1. Allometric exponents support a 3/4 power scaling law Catherine C. Farrell Nicholas J. Gotelli Department of Biology University of Vermont Burlington, VT 05405

2. Gotelli lab, May 2005

4. Allometric Scaling What is the relationship metabolic rate (Y) and body mass (M)?

5. Allometric Scaling What is the relationship metabolic rate (Y) and body mass (M)? Mass units: grams, kilograms Metabolic units: calories, joules, O 2 consumption, CO 2 production

6. Allometric Scaling What is the relationship metabolic rate (Y) and body mass (M)? Usually follows a power function: Y = CM b

7. Allometric Scaling What is the relationship metabolic rate (Y) and body mass (M)? Usually follows a power function: Y = CM b C = constant b = allometric scaling coefficient

8. Allometric Scaling: Background Allometric scaling equations relate basal metabolic rate (Y) and body mass (M) by an allometric exponent (b) Y = Y o M b Log Y = Log Y o + b log M

9. Allometric Scaling: Background Allometric scaling equations relate basal metabolic rate (Y) and body mass (M) by an allometric exponent (b) Y = Y o M b Log Y = Log Y o + b log M b is the slope of the log-log plot!

10. Allometric Scaling What is the expected value of b? ??

11. Hollywood Studies Allometry Godzilla (1954) A scaled-up dinosaur

12. Hollywood Studies Allometry The Incredible Shrinking Man (1953) A scaled-down human

13. Miss Allometry Raquel Welch Movies spanning > 15 orders of magnitude of body mass!

14. 1 Million B.C. (1970)

15. Fantastic Voyage (1964)

16. Alien (1979) Antz (1998) Hollywood (Finally) Learns Some Biology

17. Hollywood’s Allometric Hypothesis: b = 1.0

18. Surface/Volume Hypothesis b = 2/3 Surface area  length 2 Volume  length 3

19. Surface/Volume Hypothesis b = 2/3 Surface area  length 2 Volume  length 3 Microsoft Design Flaw!

20. New allometric theory of the 1990s Theoretical models of universal quarter-power scaling relationships –Predict b = 3/4 –Efficient space-filling energy transport (West et al. 1997) –Fractal dimensions (West et al. 1999) –Metabolic Theory of Ecology (Brown 2004)

21. Theoretical Predictions b = 3/4 –Maximize internal exchange efficiency – Space-filling fractal distribution networks (West et al. 1997, 1999) b = 2/3 –Exterior exchange geometric constraints –Surface area (length 2 ): volume (length 3 )

22. Research Questions Meta-analysis of published exponents 1.Is the calculated allometric exponent (b) correlated with features of the sample? 2.Mean and confidence interval for published values? 3.Likelihood that b = 3/4 vs. 2/3? 4.Why are estimates often < 3/4?

23. Allometric exponent Species in sample TaxonSource 0.71391mammals(Heusner 1991) 0.713321mammals(McNab 1988) 0.69487mammals(Lovegrove 2000) 0.737626mammals(Savage et al. 2004) 0.7410mixed(Kleiber 1932) 0.76228mammals(West et al. 2002) 0.72435passerine birds (Lasiewski and Dawson 1967)

24. Research Questions 1.Is the calculated allometric exponent (b) correlated with features of the sample? 2.Calculate mean & confidence interval for published values? 3.Likelihood that b = 3/4 vs. 2/3 4.Why are estimates often < 3/4?

25. Question 1 Can variation in published allometric exponents be attributed to variation in – sample size –average body size –range of body sizes measured

26. Allometric exponent as a function of number of species in sample Other P = 0.6491 Mammals Allometric Exponent

27. Allometric exponent as a function of midpoint of mass P = 0.5781 Weighted by sample size P = 0.565 Mammals Other Allometric Exponent

28. Allometric exponent as a function of log(difference in mass) P = 0.5792 Weighted by sample size: P =.649 Mammals Other Allometric Exponent

29. Non-independence in Published Allometric Exponents phylogenetic non-independence –species within a study exhibit varying levels of phylogenetic relatedness Bokma 2004, White and Seymour 2003 data on the same species are sometimes used in multiple studies

30. Independent Contrast Analysis Paired studies analyzing related taxa (Harvey and Pagel 1991) –e.g., marsupials and other mammals Each study was included in only one pair No correlation (P > 0.05) between difference in the allometric exponent and –difference in sample size, –midpoint of mass –range of mass

31. Question 1: Conclusions Allometric exponent was not correlated with –sample size –midpoint of mass –range of body size Reported values not statistical artifacts

32. Research Questions 1.Is the calculated allometric exponent (b) correlated with features of the sample? 2.Calculate mean & confidence interval for published values? 3.Likelihood that b = 3/4 vs. 2/3 4.Why are estimates often < 3/4?

33. Question 2: What is the best estimate of the allometric exponent? Mammals Birds Reptiles

34. Allometric Exponent b = 3/4 b = 2/3

35. Allometric Exponent b = 2/3 b = 3/4

36. Allometric Exponent b = 2/3 b = 3/4

37. Question 2: Conclusions Reptiles V ariation is due to small sample sizes and variability in experimental conditions Mammals and Birds Results suggest the true exponent is between 2/3 and 3/4

38. Research Questions 1.Is the calculated allometric exponent (b) correlated with features of the sample? 2.Calculate mean & confidence interval for published values? 3.Likelihood that b = 3/4 vs. 2/3? 4. Why are estimates often < 3/4?

39. Question 3: Likelihood Ratio b = 3/4 : b = 2/3 All species16 074 Mammals105 Birds7.08 Reptiles2.20

40. Research Questions 1.Is the calculated allometric exponent (b) correlated with features of the sample? 2.Calculate mean & confidence interval for published values? 3.Likelihood that b = 3/4 vs. 2/3? 4. Why are estimates often < 3/4?

41. Allometric Exponent b = 3/4 b = 2/3 Question 4: estimates often < 3/4?

42. Linear Regression Most published exponents based on linear regression Assumption: x variable is measured without error Measurement error in x may bias slope estimates

43. Measurement Error Limits measurement of true species mean mass Includes seasonal variation Systematic variation “Classic” measurement errors

44. Simulation: Motivation e.g. y = 2x true Slope = 2.0 Slope = 1.8

45. Simulation: Assumptions Assumed model Y i = m i 0.75 Add variation in measurement of mass Y i = (m i + X i ) b Simulate error in measurement X i = Km i Z Z ~ N(0,1) Y = met. Rate m = mass X = error term (can be positive or negative) b = exponent K = % measurement error Z = a random number

46. Circles: mean of 100 trials Triangles: estimated parametric confidence intervals Allometric Exponent

47. Question 4: Conclusions Biases slope estimates down Never biases slope estimates up Parsimonious explanation for discrepancy between observed and predicted allometric exponents for homeotherms.

48. Slope Estimates Revisited Other methods than least-squares can be used to fit slopes to regression data “Model II Regression” does not assume that error is only in the y variable Equivalent to fitting principal components

49. Ordinary Least-Squares Regression Most published exponents based on OLS Assumption: x variable is measured without error Fitted slope minimizes vertical residual deviations from line

50. Reduced Major Axis Regression Minimizes perpendicular distance of points to line Does not assume all error is contained in y variable “Splits the difference” between x and y errors

51. Reduced Major Axis Regression Slope of Major Axis Regression is always > slope of OLS Regressions Major Axis Regression slope = b / r 2 increasing b

52. Re-analysis of Data Adjusted slope for n = 5 mammal data sets

53. Conclusions Measured allometric exponents not correlated with features of sample Published exponents cluster tightly for homeotherms –values slightly lower than the predicted b = 3/4. Published exponents highly variable for poikilotherm studies

54. Conclusions Body mass measurement error always biases least-squares slope estimates downward Observed allometric exponents closer to 3/4 than 2/3

55. Acknowledgements Gordon Research Conference Committee Metabolic Basis of Ecology Bates College July 4-9, 2004