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A Habitat-Based Framework for Communicating Natural Resource Condition
Author(s) -
Tim J. B. Carruthers,
Shawn L. Carter,
Todd R. Lookingbill,
Lisa N. Florkowski,
Jane M. Hawkey,
William C. Dennison
Publication year - 2012
Publication title -
isrn ecology
Language(s) - English
Resource type - Journals
eISSN - 2090-4622
pISSN - 2090-4614
DOI - 10.5402/2012/384892
Subject(s) - habitat , environmental resource management , wetland , resource (disambiguation) , natural resource , vegetation (pathology) , natural (archaeology) , resource management (computing) , ecosystem , environmental science , ecology , geography , computer science , medicine , computer network , archaeology , pathology , biology
Progress in achieving desired environmental outcomes needs to be rigorously measured and reported for effective environmental management. Two major challenges in achieving this are, firstly, how to synthesize monitoring data in a meaningful way at appropriate temporal and spatial scales and, secondly, how to present results in a framework that allows for effective communication to resource managers and scientists as well as a broader general audience. This paper presents a habitat framework, developed to assess the natural resource condition of the urban Rock Creek Park (Washington, DC, USA), providing insight on how to improve future assessments. Vegetation and stream GIS layers were used to classify three dominant habitat types, Forest, Wetland, and Artificial-terrestrial. Within Rock Creek Park, Forest habitats were assessed as being in good condition (67% threshold attainment of desired condition), Wetland habitats to be in fair condition (49% attainment), and Artificial-terrestrial habitats to be in degraded condition (26% attainment), resulting in an assessed fair/good condition (60% attainment; weighted by habitat area) for all natural resources in Rock Creek Park. This approach has potential to provide assessment of resource condition for diverse ecosystems and provides a basis for addressing management questions across multiple spatial scales.

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