Modeling effects of CEC exposures on fish populations within the Great Lakes Basin
Sector: Advanced Electronics • Location: United States of America
Source: Grants.gov
The U.S. Fish and Wildlife Service (USFWS) seeks an ecological modeler with experience developing and applying fish and wildlife population models in chemical risk assessments. This project is funded under the Great Lakes Restoration Initiative, and managed from the USFWS Region 3 Office in Bloomington, MN. The modelers will be partners in a team of scientists who, since 2010, have been investig
Project Information FAQ
Project Information
Want to explore the full details? View the full report
Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | archived |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | The U.S. Fish and Wildlife Service (USFWS) seeks an ecological modeler with experience developing and applying fish and wildlife population models in chemical risk assessments. This project is funded under the Great Lakes Restoration Initiative, and managed from the USFWS Region 3 Office in Bloomington, MN. The modelers will be partners in a team of scientists who, since 2010, have been investigating the potential for impacts in fish populations from exposures to contaminants of emerging concern (CEC) in Great Lakes tributaries. Population modeling is one of several tools USFWS will utilize in a weight-of-evidence risk assessment of CEC impacts in Great Lakes aquatic systems. The entire modeling effort must be designed, conducted and completed by September 28th, 2018 (the close of FY2018). The final product will include a fully functioning model that can be used to simulate population dynamics based on effects related to CEC exposure, and a detailed final report that describes model structure, input datasets, variables, simulations and results, assumptions, and uncertainties. The project will also meet the goals and objectives of the USFWS's CEC investigation and priorities of University of Minnesota, Venturelli Lab. There is not an application process for this funding opportunity; this is a notice of intent to award a single source cooperative agreement to the University of Minnesota- St. Paul Venturelli Lab under justification 505DM 2.14B. 2 and 4 without competition. Competition for the award is not practical as it is a the intended recipient is uniquely qualified to perform the project duties based on technical expertise in fish population modeling, specifically with sturgeon modeling in regards to environmental toxicity. The intended recipient has already been briefed on project goals and objectives and has agreed they align with their own lab's goals and objectives making collaboration ideal and successful for project completion. The intended recipient has also collaborated with other project members in the past and has good working relationships with the research team making them an excellent candidate and award recipient. The funding instrument will be a Cooperative Agreement as substantial involvement on the part of the USFWS is required for the successful completion of the activities to be funded. In particular, the USFWS will be responsible for the following; working/coordinating with project partners to identify specific species to be modeled, providing relevant data to assist with model formation, and providing technical expertise. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
Project Type | Obfuscated Data |
Article Published Date | Obfuscated Data |
