Biological scaling analyses are more than statistical line fitting

ABSTRACT The magnitude of many biological traits relates strongly and regularly to body size. Consequently, a major goal comparative biology is understand apply these ‘size-scaling’ relationships, traditionally quantified by using linear regression analyses based on log-transformed data. However, recently some investigators have questioned this traditional method, arguing that or non-linear untransformed arithmetic data may provide better statistical fits than log-linear analyses. Furthermore, they advocate the replacement method alternative specific methods case-by-case basis, simply best-fit criteria. Here, I argue use logarithms in scaling presents multiple valuable advantages, both conceptual. Most importantly, log-transformation allows biologically meaningful, properly scaled (scale-independent) comparisons organisms different size, whereas non-scaled (scale-dependent) do not. Additionally, log-based can readily reveal theoretically relevant discontinuities scale invariance during developmental evolutionary increases size are not shown In way, advances our understanding conceptually, just statistically. hope my Commentary helps students, non-specialists other interested readers general benefits size-scaling analyses, stimulates advocates show how improve