Development and Testing of Linear Regression Models Predicting Bird-Habitat Relationships

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Abstract
We used an existing forest inventory data base to develop models predicting the abundance of birds collected during the summers of 1983-85 in a mixed-conifer forest of the western Sierra Nevada. Stepwise multiple linear regression was used to develop models for 21 species of birds. Adjusted coefficients of multiple determination (R2) were low, ranging from 0.02 to 0.24. We used 1984 count data to validate models developed during 1983 ("same place, different time" validation). Most predictions ranged from 25-50% underestimates of observed values. We combined 1983 and 1984 data to produce models used to predict count data collected during 1985 from different locations ("different place, different time" validation). Predictions were about 50-75% underestimates of observed values. Most observed values, were, however, within the confidence intervals generated from the predictive equations. Although our final regression models were successful in predicting presence-absence of most species, it is doubtful that forest inventory systems can be used to predict bird abundance.

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