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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #409885
Research Project: Science and Technologies for the Sustainable Management of Western Rangeland Systems

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 Kramer, Lauren
item Elias, Emile

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/1/2024
Publication Date: 5/16/2024
Citation: Kramer, L.R., Elias, E.H. 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 in the Southwest United States, forests are experiencing more drought, wildfires, and pest pressure. Forested ecosystems provide essential services such as providing habitat, clean water, and economic and cultural benefits. Forest managers rely on resources and decision-support tools to help forests adapt to a changing climate. However, forest tools and resources are often created with limited coordination. This lack of coordination, along with the sheer number of resources available, leads to an inability on the part of decision-makers to assess options and choose the most appropriate action 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. Users can filter resources by topic, region/state, resource platform, and vegetation type to efficiently find the most relevant region-specific tools and resources to best fit their needs.