Using Bayesian Population Viability Analysis to Define Relevant Conservation Objectives

Adaptive management provides a useful framework for managing natural resources in the face of uncertainty. An important component of adaptive management is identifying clear, measurable conservation objectives that reflect the desired outcomes of stakeholders. A common objective is to have a sustainable population, or metapopulation, but it can be difficult to quantify a threshold above which such a population is likely to persist. We performed a Bayesian metapopulation viability analysis (BMPVA) using a dynamic occupancy model to quantify the characteristics of two wood frog (Lithobates sylvatica) metapopulations resulting in sustainable populations, and we demonstrate how the results could be used to define meaningful objectives that serve as the basis of adaptive management. We explored scenarios involving metapopulations with different numbers of patches (pools) using estimates of breeding occurrence and successful metamorphosis from two study areas to estimate the probability of quasi-extinction and calculate the proportion of vernal pools producing metamorphs. Our results suggest that ≥50 pools are required to ensure long-term persistence with approximately 16% of pools producing metamorphs in stable metapopulations. We demonstrate one way to incorporate the BMPVA results into a utility function that balances the trade-offs between ecological and financial objectives, which can be used in an adaptive management framework to make optimal, transparent decisions. Our approach provides a framework for using a standard method (i.e., PVA) and available information to inform a formal decision process to determine optimal and timely management policies.     

Science,Uncertainty, and Values in Ecological Restoration: A Case Study in Structured Decision‐Making and Adaptive Management

This article demonstrates a structured and collaborative approach to decision‐making in the context of adaptive management experiments, using a case study involving the restoration of a hydrological regime in a regulated river in western Canada. It provides a framework based on principles of decision analysis for structuring difficult multi‐attribute decisions and building the trust and technical capacity needed to implement them. Participants included ecologists and fisheries biologists, government regulators, electric utility employees, and representatives of aboriginal communities. The case study demonstrates a values‐based approach to implementing adaptive management that addresses some of the long‐standing difficulties associated with integrating adaptive management into restoration decisions. It highlights practical methods for incorporating participants' values concerned with learning, cultural quality, and stewardship as part of developing a decision‐making and monitoring framework for restoration initiatives. It also provides an example of how to implement principles of meaningful consultation in a restoration context, with emphasis on ensuring that all voices and concerns are heard and meaningfully incorporated. Participants have adopted the framework as a model to guide future collaborative decision‐making processes involving Aboriginal communities, regulatory agencies, and other parties.     

How to Decide Whether to Move Species Threatened by Climate Change

Introducing species to areas outside their historical range to secure their future under climate change is a controversial strategy for preventing extinction. While the debate over the wisdom of this strategy continues, such introductions are already taking place. Previous frameworks for analysing the decision to introduce have lacked a quantifiable management objective and mathematically rigorous problem formulation. Here we develop the first rigorous quantitative framework for deciding whether or not a particular introduction should go ahead, which species to prioritize for introduction, and where and how to introduce them. It can also be used to compare introduction with alternative management actions, and to prioritise questions for future research. We apply the framework to a case study of tuatara (Sphenodon punctatus) in New Zealand. While simple and accessible, this framework can accommodate uncertainty in predictions and values. It provides essential support for the existing IUCN guidelines by presenting a quantitative process for better decision-making about conservation introductions.     

Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.     

Life cycle assessment of fuel cell vehicles a methodology example of input data treatment for future technologies

Life cycle assessment (LCA) will always involve some subjectivity and uncertainty. This reality is especially true when the analysis concerns new technologies. Dealing with uncertainty can generate richer information and minimize some of the result mismatches currently encountered in the literature. As a way of analyzing future fuel cell vehicles and their potential new fuels, the Fuel Upstream Energy and Emission Model (FUEEM) developed at the University of California—Davis, pioneered two different ways to incorporate uncertainty into the analysis. First, the model works with probabilistic curves as inputs and with Monte Carlo simulation techniques to propagate the uncertainties. Second, the project involved the interested parties in the entire process, not only in the critical review phase. The objective of this paper is to present, as a case study, the tools and the methodologies developed to acquire most of the knowledge held by interested parties and to deal with their — eventually conflicted—interests. The analysis calculation methodology, the scenarios, and all assumed probabilistic curves were derived from a consensus of an international expert network discussion, using existing data in the literature along with new information collected from companies. The main part of the expert discussion process uses a variant of the Delphi technique, focusing on the group learning process through the information feedback feature. A qualitative analysis indicates that a higher level of credibility and a higher quality of information can be achieved through a more participatory process. The FUEEM method works well within technical information and also in establishing a reasonable set of simple scenarios. However, for a complex combination of scenarios, it will require some improvement. The time spent in the process was the major drawback of the method and some alternatives to share this time cost are suggested.     

Ambiguity in Social Network Data for Presence,Sensitive-Attribute,Degree and Relationship Privacy Protection

Maintaining privacy in network data publishing is a major challenge. This is because known characteristics of individuals can be used to extract new information about them. Recently, researchers have developed privacy methods based on k-anonymity and l-diversity to prevent re-identification or sensitive label disclosure through certain structural information. However, most of these studies have considered only structural information and have been developed for undirected networks. Furthermore, most existing approaches rely on generalization and node clustering so may entail significant information loss as all properties of all members of each group are generalized to the same value. In this paper, we introduce a framework for protecting sensitive attribute, degree (the number of connected entities), and relationships, as well as the presence of individuals in directed social network data whose nodes contain attributes. First, we define a privacy model that specifies privacy requirements for the above private information. Then, we introduce the technique of Ambiguity in Social Network data (ASN) based on anatomy, which specifies how to publish social network data. To employ ASN, individuals are partitioned into groups. Then, ASN publishes exact values of properties of individuals of each group with common group ID in several tables. The lossy join of those tables based on group ID injects uncertainty to reconstruct the original network. We also show how to measure different privacy requirements in ASN. Simulation results on real and synthetic datasets demonstrate that our framework, which protects from four types of private information disclosure, preserves data utility in tabular, topological and spectrum aspects of networks at a satisfactory level.     

Cost-Effective Marine Protection - A Pragmatic Approach

This paper puts forward a framework for probabilistic and holistic cost-effectiveness analysis to provide support in selecting the least-cost set of measures to reach a multidimensional environmental objective. Following the principles of ecosystem-based management, the framework includes a flexible methodology for deriving and populating criteria for effectiveness and costs and analyzing complex ecological-economic trade-offs under uncertainty. The framework is applied in the development of the Finnish Programme of Measures (PoM) for reaching the targets of the EU Marine Strategy Framework Directive (MSFD). The numerical results demonstrate that substantial cost savings can be realized from careful consideration of the costs and multiple effects of management measures. If adopted, the proposed PoM would yield improvements in the state of the Baltic Sea, but the overall objective of the MSFD would not be reached by the target year of 2020; for various environmental and administrative reasons, it would take longer for most measures to take full effect.     

Meta-performance evaluation of sustainability indicators

There are many different kinds of frameworks for evaluating environmental and sustainability performance at the organizational level (profit or not-for-profit, private or public), sectoral level (e.g. industry, transport, agriculture and tourism), and local, regional or country levels. Despite the diversity of methods and tools to measure sustainable development, indicators are one of the approaches most used. However, these tools do not usually include evaluation of the performance measurement instrument itself. The main objective of this research is to develop a conceptual framework to design and assess the effectiveness of the sustainability indicators themselves. To put the proposed tool into practice, a set of key good-practice factors and meta-performance evaluation indicators is proposed for adoption in a national case study—the national sustainable development indicators system, SIDS Portugal, and the usefulness of this methodology is demonstrated. This approach aims to evaluate how appropriate a set of sustainability indicators is and allow an evaluation of overall performance-monitoring activities and results. Stakeholder involvement is an essential component of the proposed framework. The tool developed could support continuous improvement in the performance of ongoing sustainability indicator initiatives, allowing greater guidance, objectivity and transparency in sustainability assessment processes.     

An Enhanced Adaptive Management Approach for Remediation of Legacy Mercury in the South River

Uncertainties about future conditions and the effects of chosen actions, as well as increasing resource scarcity, have been driving forces in the utilization of adaptive management strategies. However, many applications of adaptive management have been criticized for a number of shortcomings, including a limited ability to learn from actions and a lack of consideration of stakeholder objectives. To address these criticisms, we supplement existing adaptive management approaches with a decision-analytical approach that first informs the initial selection of management alternatives and then allows for periodic re-evaluation or phased implementation of management alternatives based on monitoring information and incorporation of stakeholder values. We describe the application of this enhanced adaptive management (EAM) framework to compare remedial alternatives for mercury in the South River, based on an understanding of the loading and behavior of mercury in the South River near Waynesboro, VA. The outcomes show that the ranking of remedial alternatives is influenced by uncertainty in the mercury loading model, by the relative importance placed on different criteria, and by cost estimates. The process itself demonstrates that a decision model can link project performance criteria, decision-maker preferences, environmental models, and short- and long-term monitoring information with management choices to help shape a remediation approach that provides useful information for adaptive, incremental implementation.     

Cyberinfrastructure for sourcing and processing ecological data

Cyberinfrastructure is a product of the information age that provides a framework for informing adaptive management of ecological entities under the impact of regional and global change. It supports proximity monitoring, user-friendly data management, knowledge discovery by data synthesis, and decision making by forecasting.A workflow is proposed that suits the iterative nature of adaptive management. It takes advantage of novel sensor, genomics, and communication technology for ecological monitoring, of ontologies, semantic webs and blockchain for data management, of hybrid, machine and deep learning concepts for data synthesis and forecasting. Forecasting at different time horizons is guiding decision making for adjusting management and continuing monitoring.This review aims to make researchers, decision makers and stakeholders aware of currently existing technology to make better use of ecological data and models for timely and evidence-based decisions.     

Optimization methods to solve adaptive management problems

Determining the best management actions is challenging when critical information is missing. However, urgency and limited resources require that decisions must be made despite this uncertainty. The best practice method for managing uncertain systems is adaptive management, or learning by doing. Adaptive management problems can be solved optimally using decision-theoretic methods; the challenge for these methods is to represent current and future knowledge using easy-to-optimize representations. Significant methodological advances have been made since the seminal adaptive management work was published in the 1980s, but despite recent advances, guidance for implementing these approaches has been piecemeal and study-specific. There is a need to collate and summarize new work. Here, we classify methods and update the literature with the latest optimal or near-optimal approaches for solving adaptive management problems. We review three mathematical concepts required to solve adaptive management problems: Markov decision processes, sufficient statistics, and Bayes’ theorem. We provide a decision tree to determine whether adaptive management is appropriate and then group adaptive management approaches based on whether they learn only from the past (passive) or anticipate future learning (active). We discuss the assumptions made when using existing models and provide solution algorithms for each approach. Finally, we propose new areas of development that could inspire future research. For a long time, limited by the efficiency of the solution methods, recent techniques to efficiently solve partially observable decision problems now allow us to solve more realistic adaptive management problems such as imperfect detection and non-stationarity in systems.     

Using a structured decision analysis to evaluate bald eagle vital signs monitoring in Southwest Alaska National Parks

1. Monitoring programs can benefit from an adaptive monitoring approach, where key decisions about why, where, what, and how to monitor are revisited periodically in order to ensure programmatic relevancy.
2. The National Park Service (NPS) monitors status and trends of vital signs to evaluate compliance with the NPS mission. Although abundant, The Southwest Alaska Network (SWAN) monitors bald eagles because of their inherent importance to park visitors and role as an important ecological indicator. Our goal is to identify an optimal monitoring program that may be standardized among participating parks.
3. We gathered an expert panel of scientists and managers, and implemented a Delphi Process to gather information about the bald eagle monitoring program. Panelists generated a list of means objectives for the monitoring program: minimizing cost, minimizing effort, maximizing the ability to detect change in bald eagle populations, and maximizing the amount of accurate information collected about bald eagles.
4. We used a swing‐weighting technique to assign importance to each objective. Collecting accurate information about bald eagles was considered the most important means objective.
5. Combining panelist‐generated information with objective importance, we analyzed the scenarios and defined the optimal decision using linear value modeling. Through our analysis, we found that a “Comprehensive” monitoring scenario, comprised of all feasible monitoring metrics, is the optimal monitoring scenario. Even with greatly increased cost, the Comprehensive monitoring scenario remains the best solution.
6. We suggest further exploration of the cost and effort required for the Comprehensive scenario, to determine whether it is in the parks’ best interest to begin monitoring additional metrics.

     

Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling

Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus’ extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance.     

Adaptive management of coastal ecosystem restoration projects

There is a clear need to apply better and more effective management schemes to coastal ecosystem restoration projects. It is very common for aquatic ecosystem restoration projects not to meet their goals. Poor performance has led to a high degree of uncertainty about the potential success of any restoration effort. Under adaptive management, the knowledge gained through monitoring of the project and social policies is translated into restoration policy and program redesign. Planners and managers can utilize the information from the monitoring programs in an effective way to assure that project goals are met or that informed and objective decisions are made to address both ecological and societal needs. The three main ingredients of an effective adaptive management plan in a restoration project are: (1) a clear goal statement; (2) a conceptual model; and (3) a decision framework. The goal ‘drives’ the design of the project and helps guide the development of performance criteria. The goal statement and performance criteria provide the means by which the system can be judged. With the conceptual model, the knowledge base from the field of ecological science plays an active and critical role in designing the project to meet the goal. A system-development matrix provides a simple decision framework to view the alternative states for the system during development, incorporate knowledge gained through the monitoring program, and formulate a decision on actions to take if the system is not meeting its goal.     

Biotic interactions, ecological knowledge and agriculture

This paper discusses biotic interactions in agroecosystems and how they may be manipulated to support crop productivity and environmental health by provision of ecosystem services such as weed, pest and disease management, nutrient cycling and biodiversity conservation. Important elements for understanding biotic interactions include consideration of the effects of diversity, species composition and food web structure on ecosystem processes; the impacts of timing, frequency and intensity of disturbance; and the importance of multitrophic interactions. All of these elements need to be considered at multiple scales that depend in part on the range of the movement of the organisms involved. These issues are first discussed in general, followed by an examination of the application of these concepts in agricultural management. The potential for a greater use of ecological management approaches is high; however, owing to the nature of complex interactions in ecosystems, there is some inherent unpredictability about responses to management interventions under different conditions. Such uncertainty needs to be accommodated in the development of recommendations for farm management. This requires an increased emphasis on the effective synthesis of complex and often apparently contradictory information and on field-based adaptive research, monitoring and social learning by farmer/researcher collaborations.     

Climate change,uncertainty, and natural resource management

Climate change and its associated uncertainties are of concern to natural resource managers. Although aspects of climate change may be novel (e.g., system change and nonstationarity), natural resource managers have long dealt with uncertainties and have developed corresponding approaches to decision-making. Adaptive resource management is an application of structured decision-making for recurrent decision problems with uncertainty, focusing on management objectives, and the reduction of uncertainty over time. We identified 4 types of uncertainty that characterize problems in natural resource management. We examined ways in which climate change is expected to exacerbate these uncertainties, as well as potential approaches to dealing with them. As a case study, we examined North American waterfowl harvest management and considered problems anticipated to result from climate change and potential solutions. Despite challenges expected to accompany the use of adaptive resource management to address problems associated with climate change, we conclude that adaptive resource management approaches will be the methods of choice for managers trying to deal with the uncertainties of climate change. © 2010 The Wildlife Society.     

Solving man-induced large-scale conservation problems: the Spanish imperial eagle and power lines

Background

Man-induced mortality of birds caused by electrocution with poorly-designed pylons and power lines has been reported to be an important mortality factor that could become a major cause of population decline of one of the world rarest raptors, the Spanish imperial eagle (Aquila adalberti). Consequently it has resulted in an increasing awareness of this problem amongst land managers and the public at large, as well as increased research into the distribution of electrocution events and likely mitigation measures.

Methodology/Principal Findings

We provide information of how mitigation measures implemented on a regional level under the conservation program of the Spanish imperial eagle have resulted in a positive shift of demographic trends in Spain. A 35 years temporal data set (1974–2009) on mortality of Spanish imperial eagle was recorded, including population censuses, and data on electrocution and non-electrocution of birds. Additional information was obtained from 32 radio-tracked young eagles and specific field surveys. Data were divided into two periods, before and after the approval of a regional regulation of power line design in 1990 which established mandatory rules aimed at minimizing or eliminating the negative impacts of power lines facilities on avian populations. Our results show how population size and the average annual percentage of population change have increased between the two periods, whereas the number of electrocuted birds has been reduced in spite of the continuous growing of the wiring network.

Conclusions

Our results demonstrate that solving bird electrocution is an affordable problem if political interest is shown and financial investment is made. The combination of an adequate spatial planning with a sustainable development of human infrastructures will contribute positively to the conservation of the Spanish imperial eagle and may underpin population growth and range expansion, with positive side effects on other endangered species.     

Reproductive state affects reliance on public information in sticklebacks

The degree to which animals use public and private sources of information has important implications for research in both evolutionary ecology and cultural evolution. While researchers are increasingly interested in the factors that lead individuals to vary in the manner in which they use different sources of information, to date little is known about how an animal''s reproductive state might affect its reliance on social learning. Here, we provide experimental evidence that in foraging ninespine sticklebacks (Pungitius pungitius), gravid females increase their reliance on public information generated by feeding demonstrators in choosing the richer of two prey patches than non-reproductive fish, while, in contrast, reproductive males stop using public information. Subsequent experiments revealed reproductive males to be more efficient asocial foragers, less risk-averse and generally less social than both reproductive females and non-reproductives. These findings are suggestive of adaptive switches in reliance on social and asocial sources of information with reproductive condition, and we discuss the differing costs of reproduction and the proximate mechanisms that may underlie these differences in information use. Our findings have important implications for our understanding of adaptive foraging strategies in animals and for understanding the way information diffuses through populations.