Scientific findings should facilitate decision-making activities focused on natural resources and the environment, but presently raw scientific information tends to be documented in forms that are not immediately actionable for policy makers or resource managers. Practical approaches are needed to interpret and associate diverse forms of scientific and societal information in ways that are fully describable and meaningful to a diverse audience. Disconnects in knowledge transfer arise when decisions are made in isolation or occur sequentially as a follow-up to scientific inquiry, which creates separation between the pursuit of scientific findings and decision-making activities. Other disconnects arise where resource managers are not able to access technical content because of barriers in understanding or where scientific information is too far off topic. The common scenario is that decision makers cannot easily interpret scientific information and scientists driven by scientific goals do not fully address decision makers' information needs.
Improved techniques and analysis tools are needed to bridge the gap between science and decision making, which may involve several connection points. Decision making in complex situations often deals with a large degree of uncertainty - data may be sparse, incomplete, or insufficient in quality. Policy decisions, in particular, often require a significant amount of deliberation because decisions involve important consequences, conflicting objectives and timelines, multiple stakeholders (priorities), and issue complexity. To increase the availability and relevance of scientific information for such decisions, practical approaches are needed to associate and evaluate diverse scientific and societal information. As a result, by including decision maker perspectives and by presenting scientific information in more meaningful ways, scientific contributions will be better aligned with societal priorities. Agencies at all levels could use a comparative evaluation and analysis of alternative decisions to their advantage by determining "optimal" decisions for a particular issue under different potential criteria sets. If those criteria sets represent different stakeholder perspectives, such comparisons would shed light on the specific issues and tradeoffs that might need to be resolved for an appropriate solution to emerge.
We seek a Mendenhall postdoctoral scholar to conduct leading research in one or more topical areas described below. The Mendenhall Fellow will be a member of the USGS Biogeographic Characterization Branch of the Core Science Systems Mission Area. She/he will interact primarily in a group partnership with the USGS Science and Decisions Center of the Energy and Minerals Mission Area and the Geosciences & Environmental Change Science Center. The Biogeographic Characterization Branch is actively engaged in providing technical services to enhance data use and integration and to document data provenance. Areas of focus within the biogeographic sciences include a) improving information transfer between scientific and professional communities, b) establishing techniques to integrate and evaluate diverse information used for scientific purposes and decision-making activities, c) advancing the use of analysis tools, including techniques used to support the decision-making process, and d) increasing productivity and reproducibility of scientific investigations using open source code.
We invite proposals that include aspects of biogeographic science, ecosystem services, and decision analysis. The project methodology should include components of qualitative inference and quantitative analysis (statistics, modeling, or probability theory). We expect that the scope of work will possess immediate applicability and the outcomes will be transferable into publically available analytical data services in the future. Also, where relevant it is recommended that projects address uncertainty and risk using established methods either from statistics or probability theory (Bayesian or frequentist approaches). Operationalized methods, where applicable, should be adaptive to different levels of decision granularity or temporal or spatial scales of analysis.
Research and develop logical processors and analytical capabilities that combine key components of translational ecology (biogeography) and ecosystem services/natural capital accounting in a way that allows scientists and stakeholders to guide future research directions, resulting in products that better address important environmental challenges.
Develop analysis tools for combining multiple streams of information in ways that inform land use and resource management decisions, developed through a structured process that brings together scientists, decision makers, and stakeholders to address relevant issues and decisions for a geographic region.
Identify the types of missing data products, scientific products, or analytical tools that scientists should now focus on to better inform decision makers. Operationalize methodologies that advance knowledge transfer in areas where missing components are limiting decision-making activities. Develop code-driven prototypes to demonstrate information transfer to decision makers as a precursor to production level services.
Investigate the content requirements for scientific studies to be most relevant from a decision-maker perspective. Develop analytical tools to guide scientific investigations to be fit-for-purpose based on a series of priorities, including study objectives and stakeholder interests.
Evaluate the strengths and weaknesses of producing, synthesizing and disseminating scientific information over a range of spatiotemporal scales to inform decision makers for different types of study objectives. In addition to national-scale data products and services, evaluate what factors should determine whether local information is also needed. Develop operational procedures to provide metrics to evaluate content suitability. Research the types of analyses and evidence scientists should provide decision makers to convince them of scaling requirements, for instance in cases where national scale data and related scientific findings are correlated to local scale processes and therefore suitable to serve decision making at the local level.
Interested applicants are strongly encouraged to contact the Research Advisors below early in the application process to discuss project ideas. Work will be performed collaboratively with affiliated members on key project components and software developers who bring analysis capabilities and other scientific contributions online for public benefit. Candidates with strong critical thinking skills and an interest in advancing decision-making and synthesizing multidisciplinary information are strongly encouraged to apply. Specialty skills in statistics and probability, coding of algorithms, and modeling are desirable.
Proposed Duty Station: Denver, Colorado
Areas of Ph.D.: Biology, ecology, geology, hydrology, or social sciences. (Candidates holding a Ph.D. in other disciplines, but with knowledge and skills relevant to the Research Opportunity may be considered.)
Qualifications: Research Biologist; Research Ecologist; Research Geologist; Research Hydrologist. (This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)
Research Advisor(s): Tristan Wellman, (303) 202-4081, firstname.lastname@example.org ; Sky Bristol, (303) 578-2127, email@example.com ; Karen Jenni, (303) 236-5766, firstname.lastname@example.org ; Ken Bagstad, (303) 236-1330, email@example.com ; Steve Aulenbach,(303) 202-4226, firstname.lastname@example.org.
Human Resources Office Contact: Katherine Heller, 703-648-7408, email@example.com
|Summary of Opportunities|