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1. Earlier declines in marine resources, combined with current fishing pressures and devastating coral mortality in 2015, have resulted in a degraded coral reef ecosystem state at Puakō in West Hawai'i. Changes to resource management are needed to facilitate recovery of ecosystem functions and services. 2. We developed a customised ecosystem model to evaluate the performance of alternative management scenarios at Puakō in the provisioning of ecosystem services to human users (marine tourists, recreational fishers) and enhancing the reef's ability to recover from pressures (resilience). 3. Outcomes of the continuation of current management plus five alternative management scenarios were compared under both high and low coral-bleaching related mortality over a 15-year time span. 4. Current management is not adequate to prevent further declines in marine resources. Fishing effort is already above the multispecies sustainable yield, and, at its current level, will likely lead to a shift to algal-dominated reefs and greater abundance of undesirable fish species. Scenarios banning all gears other than line fishing, or prohibiting take of herbivorous fishes, were most effective at enhancing reef structure and resilience, dive tourism, and the recreational fishery. Allowing only line fishing generated the most balanced trade-off between stakeholders, with positive gains in both ecosystem resilience and dive tourism, while only moderately decreasing fishery value within the area. 5. Synthesis and applications. Our customised ecosystem model projects the impacts of multiple, simultaneous pressures on a reef ecosystem. Trade-offs of alternative approaches identified by local managers were quantified based on indicators for different ecosystem services (e.g. ecosystem resilience, recreation, food). This approach informs managers of potential conflicts among stakeholders and provides guidance on approaches that better balance conservation objectives and stakeholders' interests. Our results indicate that a combination of reducing land-based pollution and allowing only line fishing generated the most balanced trade-off between stakeholders and will enhance reef recovery from the detrimental effects of coral bleaching events that are expected over the next 15 years.

The freshwater Fish Invasiveness Screening Kit (FISK) has been applied in 35 risk assessment areas in 45 countries across the six inhabited continents (11 applications using FISK v1; 25 using FISK v2). The present study aimed: to assess the breadth of FISK applications and the confidence (certainty) levels associated with the decision-support tool’s 49 questions and its ability to distinguish between taxa of low-to-medium and high risk of becoming invasive, and thus provide climate-specific, generalised, calibrated thresholds for risk level categorisation; and to identify the most potentially invasive freshwater fish species on a global level. The 1973 risk assessments were carried out by 70 + experts on 372 taxa (47 of the 51 species listed as invasive in the Global Invasive Species Database www.iucngisd.org/gisd/), which in decreasing order of importance belonged to the taxonomic Orders Cypriniformes, Perciformes, Siluriformes, Characiformes, Salmoniformes, Cyprinodontiformes, with the remaining ≈ 8% of taxa distributed across an additional 13 orders. The most widely-screened species (in decreasing importance) were: grass carp Ctenopharyngodon idella, common carp Cyprinus carpio, rainbow trout Oncorhynchus mykiss, silver carp Hypophthalmichthys molitrix and topmouth gudgeon Pseudorasbora parva. Nine ‘globally’ high risk species were identified: common carp, black bullhead Ameiurus melas, round goby Neogobius melanostomus, Chinese (Amur) sleeper Perccottus glenii, brown bullhead Ameiurus nebulosus, eastern mosquitofish Gambusia holbrooki, largemouth (black) bass Micropterus salmoides, pumpkinseed Lepomis gibbosus and pikeperch Sander lucioperca. The relevance of this global review to policy, legislation, and risk assessment and management procedures is discussed.

ABSTRACT Ecosystems worldwide continue to experience rapid rates of habitat and species loss. Management actions to conserve and restore functional habitats are needed to reduce these declines, but funding and resources for such actions are limited. Spatial conservation prioritization (SCP) can facilitate strategic decision‐making for targeted conservation planning and delivery, but complexities arise when management objectives include multiple wildlife species and ecological or management constraints, all of which can be further complicated by data uncertainty and existing conservation plans. The Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET), an R package‐based decision‐support tool, supports strategic ecosystem management planning across the sagebrush biome by using SCP. We adapted PReSET to better address the needs of multiple wildlife species, evaluate the effects of different ecological or management constraints on conservation outcomes, assess the influence of data uncertainty, and integrate existing conservation plans. Specifically, we developed optimization problems to identify priority sagebrush protection and restoration across the state of Wyoming, USA, and evaluated the efficacy and trade‐offs of various approaches to problem design. We evaluated trade‐offs in targeting multiple species compared to a single species, including using greater sage‐grouse as a potential umbrella species to benefit other sagebrush‐dependent wildlife. We then evaluated multi‐species protection and restoration problems aimed at minimizing the risks of inadequate connectivity, climate change, and restoration failure, and accounted for data uncertainty to assess relationships between risk aversion of managers and conservation outcomes. We also developed optimization problems within conservation areas identified by an existing sagebrush conservation plan to evaluate the efficacy of guiding local‐scale conservation delivery within more broadly defined conservation areas. Our results demonstrate how SCP methods can leverage novel spatial data to develop targeted decision‐support resources that can facilitate landscape conservation planning and improve management outcomes across a wide array of systems and species. Management actions to conserve and restore functional habitats are needed to reduce habitat loss and species declines, but funding and resources for such actions are limited. The Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) supports strategic ecosystem management planning across the sagebrush biome by using spatial conservation prioritization. We adapted PReSET to better address the needs of multiple wildlife species, evaluate the effects of different ecological or management constraints on conservation outcomes, assess the influence of data uncertainty, and integrate existing conservation plans.

Childhood tumors in the central nervous system (CNS) have longer diagnostic delays than other pediatric tumors. Vague presenting symptoms pose a challenge in the diagnostic process; it has been indicated that patients and parents may be hesitant to seek help, and health care professionals (HCPs) may lack awareness and knowledge about clinical presentation. To raise awareness among HCPs, the Danish CNS tumor awareness initiative hjernetegn.dk was launched. This study aims to present the learnings from designing and implementing a decision support tool for HCPs to reduce diagnostic delay in childhood CNS tumors. The aims also include decisions regarding strategies for dissemination and use of social media, and an evaluation of the digital impact 6 months after launch. The phases of developing and implementing the tool include participatory co-creation workshops, designing the website and digital platforms, and implementing a press and media strategy. The digital impact of hjernetegn.dk was evaluated through website analytics and social media engagement. hjernetegn.dk was launched in August 2023. The results after 6 months exceeded key performance indicators. The analysis showed a high number of website visitors and engagement, with a plateau reached 3 months after the initial launch. The LinkedIn campaign and Google Search strategy also generated a high number of impressions and clicks. The findings suggest that the initiative has been successfully integrated, raising awareness and providing a valuable tool for HCPs in diagnosing childhood CNS tumors. The study highlights the importance of interdisciplinary collaboration, co-creation, and ongoing community management, as well as broad dissemination strategies when introducing a digital support tool.

Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland ecosystems to compensate for unavoidable environmental damage from anthropogenic developments such as transportation infrastructure, urbanization, and energy development. Pursuit of energy independence in the United States will expand domestic energy production. Concurrent with this increased growth is increased disruption to wildlife habitats, including avian displacement from suitable breeding habitat. Recent studies at energy-extraction and energy-generation facilities have provided evidence for behavioral avoidance and thus reduced use of habitat by breeding waterfowl and grassland birds in the vicinity of energy infrastructure. To quantify and compensate for this loss in value of avian breeding habitat, it is necessary to determine a biologically based currency so that the sufficiency of offsets in terms of biological equivalent value can be obtained. We describe a method for quantifying the amount of habitat needed to provide equivalent biological value for avifauna displaced by energy and transportation infrastructure, based on the ability to define five metrics: impact distance, impact area, pre-impact density, percent displacement, and offset density. We calculate percent displacement values for breeding waterfowl and grassland birds and demonstrate the applicability of our avian-impact offset method using examples for wind and oil infrastructure. We also apply our method to an example in which the biological value of the offset habitat is similar to the impacted habitat, based on similarity in habitat type (e.g., native prairie), geographical location, land use, and landscape composition, as well as to an example in which the biological value of the offset habitat is dissimilar to the impacted habitat. We provide a worksheet that informs potential users how to apply our method to their specific developments and a framework for developing decision-support tools aimed at achieving landscape-level conservation goals.

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. Los conflictos de conservación están incrementando en una escala global y se necesitan urgentemente instrumentos para reconciliar los intereses en competencia. El análisis de decisiones multicriterio (ADMC) es un proceso estructurado de apoyo a toma de decisiones que puede facilitar el diálogo entre grupos con intereses contrastantes e incorporar las dimensiones humanas y ambientales del conflicto. ADMC es un método estructurado y transparente de descomposición de problemas complejos e incorporación de objetivos múltiples. El valor de este proceso para efrentar a grandes obstáculos en el manejo de conflictos de conservación es que ADMC ayuda en la definición de metas reales, implica un proceso transparente de toma de decisiones y atiende a la desconfianza, visiones mundiales diferentes, asuntos transescalares, información turbia o impugnada y herramientas legislativas inflexibles. En general creemos que ADMC proporciona una herramienta valiosa de apoyo a la toma de decisiones, particularmente para incrementar la conciencia de los efectos de valores particulares y opciones para trabajar hacia compromisos negociados, aunque una conciencia de los efectos de las opciones metodológicas y las limitaciones del método es vital antes de aplicarlo en situaciones de conflictos.

Altered disturbance regimes and shifting climates have increased the need for large‐scale restoration treatments across the western United States. Seed‐sourcing remains a considerable challenge for revegetation efforts, particularly on public lands where policy favors the use of native, locally sourced plant material to avoid maladaptation. An important area of emphasis for public agencies has been the development of spatial tools to guide selection of genetically appropriate seed. When genetic information is not available, current seed transfer guidelines stipulate use of climate‐based or provisional seed transfer zones, which serve as a proxy for local adaptation by representing climate gradients to which plants are commonly adapted. Despite this guidance, little emphasis has been placed on identifying best practices for deriving provisional seed zones or on incorporating predictions from future climate. We describe a flexible, multivariate procedure for deriving such zones that incorporates a broad range of climatic characteristics while accounting for covariation among climate variables. With this approach, we derive provisional seed zones for four regions in the Desert Southwest (the Mojave Desert, Sonoran Desert, Colorado Plateau, and Southern Great Basin). To facilitate future‐resilient restoration designs, we project each zone into its relative position in the future climate based on near‐term, RCP4.5 and RCP8.5 emissions scenarios. Although provisional seed zones are useful in a variety of contexts, there are also situations in which site‐specific guidance is preferable. To meet this need, we implement Climate Distance Mapper, an interactive decision‐support tool designed to help practitioners match seed sources with restoration sites through an accessible online interface. The application allows users to rank the suitability of seed sources anywhere on the landscape based on multivariate climate distances. Users can perform calculations for either the current or future climates. Additionally, tools are available to guide sample effort in regional‐scale seed collections or to partition the landscape into climate clusters representing suitable planting sites for different seed sources. Our tools and analytic procedures represent a flexible and reproducible framework for advancing native plant development programs in the Desert Southwest and beyond.

Environmental managers and planners have become increasingly enthusiastic about the potential of decision support tools (DSTs) to improve environmental decision-making processes as information technology transforms many aspects of daily life. Discussions about DSTs, however, rarely recognize the range of ways software can influence users’ negotiation, problem-solving, or decision-making strategies and incentives, in part because there are few empirical studies of completed processes that used technology. This mixed-methods study—which draws on data from approximately 60 semi-structured interviews and an online survey—examines how one geospatial DST influenced participants’ experiences during a multi-year marine planning process in California. Results suggest that DSTs can facilitate communication by creating a common language, help users understand the geography and scientific criteria in play during the process, aid stakeholders in identifying shared or diverging interests, and facilitate joint problem solving. The same design features that enabled the tool to aid in decision making, however, also presented surprising challenges in certain circumstances by, for example, making it difficult for participants to discuss information that was not spatially represented on the map-based interface. The study also highlights the importance of the social context in which software is developed and implemented, suggesting that the relationship between the software development team and other participants may be as important as technical software design in shaping how DSTs add value. The paper concludes with considerations to inform the future use of DSTs in environmental decision-making processes.

Sustainable housing and buildings constitute a fundamental part of the future urban fabric. This study aims at clarifying how different actors employ parameters of sustainability in building design and what enables the holistic perspective of the interrelating social, economic and environmental parameters. Interviews with building developers and designers show that decision support tools are used late in the design process and commonly focused on single parameters of sustainability. The analysis shows how practitioners of the planning and early design phases operate at general levels of geometrical clusters and volumes but must continuously evaluate each project from the perspective of the specifications of end-users and the public, to ensure holistic sustainability. This opposing relationship between need and availability of general and specific data, however, challenges the implementation of holistic sustainability. Advancing the interdisciplinary, holistic building design requires systematic aggregation of data from executed projects of this data into applicable rules-of-thumb. In parallel, future tools for simulation and dialogue must employ a broader scope of sustainability parameters. The conceptual frameworks of data and tools presented in this study can be used as a backdrop for developing sectoral initiatives to enable holistic decisions in the early stages of sustainable building design.

During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients' chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients' data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning-based clinical decision support tools.