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Background: Artificial intelligence (AI) can be a tool in the diagnosis and acquisition of knowledge, particularly in dentistry, sparking debates on its application in clinical decision-making., Objective: This study aims to evaluate the accuracy, completeness, and reliability of the responses generated by Chatbot Generative Pre-Trained Transformer (ChatGPT) 3.5 in dentistry using expert-formulated questions., Materials and Methods: Experts were invited to create three questions, answers, and respective references according to specialized fields of activity. The Likert scale was used to evaluate agreement levels between experts and ChatGPT responses. Statistical analysis compared descriptive and binary question groups in terms of accuracy and completeness. Questions with low accuracy underwent re-evaluation, and subsequent responses were compared for improvement. The Wilcoxon test was utilized (α = 0.05)., Results: Ten experts across six dental specialties generated 30 binary and descriptive dental questions and references. The accuracy score had a median of 5.50 and a mean of 4.17. For completeness, the median was 2.00 and the mean was 2.07. No difference was observed between descriptive and binary responses for accuracy and completeness. However, re-evaluated responses showed a significant improvement with a significant difference in accuracy (median 5.50 vs. 6.00; mean 4.17 vs. 4.80; p=0.042) and completeness (median 2.0 vs. 2.0; mean 2.07 vs. 2.30; p=0.011). References were more incorrect than correct, with no differences between descriptive and binary questions., Conclusions: ChatGPT initially demonstrated good accuracy and completeness, which was further improved with machine learning (ML) over time. However, some inaccurate answers and references persisted. Human critical discernment continues to be essential to facing complex clinical cases and advancing theoretical knowledge and evidence-based practice., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Institutional Research Ethics Committee issued approval 69712923.6.0000.5419. "The research project was approved". Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Molena et al.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Maintaining ecological integrity is globally acknowledged as a strategic goal, yet there is no consensus on a practical and widely usable methodology to assess it. This study proposes a comprehensive approach to quantify regional ecosystem integrity based on FAIR data, obtained using satellite remote sensing and image analysis. Three variables are central to this approach: normalized difference vegetation index (NDVI), at-satellite brightness temperature (BT) and vegetation surface heterogeneity (HG), corresponding to ecosystem integrity indicators exergy capture, biotic water flows and abiotic heterogeneity. The indicators are assessed across the vegetation period and a representative Regional Index of Ecological Integrity (RIEI) is proposed to express the integrity of two case study areas and representative land use types. The proposed approach proved powerful in representing the anthropogenic and autopoietic gradient within study regions in high detail. Arable lands and urban areas ranked lowest, while dense forests and wetlands highest, agriculture being the most significant factor reducing regional integrity. Areas with conservation significance ranked either having the highest integrity, when dense vegetation was present, and mediocre or even low in case of e.g., sand dunes, marches and rock formations. Limitations of the method comprise: insufficient representation of biodiversity, sensitivity to cloud cover and demanding in-situ validation. The approach can be scaled from global to local level, adapted to various remote sensing techniques and complemented by a diversity of data (e.g., ecosystem services, geomorphological, climatic) to provide deeper understanding of landscape ecosystem integrity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Background and Purpose: Volumetric modulated arc therapy (VMAT) planning for head and neck cancer is a complex process. While the lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori , artificial intelligence (AI) holds promise as a tool to accurately estimate the expected dose distribution for OARs. We prospectively investigated the benefits of incorporating an AI-based decision support tool (DST) into the clinical workflow to improve OAR sparing., Materials and Methods: The DST dose prediction model was based on 276 institutional VMAT plans. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD). The DST then estimated OAR doses (AI directive, AD). For each OAR, the treating physician used the lower directive to form a hybrid directive (HD). The final plan metrics were compared to each directive. A dose difference of 3 Gray (Gy) was considered clinically significant., Results: Compared to the AD and PD, the HD reduced OAR dose objectives by more than 3 Gy in 22% to 75% of cases, depending on OAR. The resulting clinical plan typically met these lower constraints and achieved mean dose reductions between 4.3 and 16 Gy over the PD, and 5.6 to 9.1 Gy over the AD alone. Dose metrics achieved using the HD were significantly better than institutional historical plans for most OARs and NRG constraints for all OARs., Conclusions: The DST facilitated a significantly improved treatment directive across all OARs for this generalized H&N patient cohort, with neither the AD nor PD alone sufficient to optimally direct planning., Competing Interests: Colin Carpenter: Payment and financial relationship with Siris Medical, including ownership of a patent related to work. Marc Nash: Payment and financial relationship with Siris Medical., (© 2021 The Authors.)

Purpose: To develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy., Methods: From a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined., Results: Feature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods - AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%)., Conclusion: The presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation., Competing Interests: Co-author MR has ownership interest in Okolo Health, a brachytherapy company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Stenhouse, Roumeliotis, Banerjee, Yanushkevich and McGeachy.)

Peer support specialists (i.e., lay interventionists representing one of the fastest-growing mental health workforce) are increasingly using technologies to support individuals with mental health challenges between clinical encounters. The use of technology by peers has been significantly increased During COVID-19. Despite the wide array of technologies available, there is no framework designed specifically for peer support specialists and service users to select technologies to support their personal recovery. The objective of the study was to develop a Decision-Support Tool for Peer Support Specialists and Service Users to facilitate shared decision-making when choosing technologies to support personal recovery. The study used an iterative co-production process, including item formulation and a series of group cognitive interviews with peer support specialists and service users ( n =9; n =9, n =4). The total sample included 22 participants: peer support specialists ( n =18, 81.8%) and service users ( n =4, 18.2%). The final version of the Decision-Support Tool for Peer Support Specialists and Service Users (D-SPSS) , includes 8 domains: (1) privacy and security; (2) cost; (3) usability; (4) accessibility; (5) inclusion and equity; (6) recovery principles; (7) personalized for service users' needs; and (8) device set-up. Our study found that involving peer support specialists and service users in the design and co-production phase of a decisionsupport tool is feasible and has the potential to empower both peer support specialists and service users, and potentially increase engagement in the use of technologies that support individuals' recovery from traditional clinical encounters.

The purpose of analyzing data is to transform it into useful knowledge. Descriptive analytics renders factual information about research and events that can be used to relate an organization's environment to its activities. However, descriptive analytics alone is not enough to gain understanding and possibly predict the future. Minding only the output of such an analysis can mislead the researcher and decisionmaker. Because many factors influence results, it is essential to advance the prediction of future challenges through statistical analytics and factual patterns that dictate the environment with scientifically tested models. The data patterns, types of analysis, and attributes the prediction will be based on are all important. Data influenced by unforeseen variables make for poor predictions, such as the evening capacity report data in this study., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2020, Ma et al.)

Background: Ministries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative., Methods: Three electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making., Results: Data from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator., Conclusions: Our experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology., Competing Interests: Competing interestsThe opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. The authors report no conflicts of interest., (© The Author(s) 2020.)

Population viability analysis (PVA) is a powerful conservation tool, but it remains impractical for many species, particularly species with multiple, broadly distributed populations for which collecting suitable data can be challenging. A recently developed method of multiple-population viability analysis (MPVA), however, addresses many limitations of traditional PVA. We built on previous development of MPVA for Lahontan cutthroat trout (LCT) (Oncorhynchus clarkii henshawi), a species listed under the U.S. Endangered Species Act, that is distributed broadly across habitat fragments in the Great Basin (U.S.A.). We simulated potential management scenarios and assessed their effects on population sizes and extinction risks in 211 streams, where LCT exist or may be reintroduced. Conservation populations (those managed for recovery) tended to have lower extinction risks than nonconservation populations (mean = 19.8% vs. 52.7%), but not always. Active management or reprioritization may be warranted in some cases. Eliminating non-native trout had a strong positive effect on overall carrying capacities for LCT populations but often did not translate into lower extinction risks unless simulations also reduced associated stochasticity (to the mean for populations without non-native trout). Sixty fish or 5-10 fish/km was the minimum reintroduction number and density, respectively, that provided near-maximum reintroduction success. This modeling framework provided crucial insights and empirical justification for conservation planning and specific adaptive management actions for this threatened species. More broadly, MPVA is applicable to a wide range of species exhibiting geographic rarity and limited availability of abundance data and greatly extends the potential use of empirical PVA for conservation assessment and planning., (© 2019 Society for Conservation Biology.)

Soil improvement measures need to be ecologically credible, socially acceptable and economically affordable if they are to enter widespread use. However, in real world decision contexts not all measures can sufficiently meet these criteria. As such, developing, selecting and using appropriate tools to support more systematic appraisal of soil improvement measures in different decision-making contexts represents an important challenge. Tools differ in their aims, ranging from those focused on appraising issues of cost-effectiveness, wider ecosystem services impacts and adoption barriers/opportunities, to those seeking to foster participatory engagement and social learning. Despite the growing complexity of the decision-support tool landscape, comprehensive guidance for selecting tools that are best suited to appraise soil improvement measures, as well as those well-adapted to enable participatory deployment, has generally been lacking. We address this gap using the experience and survey data from an EU-funded project (RECARE: Preventing and REmediating degradation of soils in Europe through land CARE). RECARE applied different socio-cultural, biophysical and monetary appraisal tools to assess the costs, benefits and adoption of soil improvement measures across Europe. We focused on these appraisal tools and evaluated their performance against three broad attributes that gauge their differences and suitability for widespread deployment to aid stakeholder decision making in soil management. Data were collected using an online questionnaire administered to RECARE researchers. Although some tools worked better than others across case studies, the information collated was used to provide guiding strategies for choosing appropriate tools, considering resources and data availability, characterisation of uncertainty, and the purpose for which a specific soil improvement measure is being developed or promoted. This paper provides insights to others working in practical soil improvement contexts as to why getting the tools right matters. It demonstrates how use of the right tools can add value to decision-making in ameliorating soil threats, supporting the sustainable management of the services that our soil ecosystems provide., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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., (Published 2019. This article is a U.S. Government work and is in the public domain in the USA. Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.)

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., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

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.

Developments in amyotrophic lateral sclerosis research and care delivery have created new arenas, and new dilemmas, for patients' decision making. This review explores three aspects of amyotrophic lateral sclerosis patient-centered care and decision making: patient-centered service delivery through the expanding multidisciplinary team; decision making for genetic testing and the implications of undergoing testing; and development of user-designed decision support tools to help patients and families make decisions as their choices become more complex. Until a cure is found, well-timed and effective decision making will rely on patient and family preferences to guide them through an increasingly complicated disease landscape.

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.

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., (© 2013 Society for Conservation Biology.)

The climatic conditions of north temperate countries pose unique influences on the rates of invasion and the potential adverse impacts of non-native species. Methods are needed to evaluate these risks, beginning with the pre-screening of non-native species for potential invasives. Recent improvements to the Fish Invasiveness Scoring Kit (FISK) have provided a means (i.e., FISK v2) of identifying potentially invasive non-native freshwater fishes in virtually all climate zones. In this study, FISK is applied for the first time in a north temperate country, southern Finland, and calibrated to determine the appropriate threshold score for fish species that are likely to pose a high risk of being invasive in this risk assessment area. The threshold between "medium" and "high" risk was determined to be 22.5, which is slightly higher than the original threshold for the United Kingdom (i.e., 19) and that determined for a FISK application in southern Japan (19.8). This underlines the need to calibrate such decision-support tools for the different areas where they are employed. The results are evaluated in the context of current management strategies in Finland regarding non-native fishes., (© 2013 Society for Risk Analysis.)