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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.

This study highlights how Chinese economic development detrimentally impacted water quality in recent decades and how this has been improved by enormous investment in environmental remediation funded by the Chinese government. To our knowledge, this study is the first to describe the variability of surface water quality in inland waters in China, the affecting drivers behind the changes, and how the government-financed conservation actions have impacted water quality. Water quality was found to be poorest in the North and the Northeast China Plain where there is greater coverage of developed land (cities + cropland), a higher gross domestic product (GDP), and higher population density. There are significant positive relationships between the concentration of the annual mean chemical oxygen demand (COD) and the percentage of developed land use (cities + cropland), GDP, and population density in the individual watersheds (p < 0.001). During the past decade, following Chinese government-financed investments in environmental restoration and reforestation, the water quality of Chinese inland waters has improved markedly, which is particularly evident from the significant and exponentially decreasing GDP-normalized COD and ammonium (NH4+-N) concentrations. It is evident that the increasing GDP in China over the past decade did not occur at the continued expense of its inland water ecosystems. This offers hope for the future, also for other industrializing countries, that with appropriate environmental investments a high GDP can be reached and maintained, while simultaneously preserving inland aquatic ecosystems, particularly through management of sewage discharge.

Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

We introduce a field survey method to assess the conservation condition of landscapes. Using a popular rapid assessment format, this study defines observable “stressed states” identified through the use of general metrics to gauge landscape degradation. Fifteen metrics within six thematic categories were selected through a literature review and extensive field trials. Field tests on the Greek island of Samothraki show a strong correlation between a single expert’s scores and five assessor’s scores at 35 landscape sites. Only three of the metrics did not maintain a high consistency among assessors; however, this is explained by the difficulty of interpreting certain anthropogenic stressors (such as livestock grazing) in Mediterranean semi-natural landscapes with culturally-modified vegetation patterns. The protocol and proposed index, with five conservation condition classes, identified areas of excellent and good quality, and reliably distinguished the most degraded landscape conditions on the island. Uncertainties and difficulties of the index are investigated, and further research and validation are proposed. The protocol effectively goes beyond a traditional visual aesthetic assessment; it can be used both by experts and non-scientists as a conservation-relevant multi-disciplinary procedure to support a holistic landscape diagnosis. The combination of an on-site experiential survey and its simple integrative format may be useful as a screening-level index, and for promoting local participation, landscape literacy and educational initiatives.

Research to date has suggested that both individual marine species and ecological processes are expected to exhibit diverse responses to the environmental effects of climate change. Evolutionary responses can occur on rapid (ecological) timescales, and yet studies typically do not consider the role that adaptive evolution will play in modulating biological responses to climate change. Investigations into such responses have typically been focused at particular biological levels (e.g., cellular, population, community), often lacking interactions among levels. Since all levels of biological organisation are sensitive to global climate change, there is a need to elucidate how different processes and hierarchical interactions will influence species fitness. Therefore, predicting the responses of communities and populations to global change will require multidisciplinary efforts across multiple levels of hierarchy, from the genetic and cellular to communities and ecosystems. Eventually, this may allow us to establish the role that acclimatisation and adaptation will play in determining marine community structures in future scenarios.