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At least 109 records · Page 6Linked to original sources
USGS2021

Engaging hunters in selecting duck season dates using decision science: Problem framing, objective setting, devising management alternatives

Waterfowl hunters have an important economic impact on local, state, and national economies, and are important stakeholders in decisions regarding waterfowl harvest season dates. Individual states are responsible for annually setting duck season dates that conform to the migratory game bird season frameworks as set by the U.S. Fish and Wildlife Service. The federal framework specifies season length and bag limits (i.e., number of birds allowed to be harvested in a day), and states have the authority to select specific season dates within the federal guidelines. The state agency decision is largely centered on social objectives related to hunter satisfaction because the U.S. Fish and Wildlife Service incorporates biological objectives when establishing the federal framework to ensure sustainable waterfowl populations. This chapter describes the problem formulation, objectives, and alternatives steps of a decision analysis process used in New York. The authors demonstrate a process that allows for engagement by waterfowl hunters and incorporation of multiple stakeholder objectives, which can be used to evaluate tradeoffs to help guide decision making regarding selection of duck season dates. Engaging duck hunters through a task force and hunter survey provided opportunity for the regulated community to help shape season dates that quantitatively considered duck hunter satisfaction.

Book chapter↗
USGS2025

Managing for tomorrow—A climate adaptation decision framework

Climate change presents new and compounding challenges to natural resource management. With changing climate patterns, managers are confronted with difficult decisions on how to minimize climate effects on habitats, infrastructure, and wildlife populations. To support climate adaptation decision making, we first conceptualized an approach that integrates the principles of the resist–accept–direct framework, climate scenario planning, and decision analysis into a general framework to support adaptation planning. This framework was implemented and refined by working with three National Wildlife Refuge System refuges within the Midwest Region. The objectives of this report are to describe the climate adaptation decision framework and provide guidance for how to apply the framework to support transparent, consistent, and decision-focused adaptation planning. We include a workbook to support the application of each step of the framework as well as lessons learned from our experiences developing the framework. The climate adaptation decision framework has wide applicability to aid adaptation planning within natural resource management and underscores the important role of engaging interest groups in climate adaptation decisions.

Illinois, Indiana, Iowa, Michigan, Minnesota, Miss↗
USGS2022

A structured decision-making framework for managing cyanobacterial harmful algal blooms in New York State parks

Cyanobacteria are increasingly a global water-quality concern because of the potential for these organisms to develop into potentially harmful blooms that affect ecological, economic, and public health. Cyanobacterial harmful algal blooms (CyanoHABs) can lead to a decrease in water quality and affect many of the recreational and ecological benefits of parks that include lakes. The New York State Office of Parks, Recreation and Historic Preservation (OPRHP) is a State agency within the New York State Executive Department charged with the operation of State parks and historic sites. Many New York State parks include lakes or other freshwater bodies, which can be susceptible to CyanoHABs. The OPRHP faces difficult decisions regarding prevention of and response to CyanoHABs. The U.S. Geological Survey partnered with the OPRHP and the New York State Department of Environmental Conservation to develop a structured decision-making template for managing CyanoHABs in OPRHP parks. Two parks, Moreau Lake State Park and Rockland Lake State Park, served as case studies to motivate and test the template. This report describes how the principles of structured decision making can be used to navigate the challenges associated with managing CyanoHABs in OPRHP parks. Management objectives and strategies for CyanoHABs in parks are described, strategies to evaluate consequences and manage tradeoffs are discussed, and potential challenges to the implementation of preferred alternatives are considered. General guidance is provided so the OPRHP can undertake the structured decision-making process for CyanoHABs in any of its parks. In addition, this report represents the first effort to create a strategy for applying decision analysis tools to the complex natural resource challenge of CyanoHAB mitigation and management. The case studies and template are intended to serve as an example that natural resource managers faced with CyanoHABs challenges can use to inform their decision-making processes.

New York↗
USGS2013

Confronting dynamics and uncertainty in optimal decision making for conservation

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.

Environmental Research Letters↗
USGS2019

Integration of social and ecological sciences for natural resource decision making: Challenges and opportunities

The last 25 years have witnessed growing recognition that natural resource management decisions depend as much on understanding humans and their social interactions as on understanding the interactions between non-human organisms and their environment. Decision science provides a framework for integrating ecological and social factors into a decision, but challenges to integration remain. The decision-analytic framework elicits values and preferences to help articulate objectives, and then evaluates the outcomes of alternative management actions to achieve these objectives. Integrating social science into these steps can be hindered by failing to include social scientists as more than stakeholder-process facilitators, assuming that specific decision-analytic skills are commonplace for social scientists, misperceptions of social data as inherently qualitative, timescale mismatches for iterating through decision analysis and collecting relevant social data, difficulties in predicting human behavior, and failures of institutions to recognize the importance of this integration. We engage these challenges, and suggest solutions to them, helping move forward the integration of social and biological/ecological knowledge and considerations in decision-making.

Environmental Management↗
USGS2023

Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems

The loss of ecosystem services due to climate change and coastal development is projected to have significant impacts on local economies and conservation of natural resources. Consequently, there has been an increase in coastal management activities such as living shorelines, oyster reef restoration, marsh restoration, beach and dune nourishment, and revegetation projects. Coastal management decisions are complex and include challenging trade-offs. Decision science offers a useful framework to address such complex problems. Here, we provide a synthesis about how decision science can help to integrate research from multiple disciplines (physical and life sciences) with management of coastal and marine systems. Specifically, we discuss the importance of considering concepts and techniques from ecology, coastal geology, geomorphology, climate science, oceanography, and decision analysis when developing conservation plans for coastal restoration. We illustrate the process with several coastal restoration studies. Our capstone example is based on a recent barrier island restoration assessment project at Dauphin Island, Alabama, which included the development of geomorphological and ecological models. We show how decision science can be used as a framework to combine geomorphological and ecological modeling to help inform management decisions while considering uncertainty about system changes and risk tolerance. We also build on our examples through a review of recently developed techniques for spatial conservation planning for land acquisition decisions and the application of adaptive management for sequential decisions.

Alabama↗
USGS2021

Using decision science for monitoring threatened western snowy plovers to inform recovery

Western Snowy Plovers ( Charadrius nivosus nivosus ) are federally listed under the US Endangered Species Act as Threatened. They occur along the US Pacific coastline and are threatened by habitat loss and destruction and excessive levels of predation and human disturbance. Populations have been monitored since the 1970s for distribution, reproduction, and survival. Since the species was federally listed in 1993 and a recovery plan was approved under the US Fish and Wildlife Service in 2007, recovery actions have resulted in growing populations with increased presence at breeding and wintering sites throughout their Pacific Coast range. This success has created logistical challenges related to monitoring a recovering species and a need for identifying and instituting the best monitoring approach given recovery goals, budgets, and the likelihood of monitoring success. We devised and implemented a structured decision analysis to evaluate nine alternative monitoring strategies. The analysis included inviting plover biologists involved in monitoring to score each strategy according to a suite of performance measures. Using multi-attribute utility theory, we combined scores across the performance measures for each monitoring strategy, and applied weighted utility values to show the implications of tradeoffs and find optimal decisions. We evaluated four scenarios for weighting the monitoring objectives and how risk attitude affects optimal decisions. This resulted in identifying six strategies that best meet recovery needs and were Pareto optimal for cost-effective monitoring. Results were presented to the US Fish and Wildlife Service, responsible for monitoring as well as for consideration to ensure consistent monitoring methods across the species’ range. Our use of structured decision-making can be applied to cases of other species once imperiled but now on the road to recovery.

California, Oregon, Washington↗
USGS2014

Strategic conservation planning for the Eastern North Carolina/Southeastern Virginia Strategic Habitat Conservation Team

The Eastern North Carolina/Southeastern Virginia Strategic Habitat Conservation Team (ENCSEVA) is a partnership among local federal agencies and programs with a mission to apply Strategic Habitat Conservation to accomplish priority landscape-level conservation within its geographic region. ENCSEVA seeks to further landscape-scale conservation through collaboration with local partners. To accomplish this mission, ENCSEVA is developing a comprehensive Strategic Habitat Conservation Plan (Plan) to provide guidance for its members, partners, and collaborators by establishing mutual conservation goals, objectives, strategies, and metrics to gauge the success of conservation efforts. Identifying common goals allows the ENCSEVA team to develop strategies that leverage joint resources and are more likely to achieve desired impacts across the landscape. The Plan will also provide an approach for ENCSEVA to meet applied research needs (identify knowledge gaps), foster adaptive management principles, identify conservation priorities, prioritize threats (including potential impacts of climate change), and identify the required capacity to implement strategies to create more resilient landscapes. ENCSEVA seeks to support the overarching goals of the South Atlantic Landscape Conservation Cooperative (SALCC) and to provide scientific and technical support for conservation at landscape scales as well as inform the management of natural resources in response to shifts in climate, habitat fragmentation and loss, and other landscape-level challenges (South Atlantic LCC 2012). The ENCSEVA ecoregion encompasses the northern third of the SALCC geography and offers a unique opportunity to apply landscape conservation at multiple scales through the guidance of local conservation and natural resource management efforts and by reporting metrics that reflect the effectiveness of those efforts (Figure 1). The Environmental Decision Analysis Team, housed within the North Carolina Cooperative Fish and Wildlife Research Unit at North Carolina State University, is assisting the ENCSEVA team in developing a scientifically sound basis for the Plan though the elicitation of expert knowledge and the organization of that knowledge using the Open Standards for the Practice of Conservation. The Open Standards for the Practice of Conservation is a framework that is well suited to incorporating decision-making tools such as Structured Decision Making and provides a multi-step process to conceptually organize conservation projects in a manner that enhances the rigor and transparency of expert and knowledge-based plans. It helps define explicit pathways from 2 planned conservation activities and ultimate impact, as well as indicators to measure success (Stem et al. 2005). Specifically, the framework identifies conservation targets, key ecological attributes, threats, and associated indicators to monitor responses given the implementation of a conservation action (Conservation Measures Partnership 2007). This report serves to provide a scientific foundation for the Plan by summarizing the expert opinion of wildlife biologists, ecologists, hydrologists, researchers, natural resource managers, and conservation practitioners regarding five environments (wetlands, riverine systems, estuaries, uplands, and barrier islands) within the ENCSEVA geography. Specifically, this report describes (1) the approach to elicit expert knowledge meant to support the strategic plan, (2) how this knowledge can inform collaborative conservation planning, and (3) a summary of opportunities available for the ENCSEVA team to address threats and impacts associated with climate change within the ecoregion.

Technical Bulletin↗
USGS2014

Uncertainty and risk evaluation during the exploration stage of geothermal development

Quantifying and representing uncertainty for geothermal systems is often ignored, in practice, during the exploration phase of a geothermal development project. We propose that this occurs potentially because the task seems so formidable. The primary goal of this paper is to initiate a dialogue within the geothermal community about: which geothermal uncertainties should receive the most attention and which uncertainty analysis methods could provide the greatest benefit for the advancement of the geothermal energy industry. In this paper, we discuss uncertainty quantification techniques that are applicable to geothermal exploration. In general, uncertainty associated with data acquisition/processing (i.e., objective uncertainty) is small compared to the uncertainty in interpretational space (i.e., subjective uncertainty) that lies between data points where extrapolation is required. Therefore, it is important to classify, assess, and quantify uncertainty to help select strategies to reduce uncertainty, and to better gauge the impact that separate uncertainties have on the overall likelihood of project success. In addition, geostatistics provides multiple quantitative methods for producing stochastic models which adhere to measured data and spatial correlation. The petroleum industry has successfully used both geostastistics and decision analysis methods to combine diverse and multiple types of uncertainties. We argue that instead of one single and final interpretation of the geothermal system, numerous interpretations may be more indicative of the possible subsurface scenarios, and these different scenarios can be evaluated using decision analyses and value of information methodologies. Lastly, we recommend that the potential power generation of a geothermal reservoir should be grounded in the geologic data and modeling for a specific field and their estimated uncertainties. In this paper, we provide a brief overview of many of these topics while a more complete review has been recently published in Witter et al. (2019).

Conference Paper↗
USGS2019

Uncertainty and risk evaluation during the exploration stage of geothermal development: A review

Quantifying and representing uncertainty for geothermal systems is often ignored, in practice, during the exploration phase of a geothermal development project. We propose that this occurs potentially because the task seems so formidable. The primary goal of this paper is to initiate a dialogue within the geothermal community about: which geothermal uncertainties should receive the most attention and which uncertainty analysis methods could provide the greatest benefit for the advancement of the geothermal energy industry. Specifically, in this paper, we review uncertainty quantification techniques that are applicable to geothermal exploration. In general, uncertainty associated with data acquisition/processing (i.e., objective uncertainty) is small compared to the uncertainty in interpretational space (i.e., subjective uncertainty) that lies between data points where extrapolation is required. Therefore, it is important to classify, assess, and quantify uncertainty to help select strategies to reduce uncertainty and to better gauge the impact that separate uncertainties have on the overall likelihood of project success. The discipline of geostatistics provides multiple quantitative methods for producing stochastic models which adhere to measured data and spatial correlation. The petroleum industry has successfully used both geostatistics and decision analysis methods to combine diverse and multiple types of uncertainties. We argue that instead of one single and final interpretation of the geothermal system, numerous interpretations may be more indicative of the possible subsurface scenarios, and these different scenarios can be evaluated using decision analyses and value of information methodologies. Finally, we recommend that the potential power generation of a geothermal reservoir should be grounded in the geologic data and modeling for a specific field and their estimated uncertainties.

Geothermics↗
USGS2022

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.

Ecological Applications↗
USGS2023

Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

PNAS↗
USGS2018

Decision making for mitigating wildlife diseases: From theory to practice for an emerging fungal pathogen of amphibians

Conservation science can be most effective in its decision‐support role when seeking answers to clearly formulated questions of direct management relevance. Emerging wildlife diseases, a driver of global biodiversity loss, illustrate the challenges of performing this role: in spite of considerable research, successful disease mitigation is uncommon. Decision analysis is increasingly advocated to guide mitigation planning, but its application remains rare. Using an integral projection model, we explored potential mitigation actions for avoiding population declines and the ongoing spatial spread of the fungus Batrachochytrium salamandrivorans ( Bsal ). This fungus has recently caused severe amphibian declines in north‐western Europe and currently threatens Palearctic salamander diversity. Available evidence suggests that a Bsal outbreak in a fire salamander ( Salamandra salamandra ) population will lead to its rapid extirpation. Treatments such as antifungals or probiotics would need to effectively interrupt transmission (reduce probability of infection by nearly 90%) in order to reduce the risk of host extirpation and successfully eradicate the pathogen. Improving the survival of infected hosts is most likely to be detrimental as it increases the potential for pathogen transmission and spread. Active removal of a large proportion of the host population has some potential to locally eradicate Bsal and interrupt its spread, depending on the presence of Bsal reservoirs and on the host's spatial dynamics, which should therefore represent research priorities. Synthesis and applications . Mitigation of Batrachochytrium salamandrivorans epidemics in susceptible host species is highly challenging, requiring effective interruption of transmission and radical removal of host individuals. More generally, our study illustrates the advantages of framing conservation science directly in the management decision context, rather than adapting to it a posteriori .

Journal of Applied Ecology↗
USGS2021

Optimal allocation of law enforcement patrol effort to mitigate poaching activities

Poaching is a global problem causing the decline of species worldwide. Optimizing the efficiency of ranger patrols to deter poaching activity at the lowest possible cost is crucial for protecting species with limited resources. We applied decision analysis and spatial optimization algorithms to allocate efforts of ranger patrols throughout a national park. Our objective was to mitigate poaching activity at or below management risk targets for the lowest monetary cost. We examined this trade‐off by constructing a Pareto efficiency frontier using integer linear programming. We used data from a ranger‐based monitoring program in Nyungwe National Park, Rwanda. Our measure of poaching risk is based on dynamic occupancy models that account for imperfect detection of poaching activities. We found that in order to achieve a 5% reduction in poaching risk, 622 ranger patrol events (each corresponding to patrolling 1‐km 2 sites) were needed within a year at a cost of US$49,760. In order to attain a 60% reduction in poaching risk, 15,560 patrol events were needed at a cost of US \$ 1,244,800. We evaluated the trade‐off between patrol cost and poaching risk based on our model by constructing a Pareto efficiency frontier and park managers found the solution for a 50% risk reduction to be a practical trade‐off based on funding constraints (comparable to recent years) and the diminishing returns between risk mitigation and cost. This expected reduction in risk required 8,558 patrol events per year at a cost of US \$ 684,640. Our results suggest that optimal solutions could increase efficiency compared to the actual effort allocations from 2006 to 2016 in Nyungwe National Park (e.g., risk reductions of ~30% under recent budgets compared to ~50% reduction in risk under the optimal strategy). The modeling framework in this study took into account imperfect detection of poaching risk as well as the directional and conditional nature of ranger patrol events given the spatial adjacency relationships of neighboring sites and access points. Our analyses can help to improve the efficiency of ranger patrols, and the modeling framework can be broadly applied to other spatial conservation planning problems with conditional, multilevel, site selection.

Nyungwe National Park↗
USGS2017

Strengthening links between waterfowl research and management

Waterfowl monitoring, research, regulation, and adaptive planning are leading the way in supporting science-informed wildlife management. However, increasing societal demands on natural resources have created a greater need for adaptable and successful linkages between waterfowl science and management. We presented a special session at the 2016 North American Duck Symposium, Annapolis, Maryland, USA on the successes and challenges of linking research and management in waterfowl conservation, and we summarize those thoughts in this commentary. North American waterfowl management includes a diversity of actions including management of harvest and habitat. Decisions for waterfowl management are structured using decision analysis by incorporating stakeholder values into formal objectives, identifying research relevant to objectives, integrating scientific knowledge, and choosing an optimal strategy with respect to objectives. Recently, the consideration of the value of information has been proposed as a means to evaluate the utility of research designed to meet objectives. Despite these advances, the ability to conduct waterfowl research with direct management application may be increasingly difficult in research institutions for several reasons including reduced funding for applied research and the lower perceived value of applied versus theoretical research by some university academics. In addition, coordination between researchers and managers may be logistically constrained, and communication may be ineffective between the 2 groups. Strengthening these links would help develop stronger and more coordinated approaches for the conservation of waterfowl and the wetlands upon which they depend.

Journal of Wildlife Management↗
USGS2013

On the role of budget sufficiency, cost efficiency, and uncertainty in species management

Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info-gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one's budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance.

Nebraska↗
USGS2014

Identifying objectives and alternative actions to frame a decision problem.

In this chapter, we discuss the role of objectives and alternative actions in framing a natural resource management decision problem, with particular attention to thresholds. We outline a number of considerations in developing objectives and measurable attributes, including when utility thresholds may be needed to express the decision-makers’ values.We also discuss the development of a set of alternative actions, and how these might give rise to decision thresholds, particularly when the predictive models contain ecological thresholds. Framing of a decision problem plays a central role in decision analysis because it helps determine the needs for a predictive ecological model, the type of solution method required, and the value and structure of a monitoring system.

Book chapter↗
USGS2018

Identifying management-relevant research priorities for responding to disease-associated amphibian declines

A research priority can be defined as a knowledge gap that, if resolved, identifies the optimal course of conservation action. We (a group of geographically distributed and multidisciplinary research scientists) used tools from nominal group theory and decision analysis to collaboratively identify and prioritize information needs within the context of disease-associated amphibian decline , in order to develop a strategy that would support US management agency needs. We developed iterated influence diagrams to create and assess a unified research strategy. We illustrated a transparent process for identifying specific knowledge gaps in amphibian disease ecology relevant to environmental management, and then constructed a research plan to address these uncertainties. The resulting priorities include a need to: (1) understand the drivers of the community-disease relationship, (2) determine the mechanisms by which exposure to contaminants influence disease outcomes, (3) identify elements of terrestrial and aquatic habitats that stabilize host-pathogen dynamics, (4) discuss how metapopulations may be managed to reduce the speed and intensity of disease outbreaks, and (5) define the relationship between habitat management and the environmental and host microbiomes. Along with identifying research priorities for disease management, we present the details of the process used to develop a consensus plan for addressing disease-related declines in amphibians on federally managed lands of the United States.

Global Ecology and Conservation↗