability of soil water conservation practices. The AquaCrop model was tested and validated for maize productivity under the selected climate smart agriculture (CSA) practices in the rainfed production systems. The model was validated using final biomass (B) and grain yield (GY) data from field experiments involving seven CSA practices (halfmoon pits, 2 cm thick mulch, 4 cm thick mulch, 6 cm thick mulch, 20 cm deep permanent planting basins (PPB), and 30 cm deep) and the control (conventional practice) where no CSA was applied. Statistics for coefficient of determination (R2), Percent bias (Pbias), and Nash–Sutcliffe (E) for B and GY indicate that the AquaCrop model was robust to predict crop yield and biomass as illustrated by the value of R2 > 0.80, Pbias −1.52–1.25% and E > 0.68 for all the CSA practices studied. The relative changes between the actual and simulated water use efficiency (WUE) of grain yield was observed in most of the CSA practices. However, measured WUE was seemingly better in the 2 cm thick mulch, indicating a potential for water saving and yield improvement. Therefore, the AquaCrop model is recommended as a reliable tool for assessing the effectiveness of the selected CSA practices for sustainable and improved maize production; although, the limitations in severely low soil moisture conditions and water stressed environments should be further investigated considering variations in agroecological zones.

Biodiversity loss and widespread ecosystem degradation are among the most pressing challenges of our time, requiring urgent action. Yet our understanding of their causes remains limited because prevailing ecological concepts and approaches often overlook the underlying complex interactions of individuals of the same or different species, interacting with each other and with their environment. We propose a paradigm shift in ecological science, moving from simplifying frameworks that use species, population or community averages to an integrative approach that recognizes individual organisms as fundamental agents of ecological change. The urgency of the biodiversity crisis requires such a paradigm shift to advance ecology towards a predictive science by elucidating the causal mechanisms linking individual variation and adaptive behaviour to emergent properties of populations, communities, ecosystems, and ecological interactions with human interventions. Recent advances in computational technologies, sensors, and analytical tools now offer unprecedented opportunities to overcome past challenges and lay the foundation for a truly integrated Individual-Based Global Change Ecology (IBGCE). Unravelling the potential role of individual variability in global change impact analyses will require a systematic combination of empirical, experimental and modelling studies across systems, while taking into account multiple drivers of global change and their interactions. Key priorities include refining theoretical frameworks, developing benchmark models and standardized toolsets, and systematically incorporating individual variation and adaptive behaviour into empirical field work, experiments and predictive models. The emerging synergies between individual-based modelling, big data approaches, and machine learning hold great promise for addressing the inherent complexity of ecosystems. Each step in the development of IBGCE must systematically balance the complexity of the individual perspective with parsimony, computational efficiency, and experimental feasibility. IBGCE aims to unravel and predict the dynamics of biodiversity in the Anthropocene through a comprehensive study of individual organisms, their variability and their interactions. It will provide a critical foundation for considering individual variation and behaviour for future conservation and sustainability management, taking into account individual-to-ecosystem pathways and feedbacks.