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Abstract The objective of this article is to design and plan sustainable bio-ethanol supply chain. Modeling supply chains that achieve economic, social and environmental feasibility through production, process and energy efficiency is a challenge. Life cycle assessment that is coupled with techno-economic optimization of bio-ethanol supply chain is an alternative solution to achieve sustainability. A simulation of the biomass conversion is used to find process parameters of the conversion technology. The results show that the unified model is capable of minimizing both CO 2 emissions and energy and utility consumptions. In addition, the supply chain is capable of contributing to local economy through jobs creation. While the model is quite comprehensive, the future research recommendation on energy integration and global sustainability is proposed.

Purpose: Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods: Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate (NO 3 − ) or ammonium (NH 4 + ) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results: Trees growing at mRH-NH 4 + had the highest total PA, 9.4 ± 1.9 cm 2 , while the lowest was in trees growing at eRH-NO 3 − , 3.9 ± 0.6 cm 2 . The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions: We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments.

AbstractEnvironments are changing rapidly, and to cope with these changes, organisms have to adapt. Adaptation can take many shapes and occur at different speeds, depending on the type of response, the trait, the population, and the environmental conditions. The biodiversity crisis that we are currently facing illustrates that numerous species and populations are not capable of adapting with sufficient speed to ongoing environmental changes. Here, we discuss current knowledge on the ability of animals and plants to adapt to environmental stress on different timescales, mainly focusing on thermal stress and ectotherms. We discuss within‐generation responses that can be fast and induced within minutes or hours, evolutionary adaptations that are often slow and take several generations, and mechanisms that lay somewhere in between and that include epigenetic transgenerational effects. To understand and predict the impacts of environmental change and stress on biodiversity, we suggest that future studies should (1) have an increased focus on understanding the type and speed of responses to fast environmental changes; (2) focus on the importance of environmental fluctuations and the predictability of environmental conditions on adaptive capabilities, preferably in field studies encompassing several fitness components; and (3) look at ecosystem responses to environmental stress and their resilience when disturbed.

With the global change in climate, the Arctic has been pinpointed as the region experiencing the fastest rates of change. As a result, Arctic biological responses-such as shifts in phenology-are expected to outpace those at lower latitudes. 15 years ago, a decade-long dataset from Zackenberg in High Arctic Greenland revealed rapid rates of phenological change.1 To explore how the timing of spring phenology has developed since, we revisit the Zackenberg time series on flowering plants, arthropods, and birds. Drawing on the full 25-year period of 1996-2020, we find little directional change in the timing of events despite ongoing climatic change. We attribute this finding to a shift in the temporal patterns of climate conditions, from previous directional change to current high inter-annual variability. Additionally, some taxa appear to have reached the limits of their phenological responses, resulting in a leveling off in their phenological responses in warm years. Our findings demonstrate the importance of long-term monitoring of taxa from across trophic levels within the community, allowing for detecting shifts in sensitivities and responses and thus for updated inference in the light of added information.

AbstractAimTo evaluate the relative importance of climatic versus soil data when predicting species distributions for Amazonian plants and to gain understanding of potential range shifts under climate change.LocationAmazon rain forest.MethodsWe produced species distribution models (SDM) at 5‐km spatial resolution for 42 plant species (trees, palms, lianas, monocot herbs and ferns) using species occurrence data from herbarium records and plot‐based inventories. We modelled species distribution with Bayesian logistic regression using either climate data only, soil data only or climate and soil data together to estimate their relative predictive powers. For areas defined as unsuitable to species occurrence, we mapped the difference between the suitability predictions obtained with climate‐only versus soil‐only models to identify regions where climate and soil might restrict species ranges independently or jointly.ResultsFor 40 out of the 42 species, the best models included both climate and soil predictors. The models including only soil predictors performed better than the models including only climate predictors, but we still detected a drought‐sensitive response for most of the species. Edaphic conditions were predicted to restrict species occurrence in the centre, the north‐west and in the north‐east of Amazonia, while the climatic conditions were identified as the restricting factor in the eastern Amazonia, at the border of Roraima and Venezuela and in the Andean foothills.Main conclusionsOur results revealed that soil data are a more important predictor than climate of plant species range in Amazonia. The strong control of species ranges by edaphic features might reduce species’ abilities to track suitable climate conditions under a drought‐increase scenario. Future challenges are to improve the quality of soil data and couple them with process‐based models to better predict species range dynamics under climate change.