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1. Marine Heat Waves (MHWs) are episodes of anomalous warming in the ocean that can last from a few days to months. MHWs have different characteristics in terms of intensity, duration, and frequency and generate thermal stress on marine ecosystems. In reef ecosystems, they are one of the main causes of decreased presence and abundance of corals, invertebrates, and fish. The deleterious capacity of thermal stress often depends upon biotic factors such as resource availability (bottom-up control on predators) and predation (top-down control on prey). Despite the evidence of thermal stress and biotic factors affecting individual species, the combined effects of both stressors on the entire reef ecosystems are far less studied. 2. Here, using a food-web modeling approach, we estimated the rate of change in species’ biomass due to different MHW scenarios based on their physical characteristics. Specifically, we modeled the mechanistic link between species’ consumption rate and seawater temperature (thermal stressor), simulating species’ biomass dynamics for different MHW scenarios under different trophic control assumptions (biotic factor). 3. We find that total reef ecosystem biomass declined by 10% ± 5% under MHWs with severe intensity and top-down control assumption. The bottom-up control assumption moderates the total ecosystem biomass reduction by 5% ± 5%. Irrespective of the MHW scenario and the trophic control assumption, the most substantial biomass changes occur among top, meso-predators, and corals (5% to 20% ± 10%).4. Since habitat degradation may lead to reef ecosystems governed by top-down control on prey, our findings point to the critical importance of protecting reef ecosystems as a pivotal strategy to alleviate the impacts of thermal stress induced by MHWs. Overall, our results provide a unified understanding of the interplay between abiotic stressors and biotic factors in reef ecosystems under extreme thermal events, offering insights into present baselines and future ecological states for reef ecosystems.

In this paper, we address the trade-off between primary energy consumption and battery wear for hybrid electric vehicles in an optimal manner, for which we provide three contributions: First, we suggest a control structure to track a battery lifetime target in a closed control loop by incorporating periodic measurements of the state of health. This feedback enables the energy management system to reliably meet the target lifetime in the presence of disturbances and model mismatch. We validate the control scheme in a case study featuring a battery-assisted trolley bus. In this case study, we show that without the proposed measurement feedback and in the presence of disturbances and model mismatch, the sub-optimal use of the battery can either result in an increase in energy consumption of up to 9% over the vehicle's lifetime or in a prematurely required battery replacement. Second, to speed up the necessary calculations, we devise an algorithm that is able to perform simulations of a complete vehicle lifetime in less than a minute. A comparison to a standard simulation approach shows that our approach is able to accurately calculate both energy consumption and battery degradation with an error of less than 1% on average, while the execution time is reduced by a factor of about 70000. Third, we numerically optimize the battery health trajectory over the vehicle lifetime. We show that, while a quadratic health trajectory leads to improved energy efficiency, for the specific vehicle and cell technology considered in our case study, a linear trajectory results in only a small energy penalty of 0.05% over the vehicle lifetime. eTransportation, 17 ISSN:2590-1168

Natural ecosystems are threatened by climate change, fragmentation, and non-native species. Dispersal-limitation potentially compounds impacts of these factors on plant diversity, especially in isolated vegetation patches. Changes in climate can impact the phenology of native species in distinct ways from non-natives, potentially resulting in cascading impacts on native communities. Few empirical studies have examined the combined effects of climate change and dispersal limitation on community diversity or phenology. Using a five-year dispersal-restriction experiment in an invaded semi-arid annual plant system in Western Australia, we investigated the interactive effects of dispersal-restriction and inter-annual rainfall variation on community composition, species dominance and seed production timing. We found inter-annual rainfall variation to be the principal driver of community dynamics. Drought years had long-term, stable effects on community composition, with evidence of shifts from native toward non-native dominance. Surprisingly, community composition remained largely unchanged under dispersal restriction. A subtle dispersal rescue effect was evident for a dominant native annual forb and a dominant annual non-native grass but only in average rainfall years. The timing of seed production was primarily driven by annual rainfall with native and non-native grasses having opposite responses. There was no evidence that inter-annual variation in seeding timing affected community diversity over time. Our study demonstrates that dispersal is not a major factor in driving community diversity in this invaded, semi-arid system. Results do suggest, however, that increases in drought frequency likely benefit non-native species over natives in the long term. Climate Change Ecology, 2 ISSN:2666-9005

Semiconductor quantum dot arrays defined electrostatically in a 2D electron gas provide a scalable platform for quantum information processing and quantum simulations. For the operation of quantum dot arrays, appropriate voltages need to be applied to the gate electrodes that define the quantum dot potential landscape. Tuning the gate voltages has proven to be a time-consuming task, because of initial electrostatic disorder and capacitive cross-talk effects. Here, we report on the automated tuning of the inter-dot tunnel coupling in gate-defined semiconductor double quantum dots. The automation of the tuning of the inter-dot tunnel coupling is the next step forward in scalable and efficient control of larger quantum dot arrays. This work greatly reduces the effort of tuning semiconductor quantum dots for quantum information processing and quantum simulation.

At present, the synthesis of ammonia through the Haber-Bosch (HB) process accounts for 1.2% of the global carbon emissions, representing roughly one-fourth of the global fossil consumption from the chemical industry, which creates a pressing need for alternative low-carbon synthesis routes. Analyzing seven essential planetary boundaries (PBs) for the safe operation of our planet, we find that the standard HB process is unsustainable as it vastly transgresses the climate change PB. In order to identify more responsible strategies from this integrated perspective, we assess the absolute sustainability level of 34 alternative routes where hydrogen (H-2) is supplied by steam methane reforming with carbon capture and storage, biomass gasification, or water electrolysis powered by various energy sources. We found that some of these scenarios could substantially reduce the global impact of fossil HB, yet alleviating the impact on climate change could critically exacerbate the impacts on other Earth-system processes. Furthermore, we identify that reducing the cost of electrolytic H-2 is the main avenue toward the economic appeal of the most sustainable routes. Our work highlights the need to embrace global impacts beyond climate change in the assessment of decarbonization routes of fossil chemicals. This approach enabled us to identify more suitable alternatives and associated challenges toward environmental and economically attractive ammonia synthesis. ACS Sustainable Chemistry & Engineering, 9 (29) ISSN:2168-0485

Optimization models can support decision-makers in the synthesis and operation of multi-sector energy systems. To identify the optimal design and operation of a low-carbon system, we need to consider high temporal and spatial variability in the electricity supply, sector coupling, and environmental impacts over the whole life cycle. Incorporating such aspects in optimization models is demanding. To avoid redundant research efforts and enhance transparency, the developed models and used data sets should be shared openly. In this work, we present the SecMOD framework for multi-sector energy system optimization incorporating life-cycle assessment (LCA). The framework allows optimizing multiple sectors jointly, ranging from industrial production and their linked energy supply systems to sector-coupled national energy systems. The framework incorporates LCA to account for environmental impacts. We hence provide the first open-source framework to consistently include a holistic life-cycle perspective in multi-sector optimization by a full integration of LCA. We apply the framework to a case-study of the German sector-coupled energy system. Starting with few base technologies, we demonstrate the modular capabilities of SecMOD by the stepwise addition of technologies, sectors and existing infrastructure. Our modular open-source framework SecMOD aims to accelerate research for sustainable energy systems by combining multi-sector energy system optimization and life-cycle assessment.