Non-Fiction Books:

Development of Lichen Response Indexes Using a Regional Gradient Modeling Approach for Large-Scale Monitoring of Forests

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$47.00
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Description

Development of a regional lichen gradient model from community data is a powerful tool to derive lichen indexes of response to environmental factors for large-scale and long-term monitoring of forest ecosystems. The Forest Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture Forest Service includes lichens in its national inventory of forests of the United States, to help monitor the status of forested ecosystems. Development of a model for a specific region to calculate lichen response indexes that are correlated with air quality and major climate factors, and are also independent of each other, is a critical step in achieving program goals. These indexes are the primary lichen bioindicators used in FIA for assessing regional patterns and monitoring trends of lichen response to environment over time. This general approach is also applicable to other monitoring efforts. A first step in the modeling process is to identify an appropriate geographic region for a model. Unconstrained ordination alone, or combined with indicator species analysis followed by regression analysis, are two approaches borrowed from plant ecology that have been shown to generate successful regional lichen gradient models. Calculation of lichen response indexes for new plots not part of the original model is necessary to support long-term monitoring. We explain the rationale for recommended approaches, describe in detail the recommended steps in the modeldevelopment process, and explain how to document and evaluate results, all to support successful application of a model for monitoring. A template is included for documenting a model and archiving all products necessary to understand and apply it, as is required for each FIA model.
Release date NZ
February 14th, 2015
Audience
  • General (US: Trade)
Pages
72
Dimensions
216x280x4
ISBN-13
9781506120218
Product ID
37690361

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