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Simulating the Effect of Landscape Size and Age Structure on Cavity Tree Density Using a Resampling Technique

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Abstract:

Cavity-tree density (CTD) is an important indicator of habitat quality for cavity-dependent wildlife. However, the abundance of cavities and cavity trees can vary dramatically, even among trees or stands with similar attributes. This uncertainty can make it difficult to expand stand-level estimates of cavity abundance to large landscapes, although it is often desirable to do so. This limits the utility of CTD as a measure of habitat quality. We use a resampling method (statistical bootstrap) to construct a set of regression models to predict CTD based on landscape age structure and landscape size. The estimated regression coefficients are highly variable (in terms of the adjusted R2 and root mean square error) for landscapes <100 ha, but the models perform well for larger landscapes. We test the regression models using an independent data set from the Missouri Ozark Forest Ecosystem Project (MOFEP) and find that the mean relative error (RE) when predicting CTD for landscapes between 300 and 4,000 ha is less than 10%. Both the size (in hectares) of the landscape and the stand age-class components affect RE; but RE decreases with increasing landscape size in a consistent and quantifiable manner. For Ozark landscapes ≥100 ha, knowledge of the proportion of the area in the seedling/sapling, pole, sawtimber, and old-growth age classes can be used to readily estimate the number of cavity trees and how that number will change if the landscape age structure is altered. FOR. SCI. 50(5):603–609.

Keywords: Missouri; Regression; bootstrap; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; relative error

Document Type: Regular Article

Affiliations: 1: School of Natural Resources Univ. of Missouri 203 ABNR Bldg. Columbia MO 65211-7280, Fax: 573-882-1977, Email: fanzha@missouri.edu 2: Div. of Statistics, College of Science Univ. of Idaho Moscow ID 83843, Email: stevel@uidaho.edu 3: USDA-Forest Service, North Central Res. Sta. Univ. of Missouri 202 ABNR Bldg. Columbia MO 65211-7260, Email: sshifley@fs.fed.us 4: USDA-Forest Service, North Central Res. Sta. Univ. of Missouri 202 ABNR Bldg. Columbia MO 65211-7260, Email: frthompson@fs.fed.us 5: School of Natural Resources Univ. of Missouri 203 ABNR Bldg. Columbia MO 65211-7280, Fax: 573-882-1977, Email: larsendr@missouri.edu

Publication date: October 1, 2004

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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