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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #415852
Research Project: Knowledge Systems and Tools to Increase the Resilience and Sustainablity of Western Rangeland Agriculture

Location: Range Management Research

Title: Forest Resource Index for Decisions in Adaptation (FRIDA), a library of resources for forest stewardship in the Southwest

Author
item Elias, Emile
item Kramer, Lauren

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2024
Publication Date: 11/22/2024
Citation: Elias, E.H., Kramer, L.R. 2024. Forest Resource Index for Decisions in Adaptation (FRIDA), a library of resources for forest stewardship in the Southwest. Meeting Abstract. Abstract.

Interpretive Summary:

Technical Abstract: As the climate becomes hotter and drier, forests in the Southwest are experiencing more intense drought, wildfires, and pest pressure. Forested ecosystems provide essential ecosystem services by providing habitat, clean water, and economic and cultural benefits. Resource managers rely on resources and decision-support tools to help forests adapt to a changing climate. However, forest information and resources are often created with limited coordination. This lack of coordination, and the sheer number of resources available, leads to decision-makers' inability to assess options and choose the most appropriate resource for their specific objectives. In response to this challenge and in collaboration with the South Central and Southwest Climate Adaptation Science Centers (CASCs), the USDA Southwest Climate Hub has developed the Forest Resource Index for Decisions in Adaptation or FRIDA. FRIDA is an online library of decision-support tools and resources to help support climate change adaptation decision-making and forest stewardship in the Southwest. FRIDA allows managers and decision-makers to easily query based on their objectives and area(s) of interest. Resources can be filtered by topic area, region/state, platform type, and vegetation type to efficiently find the most relevant region-specific tools or resources to best fit their needs.