Cooperative Ecosystem Studies Unit, Pacific Northwest CESU

Funding Opportunity ID: 328718
Opportunity Number: G20AS00134
Opportunity Title: Cooperative Ecosystem Studies Unit, Pacific Northwest CESU
Opportunity Category: Discretionary
Opportunity Category Explanation:
Funding Instrument Type: Cooperative Agreement
Category of Funding Activity: Science and Technology and other Research and Development
Category Explanation:
CFDA Number(s): 15.808
Eligible Applicants: Others (see text field entitled “Additional Information on Eligibility” for clarification)
Additional Information on Eligibility: This financial assistance opportunity is being issued under a Cooperative Ecosystem Studies Unit (CESU) Program. CESU¿s are partnerships that provide research, technical assistance, and education. Eligible recipients must be a participating partner of the Pacific Northwest Cooperative Ecosystem Studies Unit (CESU) Program
Agency Code: DOI-USGS1
Agency Name: Department of the Interior
U. S. Geological Survey
Posted Date: Aug 17, 2020
Close Date: Sep 04, 2020
Last Updated Date: Aug 17, 2020
Award Ceiling: $80,394
Award Floor: $0
Estimated Total Program Funding: $80,934
Expected Number of Awards: 1
Description: The USGS is offering a funding opportunity to a CESU partner for developing research to assess the vulnerability of species to environmental change is an important management challenge, particularly for poorly studied species for which species status assessments are required. Agencies such as the Fish and Wildlife Service are tasked with assessing species sensitivities to environmental conditions, their exposure to, and ability to adapt to changing conditions. Yet, defensible assessments currently require detailed knowledge of species-specific traits and ecologies and this information is hard to come by. Vulnerability assessments for lesser-studied species can be extremely challenging. Most vulnerability assessment methods and frameworks are developed using well-studied species and their applicability to species with poorly understood traits and ecologies is questionable. Advances in machine learning and statistical clustering can provide new ways of simply and defensibly assessing sensitivity and adaptive capacity for priority species. A predictive model and associated classification tree can provide an accessible, transparent, and repeatable means of the vulnerability for lesser studied species, lessening the research burden of agencies and staff.
Version: 2
Modification Comments: to extend due date

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