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ABSTRACT Biological invasions pose a serious threat to native biodiversity, and climate change intensifies the risk of non‐native species invasion in human‐altered environments, particularly in freshwater ecosystems. In South Korean freshwaters, the number of non‐native fish species increased significantly since the early 1950s. We screened risk of invasion by 43 freshwater fish species in South Korea using the Aquatic Species Invasiveness Screening Kit (AS‐ISK). Of these species, 26 (~ 61%) posed a high to very high risk of invasiveness. Black bullhead Ameiurus melas, brown bullhead Ameiurus nebulosus, North African catfish Clarias gariepinus, green sunfish Lepomis cyanellus, black carp Mylopharyngodon piceus, round goby Neogobius melanostomus, and Chinese sleeper Perccottus glenii were very high risk under current and predicted climate conditions. Striped snakehead Channa striata, longear sunfish Lepomis megalotis, smallmouth bass Micropterus dolomieu, white perch Morone americana, Eurasian perch Perca fluviatilis, and rudd Scardinius erythrophthalmus were high risk only under predicted climate conditions. Present risks were generally consistent with previous qualitative assessment, with some discrepancies, likely due to unobserved or under‐reported invasive traits of some species or a better ability of the present semi‐quantitative approach to estimate potential risks. Our results will inform South Korean decision‐making to potentially enhance regional cooperation across East Asia against transboundary introductions of aquatic invasive species. [ABSTRACT FROM AUTHOR]

The authors comment on modernization activities underway at the U.S. Centers for Disease Control and Prevention (CDC) and across the field of public health. Topics discussed include highlighting explicit dependencies among foundational data activities and advanced capabilities, importance of additional support for the shift to a data-ready ecosystem framework, and need to strengthen the public health data science workforce.

Antibiotic overprescribing is a global issue that significantly contributes to increased antimicrobial resistance. Strengthening antimicrobial prescribing practices should be considered a priority. The emergency department (ED) represents a setting where antibiotics are frequently prescribed, but the determinants that influence prescribing choices are complex and multifaceted. We conducted an exploratory qualitative study to investigate the contextual factors that influence antibiotic prescribing choices among clinicians in the ED. The study employed video-reflexive ethnography (VRE) to capture prospective clinical decision-making in situated practice. Data collection involved fieldwork observations, video observations, and delivery of facilitated group reflexive sessions, where clinicians viewed a selection of recorded video snippets relating to antibiotic prescribing. Study was conducted across two EDs within the same health service in Australia. A total of 29 clinical conversations focusing on antibiotic prescribing were recorded. Additionally, 34 clinicians participated in group reflexive sessions. Thematic analysis from the transcribed data yielded four themes: 'importance of clinical judgment', 'usability of prescribing guidelines', 'managing patient expectations', and 'context-dependent disruptions'. Our findings provide insights into the challenges faced by clinicians in navigating complex ED environment, utilising electronic decision-support tools and engaging in discussions about patient treatments with senior clinicians. The findings also indicate that VRE is useful in visualising full complexity of the ED setting, and in initiating meaningful discussions among clinical teams. Integrating the use of VRE in everyday clinical settings can potentially facilitate the implementation of pragmatic solutions for delivering effective antibiotic stewardship practices. [ABSTRACT FROM AUTHOR]

Population in developed countries spend most of their time indoors, whether in their homes, workplaces, stores or leisure areas. Due to the COVID-19 pandemic, this situation worsened and now, more than ever, the importance of a high Indoor Environmental Quality (IEQ) is highlighted. The IEQ is very important in building performance since it is directly related to its occupants' comfort, health, wellbeing, and productivity and the Sick Building Syndrome (SBS) concept. Therefore, it is essential to develop tools to support designers' decision-making in the materialization of indoor environments with higher quality. From the state-of-art analysis, it is possible to conclude that the methods to assess the overall building performance already consider the IEQ. Still, most use an approach that does not cover all relevant indicators. In this context, this paper presents the first milestone of a research work that aims to develop a new method to rate the overall IEQ of office buildings in Portugal. The main objective of the present study is to propose a list of IEQ indicators for office buildings, adapted to the Portuguese context, based on the analysis of existing rating methods for buildings and the recommendations of national and international standards. [ABSTRACT FROM AUTHOR]

Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modeling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps. To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU). The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field. • AI makes mechanistic epidemiological models usable to support decisions on farms. • Structured text for knowledge representation enhances collaborative tool design. • AI and software engineering automate web application generation for disease control. • The resulting code for creating decision-support tools from models is open-source. [ABSTRACT FROM AUTHOR]

Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature-physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature's potential contribution to creating more equitable, green, livable cities with active inhabitants. [ABSTRACT FROM AUTHOR]

Policies and legislation for the importation and use of non-native (NN) freshwater fishes have been rapidly evolving in several countries around the world in response to heightened evidence of their adverse ecological and economic impacts. In China, the already large and growing number of NN freshwater fishes being imported from foreign countries or translocated within China has necessitated the development of management strategies supported by policies at both national and regional levels. Despite increasing awareness of the damage caused by NN fishes, such initiatives in China continue to lag behind those already implemented in other countries. This study endeavors to narrow this policy gap by: (i) providing a synoptic overview of the vectors and pathways of NN freshwater fish introductions and dispersal both into and within China; (ii) evaluating existing policies and legislation relative to those currently implemented in Europe, the USA and Canada; (iii) examining the role of decision-support tools for identifying and assessing the risks posed by NN fishes; and (iv) proposing perspectives and possible options for NN fish policies, legislation and management in China. [ABSTRACT FROM AUTHOR]

• BIM is used as a thinking tool for the conceptual design process. • The model is intended for the neutralization of energy consumption, estimating the amount of photovoltaic panels needed to achieve a NZEH. • A new, original methodology is used, with a built BIM prototype to measure energy consumption. • This tool for enriching the world with a new sustainable dimension is implemented in the BIM app as part of the software. • The originality of this paper is to create an embedded simplification tool for evaluating the electrical energy consumption (energy system) inside BIM. BIM is not simply a drafting tool or advanced CAD. BIM is a collaboratively generated and maintained data-rich information source, especially when it comes to the energy-calculation process and beyond. Due to growing awareness around energy conservation, numerous projects and initiatives have occurred to promote the development of low- and net-zero-energy homes. The widespread adoption of photovoltaic rooftop systems has shown that solar homes can be a effective solution to that energy demand; nowadays, the idea that BIM is not just software has been decided upon. In this paper, a new approach was used to calculate energy consumption using a BIM template designed for this purpose. The greatest value derived from BIM was in the template designed to neutralize energy consumption by calculating the number of photovoltaic panels needed to achieve a net-zero-energy house. The BIM prototype developed is considered a useful tool, intended to be used by everyday, non-specialist users to aid and disseminate energy knowledge in order to achieve a zero-energy home, as well as to be used as a decision-support tool, incorporating energy simulation into the early design process of zero-energy buildings in architectural practice. Finally, the tool was tested on a group of users in order to fine-tune and develop the design parameters of the template. [ABSTRACT FROM AUTHOR]

Collectively, these included 84 streams, where LCT had been observed by management agencies (occupied streams) and 127 historical streams, where LCT had not been observed in the last several decades (unoccupied streams). Unoccupied streams were assumed to contain LCT habitat because they currently support non-native trout populations (the likely cause of LCT extirpation) or were selected by management partners based on habitat data or resolved historical threats. In streams where LCT co-occurred with non-native trout, we simulated effects of non-native trout eradication by comparing LCT population forecasts with and without non-natives. Eliminating non-natives while simultaneously reducing environmental stochasticity (to the mean for populations without non-native trout or 73% of the mean for populations with non-native trout) substantially reduced extinction risks for most populations, where non-native trout co-occurred with LCT (Fig.). [Extracted from the article]

• High throughput scale-down chromatography workflow with decision-support. • Augmentation of design of experiments with the fractionation diagram method. • Automated and systematic high-throughput raw data manipulation. • Resin selection using empirical correlations through design of experiments. • Creation of windows of operation under uncertainty using empirical correlations. Chromatography remains the workhorse in antibody purification; however process development and characterisation still require significant resources. The high number of operating parameters involved requires extensive experimentation, traditionally performed at small- and pilot-scale, leading to demands in terms of materials and time that can be a challenge. The main objective of this research was the establishment of a novel High Throughput Process Development (HTPD) workflow combining scale-down chromatography experimentation with advanced decision-support techniques in order to minimise the consumption of resources and accelerate the development timeframe. Additionally, the HTPD workflow provides a framework to rapidly manipulate large datasets in an automated fashion. The central component of the HTPD workflow is the systematic integration of a microscale chromatography experimentation strategy with an advanced chromatogram evaluation method, design of experiments (DoE) and multivariate data analysis. The outputs of this are leveraged into the screening and optimisation components of the workflow. For the screening component, a decision-support tool was developed combining different multi-criteria decision-making techniques to enable a fair comparison of a number of CEX resin candidates and determine those that demonstrate superior purification performance. This provided a rational methodology for screening chromatography resins and process parameters. For the optimisation component, the workflow leverages insights provided through screening experimentation to guide subsequent DoE experiments so as to tune significant process parameters for the selected resin. The resulting empirical correlations are linked to a stochastic modelling technique so as to predict the optimal and most robust chromatographic process parameters to achieve the desired performance criteria. [ABSTRACT FROM AUTHOR]

This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilised geographical, temporal, and modus operandi information relating to residential burglaries (n = 180), commercial robberies, (n = 118), and car thefts (n = 376). Model performance was assessed using receiver operating characteristic analysis and by examining the success with which the seven models could successfully prioritise linked over unlinked crimes. The regression‐based and probabilistic models achieved comparable accuracy and were generally more accurate than the tree‐based models tested in this study. The Logistic algorithm achieved the highest area under the curve (AUC) for residential burglary (AUC = 0.903) and commercial robbery (AUC = 0.830) and the SimpleLogistic algorithm achieving the highest for car theft (AUC = 0.820). The findings also indicated that discrimination accuracy is maximised (in some situations) if behavioural domains are utilised rather than individual crime scene behaviours and that the AUC should not be used as the sole measure of accuracy in behavioural crime linkage research. [ABSTRACT FROM AUTHOR]

Abstract Recreational fishing activity has recovered in the Nerbioi estuary (Northern Spain), after water sanitation and environmental improvement. Recreational fishing is important for the local population; therefore, future management measures that could cause changes in the estuary should also consider the impacts on recreational fishing. Our objective was to analyze the effects that future management decisions and unexpected environmental changes, alone or in combination with climate change effects, can produce in recreational fishing in Nerbioi. The current recreational fishing activity was modelled using a System Dynamics Modelling (SDM). Based on those results, seven future scenarios were simulated. Results suggested that the adoption of future management measures to improve the environmental conditions could lead to additional positive changes for recreational fishing, as after water quality improvement, fish stocks will continue to recover, and these better conditions could attract more fishers and increase their satisfaction. Simulation of temporary and unexpected environmental changes resulted in quick estuarine recovery, without dramatic consequences for recreational fishing. In conclusion, analysing future scenarios on cultural ecosystem services such as recreational fishing, using SDM, can produce valuable information for decision making processes, facilitating the selection between environmental management alternatives. Graphical abstract Image 1 Highlights • System Dynamics Modelling (SDM) used in 7 scenarios affecting recreational fishing. • Chronic pressure removal is more effective than acute events removal in recreation recovery. • SDM assists managers in social-ecological systems and ecosystem services recovery. [ABSTRACT FROM AUTHOR]

Invasive species are a major threat to the economy, the environment, health, and thus human well-being. The international community, including the United Nations’ Global Invasive Species Program (GISP), National Invasive Species Council (NISC), and Center for Invasive Species Management (CISM), has called for a rapid control of invaders in order to minimize their adverse impacts. The effective management of invasive species is a highly complex problem requiring the development of decision tools that help managers prioritize actions most efficiently by considering corresponding bio-economic costs, impacts on ecosystems, and benefits of control. Operations research methods, such as mathematical programming models, are powerful tools for evaluating different management strategies and providing optimal decisions for allocating limited resources to control invaders. In this paper, we summarize the mathematical models applied to optimize invasive species prevention, surveillance, and control. We first define key concepts in invasive species management (ISM) in a framework that characterizes biological invasions, associated economic and environmental costs, and their management. We then present a spatio-temporal optimization model that illustrates various biological and economic aspects of an ISM problem. Next, we classify the relevant literature with respect to modeling methods: optimal control, stochastic dynamic programming, linear programming, mixed-integer programming, simulation models, and others. We further classify the ISM models with respect to the solution method used, their focus and objectives, and the specific application considered. We discuss limitations of the existing research and provide several directions for further research in optimizing ISM planning. Our review highlights the fact that operations research could play a key role in ISM and environmental decision-making, in particular closing the gap between the decision-support needs of managers and the decision-making tools currently available to management. [ABSTRACT FROM AUTHOR]

Abstract Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders – a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the "3 Cs" (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools. Highlights • Technical experts and stakeholders oftentimes co-produce decision-support tools. • We discuss co-production and uncertainty in watershed modeling DSTs. • We present a list of best practices derived from watershed modeling case studies. [ABSTRACT FROM AUTHOR]

Telecoupling is a novel interdisciplinary umbrella concept that enables natural and social scientists to understand and generate information for managing how humans and nature can sustainably coexist worldwide. The telecoupling framework gains its distinction by enabling researchers to dive deeply into systemic complexities, even if systems are far away from each other. It is also ambitious in its aim to meet challenges unencumbered by disciplines. To understand the forces affecting sustainability across local to global scales, it is essential to build a comprehensive set of spatially explicit tools for describing and quantifying multiple reciprocal socioeconomic and environmental interactions over distances. We introduce the Telecoupling Toolbox, the first set of tools developed to map and identify the five major interrelated components of the telecoupling framework: systems, flows, agents, causes, and effects. The modular design of the toolbox allows the integration of existing tools and software to assess synergies and trade-offs associated with policies and other local to global interventions. We show applications of the toolbox by using two representative telecoupling case studies that address a variety of socioeconomic and environmental issues. The results suggest that the toolbox can systematically map and quantify multiple telecouplings under various contexts while providing users with an easy-to-use interface. It is our hope that the innovative, free, and open-source toolbox can provide a useful platform to address globally important issues, such as land use and land cover change, species invasion, migration, flows of ecosystem services, and trade of goods and products. [ABSTRACT FROM AUTHOR]

• Organization effect on safety and risk management. • Safety and Risk management in process industries and healthcare. • Human decision-making in safety critical operations. • Design of decision-support tools for effective human decision-making. • Process systems engineering applications in healthcare. Cyber-physical-human systems are ubiquitous entities with varied scales of function. Apart from the systems-level complexity, it also poses a variety of decision-making problems to designers and practitioners. This decision-making is done by humans-in-the-loop, at every level of cyber-physical-human systems, with the help of decision-support tools. For any safety and risk critical cyber-physical-human systems organization, certain key features are useful to assess and compare the organizational functioning. Two such examples are process and healthcare industries, with safety and risk as major objectives. The paper uses the lens of humans-in-the-loop decision-making and decision-support tools to survey how safety and risk are handled in process and healthcare industries. Further, a discussion of current practices in the two domains and how future applications will involve more interdisciplinary aspects is provided. The paper concludes by discussing the ethical and data related concerns of a humans-in-the-loop and algorithm based shared decision-making future. [ABSTRACT FROM AUTHOR]

Many decision-makers are looking to science to clarify how nature supports human well-being. Scientists' responses have typically focused on empirical models of the provision of ecosystem services (ES) and resulting decision-support tools. Although such tools have captured some of the complexities of ES, they can be difficult to adapt to new situations. Globally useful tools that predict the provision of multiple ES under different decision scenarios have proven challenging to develop. Questions from decision-makers and limitations of existing decision-support tools indicate three crucial research frontiers for incorporating cutting-edge ES science into decision-support tools: (1) understanding the complex dynamics of ES in space and time, (2) linking ES provision to human well-being, and (3) determining the potential for technology to substitute for or enhance ES. We explore these frontiers in-depth, explaining why each is important and how existing knowledge at their cutting edges can be incorporated to improve ES decision-making tools. [ABSTRACT FROM AUTHOR]

Background: The vision of transforming health systems into learning health systems (LHSs) that rapidly and continuously transform knowledge into improved health outcomes at lower cost is generating increased interest in government agencies, health organizations, and health research communities. While existing initiatives demonstrate that different approaches can succeed in making the LHS vision a reality, they are too varied in their goals, focus, and scale to be reproduced without undue effort. Indeed, the structures necessary to effectively design and implement LHSs on a larger scale are lacking. In this paper, we propose the use of architectural frameworks to develop LHSs that adhere to a recognized vision while being adapted to their specific organizational context. Architectural frameworks are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution. Because these frameworks support the analysis of LHSs and allow their outcomes to be simulated, they act as pre-implementation decision-support tools that identify potential barriers and enablers of system development. They thus increase the chances of successful LHS deployment.Discussion: We present an architectural framework for LHSs that incorporates five dimensions-goals, scientific, social, technical, and ethical-commonly found in the LHS literature. The proposed architectural framework is comprised of six decision layers that model these dimensions. The performance layer models goals, the scientific layer models the scientific dimension, the organizational layer models the social dimension, the data layer and information technology layer model the technical dimension, and the ethics and security layer models the ethical dimension. We describe the types of decisions that must be made within each layer and identify methods to support decision-making.Conclusion: In this paper, we outline a high-level architectural framework grounded in conceptual and empirical LHS literature. Applying this architectural framework can guide the development and implementation of new LHSs and the evolution of existing ones, as it allows for clear and critical understanding of the types of decisions that underlie LHS operations. Further research is required to assess and refine its generalizability and methods. [ABSTRACT FROM AUTHOR]

Decision-support tools for nitrogen fertilization of oilseed rape are being developed to support fertilization practices. In order to optimize the inputs of mineral nitrogen, economically and energetically costly, Terres Inovia produces data, accompanies the tools in the culture system. We will report here a multi-year observation of the use and appropriation of a satellite tool by producers. [ABSTRACT FROM AUTHOR]

In contrast to common perceptions, the urban water infrastructure system is a complex and dynamic system that is constantly evolving and adapting to changes in the urban environment, to sustain existing services and provide additional ones. Instead of simplifying urban water infrastructure to a static system that is decoupled from its urban context, new management strategies use the complexity of the system to their advantage by integrating centralised with decentralised solutions and explicitly embedding water systems into their urban form. However, to understand and test possible adaptation strategies, urban water modelling tools are required to support exploration of their effectiveness as the human-technology-environment system coevolves under different future scenarios. The urban water modelling community has taken first steps to developing these new modelling tools. This paper critically reviews the historical development of urban water modelling tools and provides a summary of the current state of integrated modelling approaches. It reflects on the challenges that arise through the current practice of coupling urban water management tools with urban development models and discusses a potential pathway towards a new generation of modelling tools. [ABSTRACT FROM AUTHOR]

• WEPPcloud is a web-based decision-support tool for the WEPP model that: • was applied to relatively undisturbed forested watersheds to assess model performance. • satisfactorily simulated streamflow, sediment, and phosphorus loads. • was used to assess forest management impacts on water quality. • was applied to both gauged and ungauged watersheds. Suspended sediment and nutrients following forest management activities or wildfires are transported to streams and lakes via surface runoff and are a major threat to water quality. Land and water managers resort to hydrologic models to test hypotheses that can help them make informed decisions to minimize disturbances and protect water resources. We present applications of an online interface, WEPPcloud, for the Water Erosion Prediction Project (WEPP) model as a pre- and post-disturbance management tool to model various gauged and ungauged forested watersheds throughout the western U.S. We compare simulated streamflow, sediment, and phosphorus to observations at USGS gauging stations and assess the accuracy of the online interface with minimal or no calibration. Specifically, we present modeling results from 28 relatively undisturbed forested watersheds in the states of California, Nevada, Oregon, Washington, and Idaho. Across all watersheds, the NSEs based on the daily streamflow values, were in the range of 0.43 to 0.64 indicating satisfactory agreement between modeled and observed values. Similarly, annual average NSE for sediment yield was 0.61, while for phosphorus it was 0.75, 0.71, and 0.66, for total, particulate, and soluble reactive phosphorus, respectively. Additionally, we demonstrate the utility of the WEPPcloud interface as a tool to compare model results for ungauged watersheds from various disturbed conditions including prescribed fire, thinning, and wildfire to undisturbed model results to better understand the effects of forest management and wildfires on water quality and quantity. [ABSTRACT FROM AUTHOR]

• WEPPcloud is a web-based decision-support tool for the WEPP model that: • accesses free online databases for topography, soils, weather, land cover; • automatically prepares input data for the WEPP model; • allows users to run hydrologic simulations from an internet-connected computer; • is designed to assess forest management impacts on water quality in ungauged basins. We developed a new online interface for the Water Erosion Prediction Project (WEPP) model (WEPPcloud) with a framework that allows the incorporation and development of several other models and applications to make hydrologic models more accessible to land managers and facilitate the decision-making process. All inputs to WEPP, such as topography, soils, land use, and weather, are automatically created from publicly available online international databases and converted into input formats readable by the model. The WEPPcloud interface can be applied to forested, cropland, and rangeland conditions, but it is specialized for undisturbed forest conditions, post-wildfire, and pre- and post-wildfire management scenarios such as forest thinning, prescribed fire, or post-wildfire mulching. Users only need a computer or device with a web browser and an internet connection to perform advanced hydrologic simulations. All the model runs are stored remotely on WEPPcloud, which can be accessed by the users at any given time. This modeling tool is presented in two parts: Part I: Model description and parameterization, and Part II: Model performance assessment and applications to forest management and wildfires. [ABSTRACT FROM AUTHOR]

• Site suitability assessments are at the heart of renewable energy planning. • A collaborative approach enables developing a fit for purpose decision-support tool. • Stakeholder-driven site suitability criteria supports transparent decisions. • The tool supports informed discussions and fosters consensus on deployment areas. • Data and technological limitations signal action areas for advancing practice. Competing land-use demands associated with planning for renewables is resulting in growing social and political challenges. Spatial decision-support tools are being developed and applied to tackle some of these challenges by facilitating systematic identification of suitable development sites based on technical, environmental and socio-economic criteria. Nevertheless, decision-support tools are often developed with limited input from stakeholders affecting participative and accountable planning and decision-making. This paper aims to address this shortcoming and foster greater transparency in the development of support tools for renewable energy planning, and thus in siting decisions. It present and discusses a stakeholder-driven approach to the co-creation of an online decision-support tool that attends to planning policy, technical considerations, environmental protection and social concerns influencing renewable energy planning decisions in Ireland. This bottom-up approach, easily transferable to other jurisdictions, fosters proactive expert input, awareness raising and greater comprehensibility and acceptance of siting decisions. It also enables developing an interface that responds to users' needs. The paper contributes to advancing renewable energy planning practice through effective and meaningful stakeholder engagement that supports transparent and accountable decisions from the outset. It also highlights ongoing data and technological shortcomings affecting the integration and operationalisation of stakeholders demands and needs, which signal priority areas of action to further advance the development of renewable energy decision-support tools. [ABSTRACT FROM AUTHOR]

• Water decision-support tools can improve management of landscapes during drought. • We studied the use of these tools by agency staff and how they can be improved. • Accuracy, interpretability, capacity, scope, and institutional fit affected tool use. • Increased communication between tool developers and end-users could improve tools and encourage tool use. Climate is changing in ways that may significantly affect the provision of hydrologic ecosystem services in arid or semi-arid regions. To answer this challenge, there has been an effort to increase the adaptive capacity of organizations that manage water and the land-uses water supports. Governmental and non-governmental organizations (NGOs) managing large landscapes in the United States Northern Rockies region have access to a variety of water decision-support tools, such as indicators of precipitation and snowpack, which could increase their adaptive capacity to manage hydrologic ecosystem services under changing conditions. Yet little is known about the use of decision-support tools in this region and how tools could be improved. With the aim of informing future tool development and addressing information-use gaps, we conducted semi-structured interviews with representatives of federal and state agencies and NGOs to 1) identify which tools are being used, 2) describe tool-supported management actions across different types of organizations, and 3) determine "usability" criteria managers consider when adopting a climate tool. Through qualitative analysis, we found multiple types of tools being used, including processes and frameworks, data and models, and geospatial or web-based tools. We also identified several criteria that study participants used to assess whether or not to use a tool within their organization, including tool accuracy, robustness, extendibility, interpretability, capacity, and institutional fit. This study suggests that increased communication between tool developers and end-users, with a focus on tools' relevance and ability to support management actions, could improve tools and increase the adaptive capacity of users. This research also points to the need for multiple lines of future research including how to improve the fit between organizational goals and water tools. [ABSTRACT FROM AUTHOR]

• Pi-VAT is a post-processing, interactive visualization, and analysis tool. • Simple implementation to facilitate 'what-if' analysis using WEPP and SWAT outputs. • Suitable for assessing areas for targeted management. • Synthesize and visualize multiple watersheds and scenarios. • An approach to address the need for integrating extensive outputs from process-based models into decision support tools. Effective watershed management and protection of water resources from non-point source pollution require identification, prioritization, and targeting of pollutant source areas. Process-based hydrology and water quality models are powerful heuristic tools for land and water resources managers. However, because of their complexity, such models are often under-utilized as management prioritization and planning tools. In this paper, we present a prioritization, interactive visualization, and analysis tool (Pi-VAT) that is programmed to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models. We demonstrate the utility of Pi-VAT to examine simulated hydrologic, sediment, and water quality response at the hillslope/hydrologic response unit (HRU) scale. We apply Pi-VAT to output from multiple watersheds and for multiple management scenarios and treatments from two geospatial models for watershed management: Water Erosion Prediction Project (WEPP) and Soil & Water Assessment Tool (SWAT). Pi-VAT was developed using the Shiny web application framework for the R programming language. In a matter of minutes, Pi-VAT can synthesize overwhelming amounts of output from process-based models into information useful for land and water resources managers. We illustrate the use of Pi-VAT to interactively identify, quantify, and visualize areas that are most susceptible to disturbance under different scenarios and provide a synthesis approach based on land use, soil type, and slope steepness. This approach guides land and water resources managers in prioritizing the areas of the watershed that provide the maximum reduction in pollutant loads while treating the least amount of area. Pi-VAT provides a flexible reactive platform for the development of decision support tools based on process-based models intended for watershed management and research applications. [ABSTRACT FROM AUTHOR]

Conservation conflicts are increasing on a global scale and instruments for reconciling competing interests are urgently needed. Multicriteria decision analysis (MCDA) is a structured, decision-support process that can facilitate dialogue between groups with differing interests and incorporate human and environmental dimensions of conflict. MCDA is a structured and transparent method of breaking down complex problems and incorporating multiple objectives. The value of this process for addressing major challenges in conservation conflict management is that MCDA helps in setting realistic goals; entails a transparent decision-making process; and addresses mistrust, differing world views, cross-scale issues, patchy or contested information, and inflexible legislative tools. Overall we believe MCDA provides a valuable decision-support tool, particularly for increasing awareness of the effects of particular values and choices for working toward negotiated compromise, although an awareness of the effect of methodological choices and the limitations of the method is vital before applying it in conflict situations. Uso de Análisis de Decisiones Multicriterio para Abordar Conflictos de Conservación [ABSTRACT FROM AUTHOR]

Background: In many countries, the responsibility for planning and delivery of health services is devolved to the subnational level. Health programs, however, often fall short of efficient use of data to inform decisions. As a result, programs are not as effective as they can be at meeting the health needs of the populations they serve. In Kenya, a decision-support tool, the District Health Profile (DHP) tool was developed to integrate data from health programs, primarily HIV, at the district level and to enable district health management teams to review and monitor program progress for specific health issues to make informed service delivery decisions. Methods: Thirteen in-depth interviews were conducted with ten tool users and three non-users in six districts to qualitatively assess the process of implementing the tool and its effect on data-informed decision making at the district level. The factors that affected use or non-use of the tool were also investigated. Respondents were selected via convenience sample from among those that had been trained to use the DHP tool except for one user who was self-taught to use the tool. Selection criteria also included respondents from urban districts with significant resources as well as respondents from more remote, under-resourced districts. Results: Findings from the in-depth interviews suggest that among those who used it, the DHP tool had a positive effect on data analysis, review, interpretation, and sharing at the district level. The automated function of the tool allowed for faster data sharing and immediate observation of trends that facilitated data-informed decision making. All respondents stated that the DHP tool assisted them to better target existing services in need of improvement and to plan future services, thus positively influencing program improvement. Conclusions: This paper stresses the central role that a targeted decision-support tool can play in making data aggregation, analysis, and presentation easier and faster. The visual synthesis of data facilitates the use of information in health decision making at the district level of a health system and promotes program improvement. The experience in Kenya can be applied to other countries that face challenges making district-level, data-informed decisions with data from fragmented information systems. [ABSTRACT FROM AUTHOR]

Abstract: Multiple competing uses of continental-shelf environments have led to a proliferation of marine spatial planning initiatives, together with expert guidance on marine spatial planning. This study provides an empirical review of marine spatial plans, their attributes, and the extent to which the expert guidance is actually being followed. We performed a structured review of 16 existing marine spatial plans and created an idealized marine spatial plan from the steps included in recent expert papers. A cluster analysis of the yes/no answers to 28 questions was used to ordinate the 16 marine spatial plans and to compare them with the idealized plan. All the plans that have been implemented have a high-level government mandate and the authority to implement spatial planning vested in existing institutions. Almost all the plans used data with clear criteria for data inclusion. Stakeholders were included in almost all the plans; they did not participate in all stages of the planning process but their roles were generally clearly defined. Decision-support tools were applied inconsistently across plans and were seldom used dynamically over time. Most spatial planning processes did not select specific outcomes, such as preferred use scenarios. Success is defined inconsistently across plans; in half the cases there are no metrics of success with reference benchmarks. Although monitoring is included in the majority of plans, only in some cases do monitoring results feed back into management decisions. The process of marine spatial planning had advanced in that some of the more recent plans were developed more quickly and contain more desirable attributes than earlier plans. Even so, existing marine spatial plans are heterogeneous—there are essential ingredients, but no single recipe for success. [Copyright &y& Elsevier]

REEFGAME is a computer-assisted role-playing game that explores the interactions among management strategies, livelihood options, and ecological degradation in subsistence fishing communities. The tool has been successfully used in the Philippines and a variety of student workshops. In the field, REEFGAME operated as a two-way learning tool, helping local fishers better understand their collective impacts on the marine ecosystem and providing researchers with insights into fisher livelihood strategies. This demonstrates the game’s ability to serve as powerful springboard for social learning and discussion among stakeholders, while providing useful scientific insights into decision-making processes. Although not specifically designed for the purpose, REEFGAME has also been used in the university setting to facilitate student engagement and demonstrate a range of social and ecological concepts. [ABSTRACT FROM AUTHOR]

Murawski, S. A., Steele, J. H., Taylor, P., Fogarty, M. J., Sissenwine, M. P., Ford, M., and Suchman, C. 2010. Why compare marine ecosystems? – ICES Journal of Marine Science, 67: 1–9.Effective marine ecosystem-based management (EBM) requires understanding the key processes and relationships controlling the aspects of biodiversity, productivity, and resilience to perturbations. Unfortunately, the scales, complexity, and non-linear dynamics that characterize marine ecosystems often confound managing for these properties. Nevertheless, scientifically derived decision-support tools (DSTs) are needed to account for impacts resulting from a variety of simultaneous human activities. Three possible methodologies for revealing mechanisms necessary to develop DSTs for EBM are: (i) controlled experimentation, (ii) iterative programmes of observation and modelling (“learning by doing”), and (iii) comparative ecosystem analysis. We have seen that controlled experiments are limited in capturing the complexity necessary to develop models of marine ecosystem dynamics with sufficient realism at appropriate scales. Iterative programmes of observation, model building, and assessment are useful for specific ecosystem issues but rarely lead to generally transferable products. Comparative ecosystem analyses may be the most effective, building on the first two by inferring ecosystem processes based on comparisons and contrasts of ecosystem response to human-induced factors. We propose a hierarchical system of ecosystem comparisons to include within-ecosystem comparisons (utilizing temporal and spatial changes in relation to human activities), within-ecosystem-type comparisons (e.g. coral reefs, temperate continental shelves, upwelling areas), and cross-ecosystem-type comparisons (e.g. coral reefs vs. boreal, terrestrial vs. marine ecosystems). Such a hierarchical comparative approach should lead to better understanding of the processes controlling biodiversity, productivity, and the resilience of marine ecosystems. In turn, better understanding of these processes will lead to the development of increasingly general laws, hypotheses, functional forms, governing equations, and broad interpretations of ecosystem responses to human activities, ultimately improving DSTs in support of EBM. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we draw on the analytic perspectives of ethnomethodology to explore doctor–patient encounters in an experimental trial of a complex intervention: an efficacy randomised controlled trial (RCT) of decision-support tools in the UK. We show how the experimental context in which these encounters take place pervades the interactions within them. We argue that two interactional orders were at work in the encounters that we observed: (i) the ceremonial order of the consultation and (ii) the assemblage of the decision-support tool trial. We demonstrate how doctors in the trial oscillate between positions as authoritative clinician and neutralistic decision-support tool-implementer, and patients move between positions as passive recipients of clinical knowledge and as active subjects required to render their experience as calculable in terms of the demands of the decision-support tools and the broader trial they are embedded in. We demonstrate how the RCT coordinates the world of the clinical environment and the world of experimental evidence. [Copyright &y& Elsevier]

With the increasing corporate and governmental rationalisation of medical care, the mandate of efficiency has caused many to fear that concern for the individual patient will be replaced with impersonal, rule-governed allocation of medical resources. Largely ignored is the role of moral principles in medical decision-making. This analysis comes from an ethnographic study conducted from 1999–2001 in three US Intensive Care Units, two of which were using the computerised decision-support tool, APACHE III (Acute Physiological and Chronic Health Evaluation III), which notably predicts the probability that a patient will die. It was found that the use of APACHE presents a paradox regarding concern for the individual patient. To maintain jurisdiction over the care of patients, physicians share the data with the payers and regulators of care to prove they are using resources effectively and efficiently, yet they use the system in conjunction with moral principles to justify treating each patient as unique. Thus, concern for the individual patient is not lessened with the use of this system. However, physicians do not share the data with patients or surrogate decision-makers because they fear they will be viewed as more interested in profits than patients. [ABSTRACT FROM AUTHOR]

Phalaris minor Retz. (littleseed canarygrass) is the most problematic and herbicide-resistant weed in the rice-wheat cropping system in India. As such, it poses a severe threat to wheat yield and food security. A number of herbicidal and agronomic practices have been identified for the effective control of P. minor. These include crop rotation, crop establishment methods, herbicide spray technology, sowing time, weed seed harvest and effective herbicide mixtures. A population model of P. minor was built based on the life cycle of the species, herbicide resistance mechanisms and the effects of weed control practices. The model simulated the interactions of these factors and provided the best management recommendations for sustainably controlling this noxious weed species. Model results indicate that integration of chemical and non-chemical control methods was the most effective and sustainable strategy. For example, the integration of a happy seeder (a tractor-mounted mulching and sowing machine) with an effective post-emergence herbicide reduced the probability of weed control failure by 32% compared to the scenario with a rotavator and the same herbicide. Similarly, more conventional crop establishment methods such as a rotavator and conventional tillage could be accompanied by pre- or post-emergence applications of herbicide mixtures. Adoption of good herbicide spray technology and weed seed harvest delayed the onset of resistance evolution by up to four years. Furthermore, effective crop rotation such as the inclusion of sugarcane in place of rice in the summer season reduced the risk of resistance evolution by 31% within the 10 year simulation period. In addition to the scenarios using representative parameter values, the variability of model predictions was investigated based on some field experiments. The model provided a powerful tool for promoting Integrated Weed Management and the sustainable use of herbicides. Pragmatic ways of dealing with uncertainty in model prediction are discussed. [ABSTRACT FROM AUTHOR]

Urban planning stakeholders can use decision-support tools (DST) to foster transformation towards sustainable urban morphologies. This paper proposes an analytical framework to support urban planning practitioners in assessing how the use of DST might impact planning outcomes due to path dependence. We identify five key dimensions of path dependence in a spatial socio-technical system as analytical framework to assess the influence of DST on planning outcomes. Potential impacts on urban planning outcomes are analysed by applying the proposed framework to a particular spatial socio-technical system, namely New Zealand's use of DST to support urban planning decision-making towards sustainable urban morphologies. The assessment framework and comparative case study analysis illustrate how the interaction between planning culture and some DST features can influence decisions pertaining to urban morphologies and raise awareness about DST-induced path dependence. • How can decision-support tool (DST) use impact urban outcomes due to path dependence? • An analytical framework to assess potential path dependence through DST use • Case study application of this framework to a spatial socio-technical system • Call for critical evaluation of practices related to choices and use of DST • Urban transformation requires awareness and mitigation of path dependence. [ABSTRACT FROM AUTHOR]

In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making. [ABSTRACT FROM AUTHOR]