Using forest inventory and analysis data and the forest vegetation simulator to predict and monitor fisher (Martes pennanti) resting habitat suitability
By: William J. Zielinski ... [et al.].
Contributors: Pacific Southwest Research Station
Series: General technical report PSW-GTR -- 232; General technical report PSW -- 232.
Publisher: United States Dept. of Agriculture, Forest Service, Pacific Southwest Research Station
Published: 2010
Place of publication: Albany, Calif
Language: eng
Number of pages: 31
Subjects: Computer simulation; Forest surveys; Habitat; Fisher (Mammal); Data processing; Forest plants
Description: New knowledge from wildlife-habitat relationship models is often difficult to implement in a management context. This can occur because researchers do not always consider whether managers have access to information about environmental covariates that permit the models to be applied. Moreover, ecosystem management requires knowledge about the condition of habitats over large geographic regions, whereas most research projects have limited spatial inference. For example, research has revealed much about the habitat of fishers (Martes pennanti) at various research sites in California, yet this work has not been translated into practical tools that managers can use to monitor fisher habitat regionally, or to evaluate and mitigate the effects of proposed forest management on fisher habitat. This led us to create new habitat models that are intimately linked to agency approaches to forest monitoring and software tools used by forest managers to plan timber harvests and vegetation management. We created habitat models that were integrated with these approaches and tools that forest managers use for two purposes: to inventory forest resources (i.e., Forest Inventory and Analysis [FIA] plots) and to simulate the response of stands to harvest, fire, insects, disease, and other disturbances (i.e., Forest Vegetation Simulator [FVS]). In this paper we provide an example of how to assess and monitor wildlife habitat using FIA vegetation monitoring protocols. We also provide an example of how to integrate an existing FIA-based model of fisher resting habitat into FVS, software that simulates the effect of alternative silvicultural treatments on vegetation data collected from field plots. Using these tools we produce quantitative predictions of the status of resting habitat quality for fishers, and describe how it can be monitored over time. We also provide an example of the effect of vegetation treatments on predicted fisher resting habitat, which illustrates a process that can be used to understand, reduce, or mitigate the effects of these activities on fisher habitat. This work on the fisher provides one example of how habitat assessments for wildlife could be advanced if they were developed with management applicability and implementation success as a goal.
Notes: Cover title.

"October 2010."

Includes bibliographical references (p. 20-23).

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