for Understanding Regional Patterns Relevant to Stakeholders
The Northern Interior region of Alaska is undergoing rapid social and environmental change due in part to recent decades of a marked warming climate. This warming is shifting seasonal patterns and affecting ecosystem services, with localized impacts on subsistence people and resource management. The...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.577.8158 http://www.2007amsterdamconference.org/Downloads/AC2007_McNeeley.pdf |
| Summary: | The Northern Interior region of Alaska is undergoing rapid social and environmental change due in part to recent decades of a marked warming climate. This warming is shifting seasonal patterns and affecting ecosystem services, with localized impacts on subsistence people and resource management. There is a paucity of data for the region; however, disparate sets of biological and weather data do exist, along with a growing archive of traditional ecological knowledge (TEK) documenting local observations of change and effects. This project is a part of collaboration between McNeeley, Alaska Native (Koyukon and Gwich’in Athabascan) communities, U.S. Fish and Wildlife Service (USFWS), and the Alaska Climate Research Center (ACRC), which aims to integrate these datasets by framing questions within the context of local observations, and asking questions of relevance to stakeholders (i.e., local residents, agency personnel, and scientists who study arctic environmental change). As we collectively grapple with how to understand and adapt to these changes while sustaining valuable resources and ecosystem services, scaling data and observations of change to a local/regional level while asking the appropriate questions of those data is critical to increase our understanding of the change and to devise adaptive strategies for management and decision making. The collaborators will be performing statistical and content analysis on |
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