Extreme weather events such as floods, bushfires, cyclones, and drought, are projected to increase in eastern Australia. Understanding how these events influence the combined, sustainable well-being of humans, animals, and ecosystems - that is One Health - will enable development of transdisciplinary and ultimately more effective interventions. A scoping review was conducted to explore the research associated with the effects of extreme weather events in eastern Australia using a One Health lens, specifically identifying the type of extreme weather events studied, the research conducted in the context of One Health, and gaps to inform improved One Health implementation. The review followed JBI guidelines (based on PRISMA). Eligible research was peer-reviewed, in English, and published since 2007, in which primary research studies investigated the impact of extreme weather events in eastern Australia on at least two of ecosystems, human health, and animal health. Using structured search terms, six databases were searched. Following removal of duplicates, 870 records were screened by two reviewers. Eleven records were eligible for data extraction and charting. The scope of extreme weather events studied was relatively limited, with studies in flood and bushfire settings predominating, but relatively little research on cyclones. Major health themes included more than the impact of extreme weather events on physical health (zoonotic and vector-borne diseases) through investigation of social well-being and mental health in the context of the human-animal bond in evacuation behaviors and drought. Research gaps include studies across a broader range of extreme weather events and health topics, as well as a more comprehensive approach to including the impacts of extreme weather events on all three domains of One Health. The limited research focus inevitably translates to limited recommendations for policy, planning and response to manage extreme weather event emergencies. Given the expected increase in frequency of these events, there is a critical need for more comprehensive primary research to better identify strategies and facilitate implementation of One Health promotion for improved outcomes in extreme weather event emergencies.

With the growing concerns about the protection of ecosystem functions and services, governments have developed public policies and organizations have produced an awesome volume of digital data freely available through their websites. On the other hand, advances in data acquisition through remote sensed sources and processing through geographic information systems (GIS) and statistical tools, allowed an unprecedent capacity to manage ecosystems efficiently. However, the real-world scenario in that regard remains paradoxically challenging. The reasons can be many and diverse, but a strong candidate relates with the limited engagement among the interest parties that hampers bringing all these assets into action. The aim of the study is to demonstrate that management of ecosystem services can be significantly improved by integrating existing environmental policies with environmental big data and low-cost GIS and data processing tools. Using the Upper Rio das Velhas hydrographic basin located in the state of Minas Gerais (Brazil) as example, the study demonstrated how Principal Components Analysis based on a diversity of environmental variables assembled sub-basins into urban, agriculture, mining and heterogeneous profiles, directing management of ecosystem services to the most appropriate officially established conservation plans. The use of GIS tools, on the other hand, allowed narrowing the implementation of each plan to specific sub-basins. This optimized allocation of preferential management plans to priority areas was discussed for a number of conservation plans. A paradigmatic example was the so-called Conservation Use Potential (CUP) devoted to the protection of aquifer recharge (provision service) and control of water erosion (regulation service), as well as to the allocation of uses as function of soil capability (support service). In all cases, the efficiency gains in readiness for plans\' implementation and economy of resources were prognosed as noteworthy.

The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research. Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: (1) environmental data integration and harmonization system, (2) clinical and omics data integration, (3) advanced analytical algorithm development, and (4) visualization interface development and targeted population-based cohort recruitment. The CHiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.

Antibiotic resistance genes (ARGs) have moved into focus as a critically important response variable in global change biology, given the increasing environmental and human health threat posed by these genes. However, we propose that elevated levels of ARGs should also be considered a factor of global change, not just a response. We provide evidence that elevated levels of ARGs are a global change factor, since this phenomenon is linked to human activity, occurs globally, and affects biota. We explain why ARGs could be considered the global change factor, rather than the organisms containing them; and we highlight the difference between ARGs and the presence of antibiotics, which are not necessarily linked since elevated levels of ARGs are caused by multiple factors. Importantly, shifting the perspective to elevated levels of ARGs as a factor of global change opens new avenues of research, where ARGs can be the experimental treatment. This includes asking questions about how elevated ARG levels interact with other global change factors, or how ARGs influence ecosystem processes, biodiversity or trophic relationships. Global change biology stands to profit from this new framing in terms of capturing more completely the real extent of human impacts on this planet.

Restoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between \'mechanistic\' and \'process-based\' ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.

Microplastics pose significant challenges on a global scale. In Ghana, these tiny pollutants infiltrate diverse ecosystems such as coastal areas, rivers, lakes, and forests, vital to the nation\'s economy and social well-being. This review examines the current depth of knowledge in research and the escalating concern of microplastics, identifying significant gaps in research and understanding. The findings highlight the limited understanding of the extent and distribution of microplastic pollution across different environmental compartments, primarily focusing on coastal environments. Additionally, detection and quantification techniques for microplastics face several complexities and limitations in the Ghanaian context due to constraints such as infrastructure, resources, and expertise. Despite some research efforts, particularly along the coastline, there is still a distinct lack of attention in various regions and ecosystems within Ghana. This imbalance in research focus hinders the understanding and effective mitigation of microplastics in the country. This therefore necessitates the implementation of systematic policy frameworks, emphasizing the importance of recycling and upcycling as effective strategies to address the challenges of microplastics in Ghana with more targeted research and public engagement. This review serves as a call to action for a strategic approach to research and policy-making on microplastic research and pollution in Ghana.

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.

Cyanobacteria, as vital components of aquatic ecosystems, face increasing challenges due to acidification driven by various anthropogenic and natural factors. Understanding how cyanobacteria adapt and respond to acidification is crucial for predicting their ecological dynamics and potential impacts on ecosystem health. This comprehensive review synthesizes current knowledge on the acclimation mechanisms and responses of cyanobacteria to acidification stress. Detailly, ecological roles of cyanobacteria were firstly briefly concluded, followed by the effects of acidification on aquatic ecosystems and cyanobacteria. Then the review focuses on the physiological, biochemical, and molecular strategies employed by cyanobacteria to cope with acidification stress, highlighting key adaptive mechanisms and their ecological implications. Finally, a summary of strategies to enhance acid resistance in cyanobacteria and future directions was discussed. Utilizing omics data and machine learning technology to build a cyanobacterial acid regulatory network allows for predicting the impact of acidification on cyanobacteria and inferring its broader effects on ecosystems. Additionally, acquiring acid-tolerant chassis cells of cyanobacteria through innovative techniques facilitates the advancement of environmentally friendly production of acidic chemicals. By synthesizing empirical evidence and theoretical frameworks, this review aims to elucidate the complex interplay between cyanobacteria and acidification stressors, providing insights for future research directions and ecosystem management strategies.

Microplastics generated from fragmentation of leftover plastics and industrial waste has reached in the remotely located Asian water tower (AWT) region, the 3rd pole of earth and origin site of several freshwater rivers. The accumulation of microplastics in AWT ecosystem has potential to alter the climatic condition contributing in global warming and disturbing the biodiversity structural dynamics. The present paper provides a comprehensive critical discussion over quantitative assessment of microplastics in different ecosystems (i.e. river, lakes, sediment and snow or glacier) of AWT. The hydrodynamic fate and transport of microplastics and their ecological impact on hydromorphology and biodiversity of AWT has been exemplified. Furthermore, key challenges, perspectives and research directions are identified to mitigate microplastics associated problems. During survey, the coloured polyethylene and polyurethane fibers are the predominant microplastics found in most areas of AWT. These bio-accumulated MPs alter the rhizospheric community structure and deteriorate nitrogen fixation process in plants. Significance in climate change, MPs pollution is enhancing the emissions of greenhouse gases (NH3 by ∼34% and CH4 by ∼9%), contributing in global warming. Considering the seriousness of MPs pollution, this review study can enlighten the pathways to investigate the effect of MPs and to develop monitoring tools and sustainable remediation technologies with feasible regulatory strategies maintaining the natural significance of AWT region.

Our knowledge of microbial processes-who is responsible for what, the rates at which they occur, and the substrates consumed and products produced-is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are-by themselves-insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.