Simulating the effect of landscape size and age structure on cavity tree density using a resampling technique

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 R 2 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.