Cooperative Ecosystem Studies Unit, Rocky Mountain CESU

The summary for the Cooperative Ecosystem Studies Unit, Rocky Mountain CESU grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Geological Survey, which is the U.S. government agency offering this grant.

Cooperative Ecosystem Studies Unit, Rocky Mountain CESU: The US Geological Survey, Northern Rocky Mountain Science Center (NOROCK) is offering a funding opportunity to a CESU partner to review the integration of heterogeneous data as this topic has been identified as a pressing challenge. Applications in ecology dealing with multiple data sources and/or species types need further study and methodological developments. Typically, ecological settings and problems require spatial and temporal dependencies. With this opportunity, the Center would like to conduct a review of Bayesian methods for integrating multi-type and multi-species ecology data with an emphasis on spatial and spatiotemporal settings. Upon completion, this review should serve as a bridge for the submission of additional proposals to other partners focusing on integrated population models (IPMs) and Bayesian hierarchical models for multi-species data. IPMs provide a way to combine heterogeneous data sources for combined inferences. These models are necessary in many ecological settings where data collection can be expensive and difficult, therefore requiring the combination of data sets of different types and spatial domains. Many times in ecological settings, multi-species data sources are often incorrectly handled by failing to account for differences between species, which leads to inaccurate inferences and unidentified uncertainties. Conventional IPMs make strong assumptions about the independence of the different data sets, which may not always be reasonable. Additionally, analyzing data with spatial and spatiotemporal structures can present challenges in IPMs.