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Published on: October 1, 2011
Phylogeny, regression, and the allometry of physiological traits
Michael P O'Connor1, Salvatore J Agosta, Frank Hansen
1Department of Bioscience and Biotechnology, Drexel University, Philadelphia, Pennsylvania 19104, USA. mike.oconnor@drexel.edu
The American Naturalist
|September 20, 2007
Summary
This study reveals that standard regression methods can bias allometric exponent estimates in evolutionary biology. Using phylogenetically independent contrasts and appropriate regression models like LSVOR is crucial for accurate slope estimation in related species.
Area of Science:
- Evolutionary Biology
- Comparative Physiology
- Ecology
- Biometry
Background:
- Physiological and ecological allometries frequently involve noncausal, phylogenetically autocorrelated variables, posing challenges for linear regression analysis.
- Traditional ordinary least squares (OLS) regression is commonly used, despite limitations in handling correlated data and variation in both independent and dependent variables.
- Existing remedies like phylogenetically independent contrasts and reduced major axis (RMA) regression are not universally applied in functional allometry studies.
Purpose of the Study:
- To investigate the impact of different regression methodologies and phylogenetic contrasts on estimating regression slopes for phylogenetically constrained data.
- To evaluate the bias and accuracy of ordinary least squares (OLS), reduced major axis (RMA), and a modified orthogonal (least squares variance-oriented residual [LSVOR]) regression.
- To assess the influence of phylogenetic structure and data censoring (simulated extinction) on regression slope estimation.
Main Methods:
- Simulated Brownian diffusive evolution of functionally related biological characters.
- Applied ordinary least squares (OLS), reduced major axis (RMA), and least squares variance-oriented residual (LSVOR) regression to simulated data.
- Incorporated phylogenetically independent contrasts to account for evolutionary history.
- Simulated data set censoring through taxon extinction to test model robustness.
Main Results:
- Both OLS and RMA regressions exhibited significant bias in estimated regression slopes under various simulated conditions.
- The modified orthogonal LSVOR regression demonstrated reduced bias compared to OLS and RMA.
- Failure to utilize phylogenetic contrasts for phylogenetically structured data led to overestimated regression strength and increased slope estimate variance.
- Simulated extinction events did not alter the importance of appropriate regression models or phylogenetic contrasts.
Conclusions:
- Standard regression techniques (OLS, RMA) can produce biased estimates of allometric exponents when dealing with phylogenetically autocorrelated data.
- The LSVOR regression method offers a less biased alternative for analyzing functional allometries.
- Employing phylogenetically independent contrasts is essential for accurate slope estimation in studies of evolutionary allometry, regardless of simulated extinction events.
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