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Global patterns of biodiversity and comparisons between tropical and temperate ecosystems have pervaded ecology from its inception. However, the urgency in understanding these global patterns has been accentuated by the threat of rapid climate change. We apply an adaptive model of environmental tolerance evolution to global climate data and climate change model projections to examine the relative impacts of climate change on different regions of the globe. Our results project more adverse impacts of warming on tropical populations due to environmental tolerance adaptation to conditions of low interannual variability in temperature. When applied to present variability and future forecasts of precipitation data, the tolerance adaptation model found large reductions in fitness predicted for populations in high-latitude northern hemisphere regions, although some tropical regions had comparable reductions in fitness. We formulated an evolutionary regional climate change index (ERCCI) to additionally incorporate the predicted changes in the interannual variability of temperature and precipitation. Based on this index, we suggest that the magnitude of climate change impacts could be much more heterogeneous across latitude than previously thought. Specifically, tropical regions are likely to be just as affected as temperate regions and, in some regions under some circumstances, possibly more so.

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

This study analyzes farmers’ perception of grazing restriction policies, grassland environment and ecological management following the implementation of environmental protection policies in northern China. Understanding farmers’ attitudes and their causes will hopefully aid in the creation and execution of future policies. One hundred and thirty-five households were surveyed at three occasions over the course of a decade to explore the causes and processes of farmers’ perception. Farmers’ ecological awareness tends to be short term. In areas with a degraded environment, farmers were eager to implement policies to improve the environment and recognized the positive impact of the grazing ban policy (GBP). However, as conditions improved, farmers’ recognition and acceptance of the GBP became negative. Although farmers recognized the benefits of the GBP, they showed little awareness of the long-term process of environmental governance. As can be seen from the farmers’ ecological awareness and their attitudes toward the GBP, they are more inclined to value short-term economic interest than ecological protection. We suggest that good environmental protection policy must take into account the ecological and economic interests of farmers.

In order to understand how global warming effects on ecosystem primary production may change depending on nutrient enrichment, a new classification is proposed to disentangle and recognize the combination of interactions among several factors, based on the effect direction (positive, negative, or neutral) and its changes induced in it by the other factor (synergizing, antagonizing, inducing no change, or changing it in some other way). Marine macroalgae were chosen (as primary producers for which there is the most experimental information available) to review the relevant studies on which this new classification can be tested. It was observed the positive effects of elevated temperature and nutrient enrichment often synergized each other within the temperature range between relatively low and optimal growth levels. However, the negative effect of further temperature elevation from optimal to higher levels was antagonized by nutrient enrichment in some studies but was synergized in others, depending on the range of temperature elevation. The positive effect of nutrient enrichment was antagonized (but still positive) by temperature increase above the optimum in many cases, although the effect sometimes switched to a negative effect depending on the magnitude of nutrient enrichment. These results predict that global warming will enhance bottom-up effects on primary production in cold seasons and areas, and there will be a negative warming effect on production in hot seasons and areas, but it may be possible to mitigate this effect by appropriate levels of nutrient enrichment.

Plants under salt stress require additional energy supply to fuel salt tolerance mechanisms and growth. Bandehagh and Taylor (2020) establish that plants must strike a balance between energy supply and demand to maintain growth and development during salt stress. This review (1) summaries how salt stress affects different physiological and biochemical process altering the abundance of different metabolites that are feeding into regular and alternative respiratory pathways and shunts; (monomeric complex I, dimeric complex III and I + III2 supercomplex) found to be higher in halophyte mitochondria in comparison with glycophyte, implying efficient electron transfer from complex I to complex III in halophyte mitochondria. Further, the stability of ATP synthase (complex V) also found to be higher in halophyte suggesting halophyte mitochondria better equipped to supply additional ATP required to support salt stress response.Synthesis of organic compatible solutes is an important component for plant salt stress tolerance. In this regard, proline plays an important role in protecting plants from under salinity conditions and showed that salt tolerance is associated with changes in lipid metabolic processes. They also discovered the important role of phosphatidylserine (PS) in mediating enzyme activity, and exogenous application of PS alleviated the effects of NaCl tissue toxicity. The results showed that the superior K + retention ability in both mature and elongation zone of rice root is the key trait conferring its differential salinity stress tolerance. They suggested that besides the superior ability to activate root H + -ATPase pump operation, this key trait is also related to the reduced sensitivity of K + efflux channels to reactive oxygen species and the lower upregulation in OsGORK and higher upregulation of OsAKT1.A key trait long recognized to improve salinity tolerance in many plants is the maintenance of a low Na + /K + ratio. Transient expression experiment showed that JcHDZ07 is a nuclear-localized protein.In improving Na + exclusion ability to maintain root ion homeostasis to ensure a relatively 9 low shoot Na + concentration under saline conditions; 2) maintaining a high shoot sugar content under saline conditions which is enabled by protecting photosystems structures, enhancing photosynthetic performance and sucrose synthetase activity, and inhibiting sucrose degradation. Further, authors suggested that targeting the key genes related to the regulatory mechanisms could provide opportunities to breed more salt tolerant sweet sorghum.Overall, we hope this special issue of benefit to plant breeders and land managers by delivering novel information and insights on the salinity stress response, signalling and adaptive mechanisms operating in plants.

In this study, an agroforestry ecosystem project (AEP) is developed for confronting the conflict between agricultural development and forest protection. A fuzzy stochastic programming with Laplace scenario analysis (FSL) is proposed for planning water resources in an AEP issue under uncertainties. FSL can not only deal with spatial and temporal variations of hydrologic elements and meteorological conditions; but also handle uncertainties that are expressed in terms of probability, possibility distributions and fuzzy sets; meanwhile, policy scenario analysis with Laplace’s criterion (PSL) is introduced to handle probability of each scenario occurrence under the supposition of no data available. The developed FSL can be applied to an AEP issue in Xixian county, located in north of China. The result of ecological effects, water allocation patterns, pollution mitigation schemes and system benefits under various scenarios are obtained, which can support policymakers adjusting current strategy to improve regional ecological function with cost-effective and sustainable manners. Meanwhile, it can support generating a robust water plan for regional sustainability in an AEP issue under uncertainties.

AbstractWater scarcity has become a global severe challenge over the past few decades. Quantifying the impact of climate variability and land use on water resource availability is crucial for integrated water resource management. Many studies have focused on blue water but ignored green water which is important in the terrestrial ecosystem, especially on different temporal scales. In this study, we selected the Shanmei Reservoir, the most import drinking water resource for a rapidly development city of Southeast China, as a case for analysis of these impacts for the entire basin. We adopted the Soil and Water Assessment Tool (SWAT) to investigate the spatial and temporal distributions of blue water (BW), green water flow (GWF) and green water storage (GWS) in the Shanmei Reservoir Basin (SRB). The results of the blue and green water components (BW and GW) revealed that SRB is dominated by BW, accounting for 52.6% of the total water resources, while GW accounted for 47.4%. There was an insignificant upward trend of BW and a significant upward trend of GWF, with a tendency rate of 1.125 mm a−1. Precipitation was the key factor affecting BW on annual and monthly scales. The GWF was more sensitive to temperature at both the annual and monthly scales. The GWS was significantly correlated with precipitation at the monthly scale, while insignificant correlation occurred at the annual scale. The spatial distribution of BW was largely dominated by precipitation, and land-use types led to the differentiation of GW. It indicates that the BW of paddy fields is greater than that of forests, while the GWS of forests is greater than that of orchards and rainfed croplands.