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21-14. A data fusion model for predicting drought-related changes in surface waters and species distribution dynamics


Closing Date: November 1, 2022

This Research Opportunity will be filled depending on the availability of funds. All application materials must be submitted through USAJobs by 11:59 pm, US Eastern Standard Time, on the closing date.

Please communicate with individual Research Advisor(s) on the right to discuss project ideas and answer specific questions about the Research Opportunity.

How to Apply


Understanding how drought will affect water resources and species is crucial to our ability to manage for societal and ecological priorities. Responses by managers to alleviate threats such as drought are often limited by uncertainty about how likely specific actions are to produce intended outcomes. Models that integrate multiple sources of species, habitat, and climate information — and the uncertainty associated with each source of information and the resultant predictions — can help improve management outcomes and the USGS’ ability to meet administration priorities like the America the Beautiful Initiative and the Bureau Infrastructure Law. To provide this information, USGS needs new modeling tools that can leverage information from several data streams such as traditional surveys, citizen science records, and emerging sources of ‘big data’.

Drought is an increasing threat to ecosystems and infrastructure across the United States. Because water is such an important component of many societal and ecological issues, and because the USGS has perhaps the most comprehensive set of infrastructure, data, and expertise related to water and the implications of its behavior, the USGS should lead the way in understanding and modeling the entire ecohydrological system. Many of the components required to develop and advance tools and models that predict dynamics of surface waters and species occurrences are already present in the USGS. However, a comprehensive approach that integrates capabilities from several mission areas in the USGS, which will ultimately improve predictive abilities, is still missing.

We propose a novel framework for creating Integrated Species and Habitat Response Models (ISHRM) that build on recent advances in integration of multiple data sources. This new framework would incorporate mechanistic modeling of ecosystem processes (particularly dynamics of surface waters), quantification and explicit incorporation of uncertainty, and provide a means to estimate how species and habitats will be affected by drivers such as drought. Potential examples of improved management outcomes include decisions such as where to construct ponds to support imperiled species, management of small reservoirs, and forecasts for harmful algal blooms.

Data fusion and integrated model approaches can improve predictions by treating each data set according to the advantages and limitations of how the data were collected, and can accommodate multiple sources of uncertainty. The methods for updating estimates with new information is straight forward with Bayesian model updating approaches. However, making these advancements will require creative development of models as there are a number of possible routes for (1) dealing with individual dataset models and (2) integration of models which account for observation and prediction errors and link ecological submodels.

The candidate will develop dynamic distribution models that integrate spatial and temporal variation from multiple data sources, interface with models of drivers (e.g., drought) and ecosystem response, and forecast ecosystem and species responses to environmental change. An essential aspect of making next-generation predictions of current and future distributions is to use statistical models that incorporate observation error from every data source.

There is broad latitude for contributions from the Mendenhall Fellow within this framework. We anticipate that the primary contributions will be 1) linking the biology models with the climate and environmental models and 2) interacting closely with natural resources managers and policy makers to incorporate the models into a structured decision process to identify cost/benefits of alternative management strategies. Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.

Proposed Duty Station(s): Turners Falls, Massachusetts

Areas of PhD: Ecology, biomathematics, statistics, or related fields (candidates holding a Ph.D. in other disciplines but with knowledge and skills relevant to the Research Opportunity may be considered).

Qualifications: Applicants must meet one of the following qualifications: Research Biologist, Research Ecologist, Research Statistician, or Research Wildlife Biologist.

(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.)

Human Resources Office Contact:  Jes Welsh, 703-648-7414,

Apply Here