R--Development of river-specific population decision support tools for Gulf sturge

The United States Geological Survey (USGS), Eastern Ecological Science Center (EESC) in Kearneysville, WV, requires services to develop river-specific population decision-support tools for Gulf sturgeon, a species of federal management concern. Research will focus on synthesizing existing adult population growth and seniority probability data to produce quantitative recovery criteria, including refinement of modeling approaches and decision frameworks necessary to optimize output for regulatory and management needs. Services include consultation and recommendations on the interpretation of population trends and the translation of model outputs into recovery-plan-ready products and Section 7 consultation guidance. Services include model development and iteration, synthesis of published population data, production of river-specific status diagnostics, creation of a decision framework, preparation of regulatory-ready deliverables, and a final report.

The total anticipated period of performance will be from date of award to June 30, 2026.

The USGS is aware of no other source that can provide the above. Therefore, it intends to make an award to Big Fin Scientific unless it is contacted by any vendor who believes they have similar technical expertise and can provide the same service(s). Vendors are invited to contact the Contract Specialist, Miranda Blankenship, via email at Miranda_blankenship@ios.doi.gov no later than 1:00 PM EDT, Wednesday, May 20th, 2026, with an affirmative answer.

Sole Source Justification States the Following Parts:

Contracting Agency and Activity: The Department of the Interior, United States Geological Survey (USGS), Eastern Ecological Science Center (EESC) in Kearneysville, WV.

Nature of Action Being Approved: Firm fixed price award to the Big Fin Scientific for Gulf sturgeon decision support model to assisting in listing decisions under the Endangered Species Act.

Statutory Authority. The proposed action may be awarded on a sole source basis in accordance with: FAR 13.106-1(b)(1) ¿Only One Source Reasonable Available¿

Rationale Supporting Use of Citation in No. 5. Big Fin Scientific is the only entity reasonably available that possesses the specific, project-critical expertise required to develop defensible, river-specific population decision-support models for Gulf sturgeon within the required timeframe. This determination is based on the vendor¿s direct role in generating and interpreting the foundational demographic parameters that underpin the proposed modeling effort, combined with demonstrated quantitative expertise and established federal partnerships necessary to execute the work.

Staff affiliated with Big Fin Scientific have developed and published the life-history, survival, and population growth relationships that form the basis of current Gulf sturgeon demographic understanding across multiple river systems. They hold unique, non-transferable knowledge of parameter derivation, underlying assumptions, data limitations, and appropriate application within regulatory decision frameworks. This expertise is coupled with unique quantitative skills in population modeling, including development and application of structured demographic models, uncertainty propagation, and decision-support frameworks.

This level of system-specific and model-specific knowledge is essential for this effort. The project requires synthesis of published demographic data and accurate reconciliation of differences among river systems, refinement of parameter estimates for management use, and translation of these results into recovery criteria and Section 7 consultation tools. Misinterpretation or inappropriate application of these parameters would directly compromise the scientific defensibility of resulting recovery benchmarks and regulatory guidance. No other vendor possesses equivalent familiarity with both the origin and appropriate use of these datasets at the level required to ensure technically sound and policy-relevant outputs.

Big Fin Scientific¿s prior collaboration with USGS on closely related efforts ensures continuity in analytical approach, parameterization, and decision framework development. This continuity is critical for maintaining consistency with previously developed products, avoiding redundant reanalysis, and ensuring that updated models remain directly comparable to prior work used in management and regulatory contexts.

Given the highly specialized nature of the work, the need for immediate application of existing system-specific expertise, and the importance of established interagency coordination, only Big Fin Scientific can meet both the technical and schedule requirements without unacceptable risk to project completion or scientific integrity.

The Efforts to Identify Additional Sources Including the Market Research Conducted: Market research conducted via the Internet and call to industry professionals shows that no other sources are known to possess the unique and essential qualifications necessary to develop the Gulf sturgeon decision support model. Due to the project¿s compressed timeline to meet the EMA redirect deadline of June 30, 2026, no other contractor possesses the specific expertise needed for rapid, accurate development of regulatory-ready decision frameworks without prior knowledge of the system.

Award to any other source would require substantial duplication of cost and time to independently reconstruct the underlying demographic datasets, re-derive parameter estimates, and re-establish the necessary technical and interagency coordination. This duplication would introduce unacceptable delays relative to the required June 30, 2026 EMA redirect deadline and would increase the risk of inconsistencies with prior analyses currently used in management and regulatory decision-making.

Future Plans to Permit Competition: The government shall conduct continuing and future market research to identify alternatives for similar testing. Any future requirements will be competed to the maximum extent practical.