|dc.description.abstract||1. The criteria for choosing the appropriate line-fitting method (LFM) and correction estimator for
determining the functional allometric relationship, and for predicting the Y-variable accurately are
controversial. A widely accepted criterion for reducing bias in allometric prediction is to minimize
the mean squared residual (MSR) on the antilog scale, and a series of correction estimators have
been designed precisely to achieve this.
2. Here, using parameter landscapes, we examine the performance of the correction estimators and
several LFMs under different data reszidual shapes, sample sizes and coefficients of determination.
3. Predictions from the nonlinear LFMwere found to have minimumMSRvalues (minimumbias),
but with obviously skewed frequency distributions of the predicted Y-variable compared with
observed data. This implies that using MSR as a bias measure for allometric prediction could be
4. We introduce a new bias measure, the discrepancy of the frequency distributions of the Y-variable
between predicted and observed data, and suggest that the reduced major axis method is the
least biased method in most cases, both on the logarithmic and antilog scales.
5. Parameter landscapes clearly illustrate the performance of each LFMand correction estimator,
as well as the best solution given specified criteria. We therefore suggest a shift in emphasis from
designing more sophisticated LFM or correction estimators (equal to finding the peaks in
the parameter landscape) to justifying the measure of bias and performance criterion in allometric