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dc.contributor.authorHui, C.
dc.contributor.authorTerblanche, J.S.
dc.contributor.authorChown, S.L.
dc.contributor.authorMcGeoch, M.A.
dc.date.accessioned2010-05-25T09:20:17Z
dc.date.available2010-05-25T09:20:17Z
dc.date.issued2010
dc.identifier.issn2041-210Xen
dc.identifier.urihttp://hdl.handle.net/123456789/545
dc.description.abstract1. 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 misleading. 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 prediction.en
dc.format.extent647851 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.subjectallometryen
dc.subjectlinear regressionen
dc.subjectlog transformationen
dc.subjectpower lawen
dc.subjectscalingen
dc.titleParameter landscapes unveil the bias in allometric predictionen
dc.typeJournalArticlesen
dc.cibjournalMethods in Ecology and Evolutionen
dc.cibprojectLarge-scale patterns in diversityen


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