Portfolio Decision Analysis Framework for Value-Focused Ecosystem Management

Reframing wildlife disease management problems with decision analysis

Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade-offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision-making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision-making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision-making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white-nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision-making, including competing objectives, risks, prediction uncertainty, and limited resources.

A portfolio decision analysis approach for selecting a subset of interdependent actions: The case of a regional climate roadmap in Finland

In this paper, we present a structured approach based on portfolio decision analysis to support the consideration of interdependencies between actions (i.e. interactions) in the selection of an efficient portfolio. One of the main challenges in modelling interactions is that the possible number of them between the pairs of actions increases exponentially with the number of actions. In environmental management, the problems can include tens of possible actions potentially leading to hundreds of pairwise interactions between them. For example, a strategy for mitigating climate change can consist of various actions in different sectors for improving technology, reducing emissions and the sequestration of carbon. Our approach aims to reduce the burden of assessing interactions by initially selecting a shortlist of actions based on specific heuristics and focusing on modelling interactions exclusively within this chosen set of actions. Another feature of the approach is the use of holistic evaluation of interactions to further reduce the cognitive load of stakeholders making the assessment. As a possible disadvantage, these features may increase the imprecision related to the results of the model. To analyse the impacts of this imprecision, we propose a way to carry out sensitivity analysis on the basis of how intensively the interactions would be taken into account in the modelling. The applicability of the approach was tested in a case related to the roadmap to a carbon neutral North Savo region in Finland by the year 2035. The approach helped to better understand synergies and trade-offs when putting the actions of the roadmap into practice, which is expected to lead to better results in terms of preparedness and adaptation to climate change.

Integrating principles and tools of decision science into value-driven watershed planning for compensatory mitigation

Several environmental policies strive to restore impaired ecosystems and could benefit from a consistent and transparent process-codeveloped with key stakeholders-to prioritize impaired ecosystems for restoration activities. The Clean Water Act, for example, establishes reallocation mechanisms to transfer ecosystem services from sites of disturbance to compensation sites to offset aquatic resource functions that are unavoidably lost through land development. However, planning for the prioritization of compensatory mitigation areas is often hampered by decision-making processes that fall into a myopic decision frame because they are not coproduced with stakeholders. In this study, we partnered with domain experts from the North Carolina Division of Mitigation Services to codevelop a real-world decision framework to prioritize catchments by potential for the development of mitigation projects following principles of a structured decision-making process and knowledge coproduction. Following an iterative decision analysis cycle, domain experts revised foundational components of the decision framework and progressively added complexity and realism as they gained additional insights or more information became available. Through the course of facilitated in-person and remote interactions, the codevelopment of a decision framework produced three main "breakthroughs" from the perspective of the stakeholder group: (a) recognition of the problem as a multiobjective decision driven by several values in addition to biogeophysical goals (e.g., functional uplift, restoring or enhancing lost functionality of ecosystems); (b) that the decision comprises a linked and sequential planning-to-implementation process; and (c) future risk associated with land-use and climate change must be considered. We also present an interactive tool for "on-the-fly" assessment of alternatives and tradeoff analysis, allowing domain experts to quickly test, react to, and revise prioritization strategies. The decision framework described in this study is not limited to the prioritization of compensatory mitigation activities across North Carolina but rather serves as a framework to prioritize a wide range of restoration, conservation, and resource allocation activities in similar environmental contexts across the nation.

Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts

Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics - underpinning persistent criticality and neutrality or independence among areas - is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.

Multicriteria decisions and portfolio analysis: land acquisition for biological and social objectives

Resource allocation for land acquisition is a common multiobjective problem that involves complex trade-offs. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service currently uses the Targeted Resource Acquisition Comparison Tool (TRACT) to allocate funds from the Migratory Bird Conservation Fund (MBCF; established through the Migratory Bird Hunting and Conservation Act of 1934) for land acquisition based on cost-benefit analysis, regional priority rankings of candidate land parcels available for acquisition, and the overall biological contribution to duck population objectives. However, current policy encourages decision makers to consider societal and economic benefits of lands acquired, in addition to their biological benefits to waterfowl. These decisions about portfolio elements (i.e., individual land parcels) require an analysis of the difficult trade-offs among multiple objectives. In the last decade the application of multicriteria decision analysis (MCDA) methods has been instrumental in aiding decision makers with complex multiobjective decisions. In this study, we present an alternative approach to developing land-acquisition portfolios using MCDA and modern portfolio theory (MPT). We describe the development of a portfolio decision analysis tool using constrained optimization for land-acquisition decisions by the NWRS. We outline the decision framework, describe development of the prototype tool in Microsoft Excel, and test the results of the tool using land parcels submitted as candidates for MBCF funding in 2019. Our results indicate that the constrained optimization outperformed the traditional TRACT method and ad hoc portfolios developed using current NWRS criteria.

Anthropogenic factors associated with contaminants of emerging concern detected in inland Minnesota lakes (Phase II)

Contaminants of emerging concern (CECs) include a variety of pharmaceuticals, personal care products, and hormones commonly detected in surface waters. Human activities, such as wastewater treatment and discharge, contribute to the distribution of CECs in water, but other sources and pathways are less frequently examined. This study aimed to identify anthropogenic activities and environmental characteristics associated with the presence of CECs, previously determined to be of high priority for further research and mitigation, in rural inland lakes in northeastern Minnesota, United States. The setting for this study consisted of 21 lakes located within both the Grand Portage Indian Reservation and the 1854 Ceded Territory, where subsistence hunting and fishing are important to the cultural heritage of the indigenous community. We used data pertaining to numbers of buildings, healthcare facilities, wastewater treatment plants, impervious surfaces, and wetlands within defined areas surrounding the lakes as potential predictors of the detection of high priority CECs in water, sediment, and fish. Separate models were run for each contaminant detected in each sample media. We used least absolute shrinkage and selection operator (LASSO) models to account for both predictor selection and parameter estimation for CEC detection. Across contaminants and sample media, the percentage of impervious surface was consistently positively associated with CEC detection. Number of buildings in the surrounding area was often negatively associated with CEC detection, though nonsignificant. Surrounding population, presence of wastewater treatment facilities, and percentage of wetlands in surrounding areas were positively, but inconsistently, associated with CECs, while catchment area and healthcare centers were generally not associated. The results of this study highlight human activities and environmental characteristics associated with CEC presence in a rural area, informing future work regarding specific sources and transport pathways. We also demonstrate the utility of LASSO modeling in the identification of these important relationships.

A chemical prioritization process: Applications to contaminants of emerging concern in freshwater ecosystems (Phase I)

Contaminants of emerging concern (CECs), such as pharmaceuticals, personal care products, and hormones, are frequently found in aquatic ecosystems around the world. Information on sublethal effects from exposure to commonly detected concentrations of CECs is lacking and the limited availability of toxicity data makes it difficult to interpret the biological significance of occurrence data. However, the ability to evaluate the effects of CECs on aquatic ecosystems is growing in importance, as detection frequency increases. The goal of this study was to prioritize the chemical hazards of 117 CECs detected in subsistence species and freshwater ecosystems on the Grand Portage Indian Reservation and adjacent 1854 Ceded Territory in Minnesota, USA. To prioritize CECs for management actions, we adapted Minnesota Pollution Control Agency's Aquatic Toxicity Profiles framework, a tool for the rapid assessment of contaminants to cause adverse effects on aquatic life by incorporating chemical-specific information. This study aimed to 1) perform a rapid-screening assessment and prioritization of detected CECs based on their potential environmental hazard; 2) identify waterbodies in the study region that contain high priority CECs; and 3) inform future monitoring, assessment, and potential remediation in the study region. In water samples alone, 50 CECs were deemed high priority. Twenty-one CECs were high priority among sediment samples and seven CECs were high priority in fish samples. Azithromycin, DEET, diphenhydramine, fluoxetine, miconazole, and verapamil were high priority in all three media. Due to the presence of high priority CECs throughout the study region, we recommend future monitoring of particular CECs based on the prioritization method used here. We present an application of a chemical hazard prioritization process and identify areas where the framework may be adapted to meet the objectives of other management-related assessments.

COVID-19 non-pharmaceutical intervention portfolio effectiveness and risk communication predominance

Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide. Different countries implemented their own combinations of NPIs to prevent local epidemics and healthcare system overloaded. Portfolios, as temporal sets of NPIs have various systemic impacts on preventing cases in populations. Here, we developed a probabilistic modeling framework to evaluate the effectiveness of NPI portfolios at the macroscale. We employed a deconvolution method to back-calculate incidence of infections and estimate the effective reproduction number by using the package EpiEstim. We then evaluated the effectiveness of NPIs using ratios of the reproduction numbers and considered them individually and as a portfolio systemically. Based on estimates from Japan, we estimated time delays of symptomatic-to-confirmation and infection-to-confirmation as 7.4 and 11.4 days, respectively. These were used to correct surveillance data of other countries. Considering 50 countries, risk communication and returning to normal life were the most and least effective yielding the aggregated effectiveness of 0.11 and - 0.05 that correspond to a 22.4% and 12.2% reduction and increase in case growth. The latter is quantified by the change in reproduction number before and after intervention implementation. Countries with the optimal NPI portfolio are along an empirical Pareto frontier where mean and variance of effectiveness are maximized and minimized independently of incidence levels. Results indicate that implemented interventions, regardless of NPI portfolios, had distinct incidence reductions and a clear timing effect on infection dynamics measured by sequences of reproduction numbers. Overall, the successful suppression of the epidemic cannot work without the non-linear effect of NPI portfolios whose effectiveness optimality may relate to country-specific socio-environmental factors.

Occurrence of contaminants of emerging concern in aquatic ecosystems utilized by Minnesota tribal communities

Pharmaceuticals, personal care products, hormones, and other chemicals lacking water quality standards are frequently found in surface water. While evidence is growing that these contaminants of emerging concern (CECs) - those previously unknown, unrecognized, or unregulated - can affect the behavior and reproduction of fish and wildlife, little is known about the distribution of these chemicals in rural, tribal areas. Therefore, we surveyed the presence of CECs in water, sediment, and subsistence fish species across various waterbodies, categorized as undeveloped (i.e., no human development along shorelines), developed (i.e., human development along shorelines), and wastewater effluent-impacted (i.e., contain effluence from wastewater treatment plants), within the Grand Portage Indian Reservation and 1854 Ceded Territory in northeastern Minnesota, U.S.A. Overall, in 28 sites across three years (2016-2018), 117 of the 158 compounds tested were detected in at least one form of medium (i.e., water, sediment, or fish). CECs were detected most frequently at wastewater effluent-impacted sites, with up to 83 chemicals detected in one such lake, while as many as 17 were detected in an undeveloped lake. Although there was no statistically significant difference between the number of CECs present in developed versus undeveloped lakes, a range of 3-17 CECs were detected across these locations. Twenty-two CECs were detected in developed and undeveloped sites that were not detected in wastewater effluent-impacted sites. The detection of CECs in remote, undeveloped locations, where subsistence fish are harvested, raises scientific questions about the safety and security of subsistence foods for indigenous communities. Further investigation is warranted so that science-based solutions to reduce chemical risks to aquatic life and people can be developed locally and be informative for indigenous communities elsewhere.

Incorporating climate change adaptation into marine protected area planning

Climate change is increasingly impacting marine protected areas (MPAs) and MPA networks, yet adaptation strategies are rarely incorporated into MPA design and management plans according to the primary scientific literature. Here we review the state of knowledge for adapting existing and future MPAs to climate change and synthesize case studies (n = 27) of how marine conservation planning can respond to shifting environmental conditions. First, we derive a generalized conservation planning framework based on five published frameworks that incorporate climate change adaptation to inform MPA design. We then summarize examples from the scientific literature to assess how conservation goals were defined, vulnerability assessments performed and adaptation strategies incorporated into the design and management of existing or new MPAs. Our analysis revealed that 82% of real-world examples of climate change adaptation in MPA planning derive from tropical reefs, highlighting the need for research in other ecosystems and habitat types. We found contrasting recommendations for adaptation strategies at the planning stage, either focusing only on climate refugia, or aiming for representative protection of areas encompassing the full range of expected climate change impacts. Recommendations for MPA management were more unified and focused on adaptative management approaches. Lastly, we evaluate common barriers to adopting climate change adaptation strategies based on reviewing studies which conducted interviews with MPA managers and other conservation practitioners. This highlights a lack of scientific studies evaluating different adaptation strategies and shortcomings in current governance structures as two major barriers, and we discuss how these could be overcome. Our review provides a comprehensive synthesis of planning frameworks, case studies, adaptation strategies and management actions which can inform a more coordinated global effort to adapt existing and future MPA networks to continued climate change.

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Resilient planning demands not only resilient actions, but also resilient implementation, which promotes adaptive capacity for the attainment of the planned objectives. This requires, in the case of multi-level infrastructure systems, the simultaneous pursuit of bottom-up infrastructure planning for the promotion of adaptive capacity, and of top-down approaches for the achievement of global objectives and the reduction of structural vulnerabilities and imbalances. Though several authors have pointed out the need to balance bottom-up flexibility with top-down hierarchical control for better plan implementation, very few methods have yet been developed with this aim, least of all with a multi-objective perspective. This work addressed this lack by including, for the first time, the mitigation of urban vulnerability, the improvement of road network condition, and the minimization of the economic cost as objectives in a resilient planning process in which both actions and their implementation are planned for a controlled, sustainable development. Building on Urban planning support system (UPSS), a previously developed planning tool, the improved planning support system affords a planning alternative over the Spanish road network, with the best multi-objective balance between optimization, risk, and opportunity. The planning process then formalizes local adaptive capacity as the capacity to vary the selected planning alternative within certain limits, and global risk control as the duties that should be achieved in exchange. Finally, by means of multi-objective optimization, the method reveals the multi-objective trade-offs between local opportunity, global risk, and rights and duties at local scale, thus providing deeper understanding for better informed decision-making.

Spatial conservation planning under uncertainty: adapting to climate change risks using modern portfolio theory

Climate change and urban growth impact habitats, species, and ecosystem services. To buffer against global change, an established adaptation strategy is designing protected areas to increase representation and complementarity of biodiversity features. Uncertainty regarding the scale and magnitude of landscape change complicates reserve planning and exposes decision makers to the risk of failing to meet conservation goals. Conservation planning tends to treat risk as an absolute measure, ignoring the context of the management problem and risk preferences of stakeholders. Application of risk management theory to conservation emphasizes the diversification of a portfolio of assets, with the goal of reducing the impact of system volatility on investment return. We use principles of Modern Portfolio Theory (MPT), which quantifies risk as the variance and correlation among assets, to formalize diversification as an explicit strategy for managing risk in climate-driven reserve design. We extend MPT to specify a framework that evaluates multiple conservation objectives, allows decision makers to balance management benefits and risk when preferences are contested or unknown, and includes additional decision options such as parcel divestment when evaluating candidate reserve designs. We apply an efficient search algorithm that optimizes portfolio design for large conservation problems and a game theoretic approach to evaluate portfolio trade-offs that satisfy decision makers with divergent benefit and risk tolerances, or when a single decision maker cannot resolve their own preferences. Evaluating several risk profiles for a case study in South Carolina, our results suggest that a reserve design may be somewhat robust to differences in risk attitude but that budgets will likely be important determinants of conservation planning strategies, particularly when divestment is considered a viable alternative. We identify a possible fiscal threshold where adequate resources allow protecting a sufficiently diverse portfolio of habitats such that the risk of failing to achieve conservation objectives is considerably lower. For a range of sea-level rise projections, conversion of habitat to open water (14-180%) and wetland loss (1-7%) are unable to be compensated under the current protected network. In contrast, optimal reserve design outcomes are predicted to ameliorate expected losses relative to current and future habitat protected under the existing conservation estate.

The Strategies for Improving Energy Efficiency of Power System with Increasing Share of Wind Power in China

Coal-fired power generation will dominate the electricity supply in China in the foreseeable future. Coal fired power units can play a crucial role in integrating intermittent wind energy and improving the overall energy efficiency of the power system. The integration benefits of wind power, along with the gains of high load rates of coal fired units, should be fully taken into account. An optimal model combining wind power and coal fired units is built to analyze the operational flexibility of coal fired units and the integration of wind power. Taking the coal fired units in North China Power Grid as an example, the dispatch costs and benefits are examined under the energy efficiency dispatch mode, in comparison with those under the fair dispatch rules and the installed capacity. The results show that increasing the flexibility of the power system under the energy efficiency dispatch mode may be the best choice for the power system with the high share of coal fired units to integrate more wind power, and that the units delivering flexibility services are financially influenced. The results also indicate that a certain amount of wind power curtailment may be reasonable, and that rational penalty rate and fees for the curtailment of wind power may help to optimize the operation of the power system and integrate more wind power. Based on these results, policy and strategy recommendations are proposed to promote the flexibility of coal fired units and change their operation mode and their dispatch mode in the power system.

An Integrated Framework for Environmental Multi-Impact Spatial Risk Analysis

Quantitative risk analysis is being extensively employed to support policymakers and provides a strong conceptual framework for evaluating decision alternatives under uncertainty. Many problems involving environmental risks are, however, of a spatial nature, i.e., containing spatial impacts, spatial vulnerabilities, and spatial risk-mitigation alternatives. Recent developments in multicriteria spatial analysis have enabled the assessment and aggregation of multiple impacts, supporting policymakers in spatial evaluation problems. However, recent attempts to conduct spatial multicriteria risk analysis have generally been weakly conceptualized, without adequate roots in quantitative risk analysis. Moreover, assessments of spatial risk often neglect the multidimensional nature of spatial impacts (e.g., social, economic, human) that are typically occurring in such decision problems. The aim of this article is therefore to suggest a conceptual quantitative framework for environmental multicriteria spatial risk analysis based on expected multi-attribute utility theory. The framework proposes: (i) the formal assessment of multiple spatial impacts; (ii) the aggregation of these multiple spatial impacts; (iii) the assessment of spatial vulnerabilities and probabilities of occurrence of adverse events; (iv) the computation of spatial risks; (v) the assessment of spatial risk mitigation alternatives; and (vi) the design and comparison of spatial risk mitigation alternatives (e.g., reductions of vulnerabilities and/or impacts). We illustrate the use of the framework in practice with a case study based on a flood-prone area in northern Italy.

Examining the Impacts of Urban Form on Air Pollution in Developing Countries: A Case Study of China's Megacities

Urban form is increasingly being identified as an important determinant of air pollution in developed countries. However, the effect of urban form on air pollution in developing countries has not been adequately addressed in the literature to date, which points to an evident omission in current literature. In order to fill this gap, this study was designed to estimate the impacts of urban form on air pollution for a panel made up of China's five most rapidly developing megacities (Beijing, Tianjin, Shanghai, Chongqing, and Guangzhou) using time series data from 2000 to 2012. Using the official Air Pollution Index (API) data, this study developed three quantitative indicators: mean air pollution index (MAPI), air pollution ratio (APR), and continuous air pollution ratio (CAPR), to evaluate air pollution levels. Moreover, seven landscape metrics were calculated for the assessment of urban form based on three aspects (urban size, urban shape irregularity, and urban fragmentation) using remote sensing data. Panel data models were subsequently employed to quantify the links between urban form and air pollution. The empirical results demonstrate that urban expansion surprisingly helps to reduce air pollution. The substitution of clean energy for dirty energy that results from urbanization in China offers a possible explanation for this finding. Furthermore, urban shape irregularity positively correlated with the number of days with polluted air conditions, a result could be explained in terms of the influence of urban geometry on traffic congestion in Chinese cities. In addition, a negative association was identified between urban fragmentation and the number of continuous days of air pollution, indicating that polycentric urban forms should be adopted in order to shorten continuous pollution processes. If serious about achieving the meaningful alleviation of air pollution, decision makers and urban planners should take urban form into account when developing sustainable cities in developing countries like China.

A Framework for Modeling Emerging Diseases to Inform Management

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Coupling Modern Portfolio Theory and Marxan enhances the efficiency of Lesser White-fronted Goose's (Anser erythropus) habitat conservation

Climate change and human activities cause uncertain changes to species biodiversity by altering their habitat. The uncertainty of climate change requires planners to balance the benefit and cost of making conservation plan. Here optimal protection approach for Lesser White-fronted Goose (LWfG) by coupling Modern Portfolio Theory (MPT) and Marxan selection were proposed. MPT was used to provide suggested weights of investment for protected area (PA) and reduce the influence of climatic uncertainty, while Marxan was utilized to choose a series of specific locations for PA. We argued that through combining these two commonly used techniques with the conservation plan, including assets allocation and PA chosing, the efficiency of rare bird's protection would be enhanced. In MPT analyses, the uncertainty of conservation-outcome can be reduced while conservation effort was allocated in Hunan, Jiangxi and Yangtze River delta. In Marxan model, the optimal location for habitat restorations based on existing nature reserve was identified. Clear priorities for the location and allocation of assets could be provided based on this research, and it could help decision makers to build conservation strategy for LWfG.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Spatial ecological networks: planning for sustainability in the long-term

Humans are producing complex and often undesirable social and ecological outcomes in many landscapes around the world. To sustain biodiversity and ecosystem services in fragmented landscapes conservation planning has turned to the identification and protection of large-scale spatial ecological networks (SEN). Now widely adopted, this approach typically focuses on static connectivity, and ignores the feedbacks between changes to the network's topology and the eco-evolutionary dynamics on the network. We review theory showing that diversity, stability, ecosystem functioning and evolutionary adaptation all vary nonlinearly with connectivity. Measuring and modelling an SEN's long-term dynamics is immensely challenging but necessary if our goal is sustainability. We show an example where the robustness of an SEN's ecological properties to node and link loss depends on the centrality of the nodes targeted. The design and protection of sustainable SENs requires scenarios of how landscape change affects network structure and the feedback this will have on dynamics. Once established, SEN must be monitored if their design is to be adapted to keep their dynamics within a safe and socially just operating space. When SEN are co-designed with a broad array of stakeholders and actors they can be a powerful means of creating a more positive relationship between people and nature.

Portfolio optimization for seed selection in diverse weather scenarios

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.

A Practitioner-Driven Research Agenda for Syndromic Surveillance

Syndromic surveillance has expanded since 2001 in both scope and geographic reach and has benefited from research studies adapted from numerous disciplines. The practice of syndromic surveillance continues to evolve rapidly. The International Society for Disease Surveillance solicited input from its global surveillance network on key research questions, with the goal of improving syndromic surveillance practice. A workgroup of syndromic surveillance subject matter experts was convened from February to June 2016 to review and categorize the proposed topics. The workgroup identified 12 topic areas in 4 syndromic surveillance categories: informatics, analytics, systems research, and communications. This article details the context of each topic and its implications for public health. This research agenda can help catalyze the research that public health practitioners identified as most important.

Defining Ecosystem Assets for Natural Capital Accounting

In natural capital accounting, ecosystems are assets that provide ecosystem services to people. Assets can be measured using both physical and monetary units. In the international System of Environmental-Economic Accounting, ecosystem assets are generally valued on the basis of the net present value of the expected flow of ecosystem services. In this paper we argue that several additional conceptualisations of ecosystem assets are needed to understand ecosystems as assets, in support of ecosystem assessments, ecosystem accounting and ecosystem management. In particular, we define ecosystems’ capacity and capability to supply ecosystem services, as well as the potential supply of ecosystem services. Capacity relates to sustainable use levels of multiple ecosystem services, capability involves prioritising the use of one ecosystem service over a basket of services, and potential supply considers the ability of ecosystems to generate services regardless of demand for these services. We ground our definitions in the ecosystem services and accounting literature, and illustrate and compare the concepts of flow, capacity, capability, and potential supply with a range of conceptual and real-world examples drawn from case studies in Europe and North America. Our paper contributes to the development of measurement frameworks for natural capital to support environmental accounting and other assessment frameworks.

Importance of Ecological Factors and Colony Handling for Optimizing Health Status of Apiaries in Mediterranean Ecosystems

We analyzed six apiaries in several natural environments with a Mediterranean ecosystem in Madrid, central Spain, in order to understand how landscape and management characteristics may influence apiary health and bee production in the long term. We focused on five criteria (habitat quality, landscape heterogeneity, climate, management and health), as well as 30 subcriteria, and we used the analytic hierarchy process (AHP) to rank them according to relevance. Habitat quality proved to have the highest relevance, followed by beehive management. Within habitat quality, the following subcriteria proved to be most relevant: orographic diversity, elevation range and important plant species located 1.5 km from the apiary. The most important subcriteria under beehive management were honey production, movement of the apiary to a location with a higher altitude and wax renewal. Temperature was the most important subcriterion under climate, while pathogen and Varroa loads were the most significant under health. Two of the six apiaries showed the best values in the AHP analysis and showed annual honey production of 70 and 28 kg/colony. This high productivity was due primarily to high elevation range and high orographic diversity, which favored high habitat quality. In addition, one of these apiaries showed the best value for beehive management, while the other showed the best value for health, reflected in the low pathogen load and low average number of viruses. These results highlight the importance of environmental factors and good sanitary practices to maximize apiary health and honey productivity.

Stochastic Identification of Stability of Competitive Interactions in Ecosystems

The problem of finding an optimum within a set of possibilities that represent the varying successfulness of numerous subjects competing with one another is highly relevant in the field of ecosystem interactions. We propose a method for solving this problem by the application of the Nash equilibrium concept, which is frequently used in ecology. The proposed model is based on the transformation of the initial payoff vectors of subjects that interact in different situations into a statistical set of symmetrical game matrices that consist of permutations of payoff values. The equilibrium solution is expressed as values of the probability of Nash equilibrium occurrence with uniform distribution over all possible permutations based on uncertainty of positions of payoff values in the matrix. We assume that this equilibrium solution provides information on the distribution of the degree of stability among individual situations and interacting subjects. In this paper, we validate this assumption and demonstrate its application to a dataset that represents interspecies interactions in plant ecology. We propose that the use of the Nash equilibrium in the analysis of datasets formalized according to the Pareto optimality scheme is applicable in numerous other contexts.

Comparing bioenergy production sites in the Southeastern US regarding ecosystem service supply and demand

Biomass for bioenergy is debated for its potential synergies or tradeoffs with other provisioning and regulating ecosystem services (ESS). This biomass may originate from different production systems and may be purposefully grown or obtained from residues. Increased concerns globally about the sustainable production of biomass for bioenergy has resulted in numerous certification schemes focusing on best management practices, mostly operating at the plot/field scale. In this study, we compare the ESS of two watersheds in the southeastern US. We show the ESS tradeoffs and synergies of plantation forestry, i.e., pine poles, and agricultural production, i.e., wheat straw and corn stover, with the counterfactual natural or semi-natural forest in both watersheds. The plantation forestry showed less distinct tradeoffs than did corn and wheat production, i.e., for carbon storage, P and sediment retention, groundwater recharge, and biodiversity. Using indicators of landscape composition and configuration, we showed that landscape planning can affect the overall ESS supply and can partly determine if locally set environmental thresholds are being met. Indicators on landscape composition, configuration and naturalness explained more than 30% of the variation in ESS supply. Landscape elements such as largely connected forest patches or more complex agricultural patches, e.g., mosaics with shrub and grassland patches, may enhance ESS supply in both of the bioenergy production systems. If tradeoffs between biomass production and other ESS are not addressed by landscape planning, it may be reasonable to include rules in certification schemes that require, e.g., the connectivity of natural or semi-natural forest patches in plantation forestry or semi-natural landscape elements in agricultural production systems. Integrating indicators on landscape configuration and composition into certification schemes is particularly relevant considering that certification schemes are governance tools used to ensure comparable sustainability standards for biomass produced in countries with variable or absent legal frameworks for landscape planning.

Optimal surveillance network design: a value of information model

Purpose

Infectious diseases are the second leading cause of deaths worldwide, accounting for 15 million deaths – that is more than 25% of all deaths – each year. Food plays a crucial role, contributing to 1.5 million deaths, most of which are children, through foodborne diarrheal disease alone. Thus, the ability to timely detect outbreak pathways via high-efficiency surveillance system is essential to the physical and social well being of populations. For this purpose, a traceability model inspired by wavepattern recognition models to detect “zero-patient” areas based on outbreak spread is proposed.

Methods

Model effectiveness is assessed for data from the 2010 Cholera epidemic in Cameroon, the 2012 foodborne Salmonella epidemic in USA, and the 2004-2007 H5N1 avian influenza pandemic. Previous models are complemented by the introduction of an optimal selection algorithm of surveillance networks based on the Value of Information (VoI) of reporting nodes that are subnetworks of mobility networks in which people, food, and species move. The surveillance network is considered the response variable to be determined in maximizing the accuracy of outbreak source detections while minimizing detection error. Surveillance network topologies are selected by considering their integrated network resilience expressing the rewiring probability that is related to the ability to report outbreak information even in case of network destruction or missing information.

Results

Independently of the outbreak epidemiology, the maximization of the VoI leads to a minimum increase in accuracy of 40% compared to the random surveillance model. Such accuracy is accompanied by an average reduction of 25% in required surveillance nodes with respect to random surveillance. Accuracy in systems diagnosis increases when system syndromic signs are the most informative in a way they reveal linkages between outbreak patterns and network transmission processes.

Conclusions

The model developed is extremely useful for the optimization of surveillance networks to drastically reduce the burden of food-borne and other infectious diseases. The model can be the framework of a cyber-technology that governments and industries can utilize in a real-time manner to avoid catastrophic and costly health and economic outcomes. Further applications are envisioned for chronic diseases, socially communicable diseases, biodefense and other detection related problems at different scales.