Area:
GABA transporter, nicotinic receptor
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High-probability grants
According to our matching algorithm, Jia Hu is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2015 — 2018 |
Hu, Jia |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Landscape Heterogeneity Modulates the Sensitivity of Forested Ecosystems to Climate @ Montana State University
The frequency, duration and severity of droughts are increasing across the western United States and are affecting the growth of trees in forests. However, predicting how the growth of specific forests will respond is difficult, especially in mountainous areas of the west that are characterized as having steep gradients in climate related to complex local topography. For example, the topography across a western landscape can affect the availability of water and light for the growth of trees and other plants, and the growth responses will depend on specific physiological attributes that vary highly across tree species. The main objective of this study is to link three factors (variability in climate, the physical characteristics of watersheds, and the physiology of vegetation) in order to better understand and predict forest productivity across mountainous landscapes of the west using model study sites in Montana and Oregon. The study will increase participation of underrepresented minorities in science, technology, engineering and mathematics through development of K-12 lesson plans and science kits for rural schools across Montana. The study will also be carried out in collaboration with the Montana Climate Office to more effectively disseminate results to stakeholders and decision makers in the western United States related to natural resource management and stewardship.
Scientists, forest managers and decision makers must be prepared to understand, respond and adapt to the effects of a changing climate. This project will lead to improved predictions of how forests will respond to changes in climate in the future by more clearly linking the physical characteristics of mountain landscapes and the physiological attributes of trees in spatially explicit computer models of forest productivity. The methods to be used include a range of hydrological, tree physiological, and isotopic measurements, all nested within large-scale satellite remote sensing estimates of forest productivity. This project will merge concepts and methods from the fields of ecology and hydrology to provide a more comprehensive understanding of vegetation sensitivity to climate as related to its position in the landscape.
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0.945 |