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The present world is now facing the challenge of proper management and resource recovery of the enormous amount of plastic waste. Lack of technical skills for managing hazardous waste, insufficient infrastructure development for recycling and recovery, and above all, lack of awareness of the rules and regulations are the key factors behind this massive pile of plastic waste. The severity of plastic pollution exerts an adverse effect on the environment and total ecosystem. In this study, a comprehensive analysis of plastic waste generation, as well as its effect on the human being and ecological system, is discussed in terms of source identification with respect to developed and developing countries. A detailed review of the existing waste to energy and product conversion strategies is presented in this study. Moreover, this study sheds light on sustainable waste management procedures and identifies the key challenges to adopting effective measures to minimise the negative impact of plastic waste. Highlights A comprehensive analysis of global plastic waste generation and its effect on land and marine environment is conducted. Plastic waste management technologies for both land and marine environment are reviewed thoroughly. The modern technologies for waste-to-energy and waste-to-production conversion are discussed in this paper. The potential challenges and a way forward for sustainable management technologies are presented.

The efficacy of drugs widely varies in individuals, and the gut microbiota plays an important role in this variability. The commensal microbiota living in the human gut encodes several enzymes that chemically modify systemic and orally administered drugs, and such modifications can lead to activation, inactivation, toxification, altered stability, poor bioavailability, and rapid excretion. Our knowledge of the role of the human gut microbiome in therapeutic outcomes continues to evolve. Recent studies suggest the existence of complex interactions between microbial functions and therapeutic drugs across the human body. Therapeutic drugs or xenobiotics can influence the composition of the gut microbiome and the microbial encoded functions. Both these deviations can alter the chemical transformations of the drugs and hence treatment outcomes. In this review, we provide an overview of (i) the genetic ecology of microbially encoded functions linked with xenobiotic degradation; (ii) the effect of drugs on the composition and function of the gut microbiome; and (iii) the importance of the gut microbiota in drug metabolism.

The phrase “hot spots and hot moments” first entered the lexicon in 2003, following the publication of the paper “Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems” by McClain and others (Ecosystems 6:301–312, 2003). This paper described the potential for rare places and rare events to exert a disproportionate influence on the movement of elements at the scale of landscapes and ecosystems. Here, we examine how the cleverly named hot spot and hot moment concept (hereafter HSHM) has influenced biogeochemistry and ecosystem science over the last 13 years. We specifically examined the extent to which the HSHM concept has: (1) motivated research aimed at understanding how and why biogeochemical behavior varies across spatiotemporal scales; (2) improved our ability to detect HSHM phenomena; and (3) influenced our approaches to restoration and ecosystem management practices. We found that the HSHM concept has provided a highly fertile framework for a substantial volume of research on the spatial and temporal dynamics of nutrient cycling, and in doing so, has improved our understanding of when and where biogeochemical rates are maximized. Despite the high usage of the term, we found limited examples of rigorous statistical or modeling approaches that would allow ecosystem scientists to not only identify, but scale the aggregate impact of HSHM on ecosystem processes. We propose that the phrase “hot spots and hot moments” includes two implicit assumptions that may actually be limiting progress in applying the concept. First, by differentiating “hot spots” from “hot moments,” the phrase separates the spatial and temporal components of biogeochemical behavior. Instead, we argue that the temporal dynamics of a putative hot spot are a fundamental trait that should be used in their description. Second, the adjective “hot” implicitly suggests that a place or a time must be dichotomously classified as “hot or not.” We suggest instead that each landscape of interest contains a wide range of biogeochemical process rates that respond to critical drivers, and the gradations of this biogeochemical topography are of greater interest than the maximum peaks. For these reasons, we recommend replacing the HSHM terminology with the more nuanced term ecosystem control points. “Ecosystem control” suggests that the rate must be of sufficient magnitude or ubiquity to affect dynamics of the ecosystem, while “points” allows for descriptions that simultaneously incorporate both spatial and temporal dynamics. We further suggest that there are at least four distinct types of ecosystem control points whose influence arises through distinct hydrologic and biogeochemical mechanisms. Our goal is to provide the tools with which researchers can develop testable hypotheses regarding the spatiotemporal dynamics of biogeochemistry that will stimulate advances in more accurately identifying, modeling and scaling biogeochemical heterogeneity to better understand ecosystem processes.

Machine learning algorithms have progressively become a part of landslide susceptibility mapping practices owing to their robustness in dealing with complicated and non-linear mechanisms of landslides. However, the internal structures of such algorithms contain a set of hyperparameter configurations whose correct setting is crucial to get the highest achievable performance. This current study investigates the effectiveness and robustness of advanced optimization algorithms, including random search (RS), Bayesian optimization with Gaussian Process (BO-GP), Bayesian optimization with Tree-structured Parzen Estimator (BO-TPE), genetic algorithm (GA), and Hyperband method, for optimizing the hyperparameters of the eXtreme Gradient Boosting (XGBoost) algorithm in the spatial prediction of landslides. 12 causative factors were considered to produce landslide susceptibility maps (LSMs) for the Trabzon province of Turkey, where translational shallow landslides are ubiquitous. Five accuracy metrics, including overall accuracy (OA), precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), and a statistical significance test were employed to measure the effectiveness of the optimization strategies on XGBoost algorithm. Compared to the XGBoost model with default setting, the optimized models provided a significant improvement of up to 13% in terms of overall accuracy, which was also ascertained by McNemar’s test. AUC analysis revealed that having statistically similar performances, GA (0.942) and Hyperband (0.922) methods had the highest predictive abilities, followed by BO-GP (0.920), BO-TPE (0.899), and RS (0.894). Analysis of computational cost efficiency showed that the Hyperband approach (40.3 s) was much faster (about 13 times) than the GA in hyperparameter tuning, and thus appeared to be the best optimization algorithm for the problem under consideration.

Research into the benefits that ecosystems contribute to human wellbeing has multiplied over the last few years following from the seminal contributions of the international Millennium Ecosystem Assessment. In comparison, the fact that some ecosystem goods and services undermine or harm human wellbeing has been seriously overlooked. These negative impacts have become known as ecosystem disservices. The neglect of ecosystem disservices is problematic because investments into the management or reduction of ecosystem disservices may yield better outcomes for human wellbeing, or at a lower investment, than management of ecosystem services. Additionally, management to optimise specific ecosystem services may simultaneously exacerbate associated disservices. We posit that one reason for the neglect of ecosystem disservices from the discourse and policy debates around ecosystems and human wellbeing is because there is no widely accepted definition or typology of ecosystem disservices. Here, we briefly examine current understandings of the term ecosystem disservices and offer a definition and a working typology to help generate debate, policy and management options around ecosystem disservices. We differentiate ecosystem disservices from natural hazards and social hazards, consider some of their inherent properties and then classify them into six categories. A variety of examples are used to illustrate the different types of, and management strategies to, ecosystem disservices.

Globally, substantial research into endophytic microbes is being conducted to increase agricultural and environmental sustainability. Endophytic microbes such as bacteria, actinomycetes, and fungi inhabit ubiquitously within the tissues of all plant species without causing any harm or disease. Endophytes form symbiotic relationships with diverse plant species and can regulate numerous host functions, including resistance to abiotic and biotic stresses, growth and development, and stimulating immune systems. Moreover, plant endophytes play a dominant role in nutrient cycling, biodegradation, and bioremediation, and are widely used in many industries. Endophytes have a stronger predisposition for enhancing mineral and metal solubility by cells through the secretion of organic acids with low molecular weight and metal-specific ligands (such as siderophores) that alter soil pH and boost binding activity. Finally, endophytes synthesize various bioactive compounds with high competence that are promising candidates for new drugs, antibiotics, and medicines. Bioprospecting of endophytic novel secondary metabolites has given momentum to sustainable agriculture for combating environmental stresses. Biotechnological interventions with the aid of endophytes played a pivotal role in crop improvement to mitigate biotic and abiotic stress conditions like drought, salinity, xenobiotic compounds, and heavy metals. Identification of putative genes from endophytes conferring resistance and tolerance to crop diseases, apart from those involved in the accumulation and degradation of contaminants, could open new avenues in agricultural research and development. Furthermore, a detailed molecular and biochemical understanding of endophyte entry and colonization strategy in the host would better help in manipulating crop productivity under changing climatic conditions. Therefore, the present review highlights current research trends based on the SCOPUS database, potential biotechnological interventions of endophytic microorganisms in combating environmental stresses influencing crop productivity, future opportunities of endophytes in improving plant stress tolerance, and their contribution to sustainable remediation of hazardous environmental contaminants. Graphical Abstract

Freshwater reservoirs are a known source of greenhouse gas (GHG) to the atmosphere, but their quantitative significance is still only loosely constrained. Although part of this uncertainty can be attributed to the difficulties in measuring highly variable fluxes, it is also the result of a lack of a clear accounting methodology, particularly about what constitutes new emissions and potential new sinks. In this paper, we review the main processes involved in the generation of GHG in reservoir systems and propose a simple approach to quantify the reservoir GHG footprint in terms of the net changes in GHG fluxes to the atmosphere induced by damming, that is, ‘what the atmosphere sees.’ The approach takes into account the pre-impoundment GHG balance of the landscape, the temporal evolution of reservoir GHG emission profile as well as the natural emissions that are displaced to or away from the reservoir site resulting from hydrological and other changes. It also clarifies the portion of the reservoir carbon burial that can potentially be considered an offset to GHG emissions.

Lake ecosystems and the services that they provide to people are profoundly influenced by dissolved organic matter derived from terrestrial plant tissues. These terrestrial dissolved organic matter (tDOM) inputs to lakes have changed substantially in recent decades, and will likely continue to change. In this paper, we first briefly review the substantial literature describing tDOM effects on lakes and ongoing changes in tDOM inputs. We then identify and provide examples of four major challenges which limit predictions about the implications of tDOM change for lakes, as follows: First, it is currently difficult to forecast future tDOM inputs for particular lakes or lake regions. Second, tDOM influences ecosystems via complex, interacting, physical-chemical-biological effects and our holistic understanding of those effects is still rudimentary. Third, non-linearities and thresholds in relationships between tDOM inputs and ecosystem processes have not been well described. Fourth, much understanding of tDOM effects is built on comparative studies across space that may not capture likely responses through time. We conclude by identifying research approaches that may be important for overcoming those challenges in order to provide policy- and management-relevant predictions about the implications of changing tDOM inputs for lakes.

Drought will increasingly threaten forest ecosystems worldwide. Understanding how competition influences tree growth response to drought is essential for forest management aiming at climate change adaptation. However, published results from individual case studies are heterogeneous and sometimes contradictory. We reviewed 166 cases from the peer-reviewed literature to assess the influence of stand-level competition on tree growth response to drought. We monitored five indicators of tree growth response: mean sensitivity (interannual tree ring width variability); association between inter-annual growth variability and water availability; resistance; recovery; and resilience to drought. Vote counting did not indicate a consistent effect of competition on mean sensitivity. Conversely, higher competition for resources strengthened the association between water availability and inter-annual growth rates. Meta-analysis showed that higher competition reduced resistance (p < 0.001) and improved recovery (p < 0.05), but did not consistently affect resilience. Species, site and stand characteristics, and drought intensity were insignificant or poor predictors for the large variability among the investigated cases. Our review and meta-analysis show that competition does not affect the response of tree growth to drought in a unidirectional and universal way. Although density reduction (thinning) can alleviate growth declines during drought, the effects on growth after stress are uncertain. The large variability among investigated cases suggests that local-scale processes play a crucial role in determining such responses and should be explicitly evaluated and integrated into specific strategies for adaptation of forests to climate change.

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.