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Using Forest Inventory and Analysis Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability (Paperback)
Loot Price: R373
Discovery Miles 3 730
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Using Forest Inventory and Analysis Data and the Forest Vegetation Simulator to Predict and Monitor Fisher (Martes pennanti) Resting Habitat Suitability (Paperback)
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Loot Price R373
Discovery Miles 3 730
Expected to ship within 10 - 15 working days
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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 habits 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 to 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.
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