Early Warning Program to Detect and Identify Emerging Contaminants and Their Effects to Fish and Wildlife
Sector: Water Supply and Storage • Location: United States of America
Source: Grants.gov
To assess the risks of Contaminants of Emerging Concern (CECs) on aquatic wildlife, the St. Cloud State University (SCSU) Aquatic Toxicology Laboratory will assist the US Fish and Wildlife Service in collecting, analyzing and interpreting fish samples collected across several streams draining into the Great Lakes. Specifically, wild (walleye, bass, and or other species) and caged (sunfish) fish wi
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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
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Description
Description | To assess the risks of Contaminants of Emerging Concern (CECs) on aquatic wildlife, the St. Cloud State University (SCSU) Aquatic Toxicology Laboratory will assist the US Fish and Wildlife Service in collecting, analyzing and interpreting fish samples collected across several streams draining into the Great Lakes. Specifically, wild (walleye, bass, and or other species) and caged (sunfish) fish will be collected in multiple segments of three streams in the Great Lakes watershed and assessed for physiological indicators of CEC exposure (plasma vitellogenin concentrations) and anatomical alterations associated with CEC exposure (histopathology, organosomatic indices). Furthermore, the SCSU Aquatic Toxicology Laboratory will expose fathead minnow larvae in the laboratory to waters collected at field sites and assess their predator avoidance performance, a sensitive indicator to CEC exposure with immediate ecological consequences. Taken together, these data sets will generate linkages between field sites and the effects of CEC exposure that will provide resource managers with information to reduce adverse impacts of CECs on exposed fish. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
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Country | Obfuscated |
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County | Obfuscated |
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Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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