The natural selection of metabolism and mass selects lifeforms from viruses to multicellular animals

2016

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Inter-specific body mass allometries can evolve from the natural selection of mass, with ±1/4 and ±3/4 exponents following from the geometry of intra-specific interactions when density dependent foraging occurs in two spatial dimensions (2D, Witting 1995). The corresponding values for three dimensional interactions (3D) are ±1/6 and ±5/6.But the allometric exponents in mobile organisms are more diverse than the prediction. The exponent for mass specific metabolism tends to cluster around -1/4 and -1/6 in terrestrial and pelagic vertebrates, but it is strongly positive in prokaryotes with an apparent value around 5/6 ( De-Long et al. 2010). And a value around zero has been reported in protozoa, and on the macro evolutionary scale from prokaryotes over larger unicells to multicellular vertebrates (Makarieva et al. 2005(Makarieva et al. , 2008.I show that mass specific metabolism can be selected as the pace of the resource handling that generates net energy for self-replication and the selection of mass, and that this selection of metabolism and mass is sufficient to explain metabolic exponents that decline from 5/6 over zero to -1/6 in 3D, and from 3/4 over zero to -1/4 in 2D. The decline follows from a decline in the importance of mass specific metabolism for the selection of mass, and it suggests i) that the body mass variation in prokaryotes is selected from primary variation in mass specific metabolism, ii) that the variation in multicellular animals are selected from primary variation in the handling and/or densities of the underlying resources, iii) that protozoa are selected as an intermediate lifeform between prokaryotes and multicellular animals, and iv) that macro evolution proceeds along an upper bound on mass specific metabolism.

Section: Diverse Allometriesmentioning

confidence: 76%

“…defining the selection dependence of mass on energy given a log-linear relation. The specific mechanisms of this selection are not considered here; they are instead described by Witting (1997Witting ( , 2008Witting ( , 2016a. From eqn 23 we may conclude that natural selection creates an evolutionary function…”

Section: Metabolism Net Energy and Massmentioning

confidence: 99%

The natural selection of metabolism and mass selects allometric transitions from prokaryotes to mammals

2016

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“…6) given data from Bonner (1965), and 1.07 ± 0.06 across selected maximum masses (Supplementary Table S6) from Payne et al (2009). This apparent log-linear evolution on the largest macroevolutionary scale is supported by a body mass invariant mass-specific metabolism across major lifeforms from prokaryotes to mammals (Makarieva et al 2005(Makarieva et al , 2008Kiørboe and Hirst 2014;Witting 2017b). The latter invariance is observed not only across species distributions in general, but also for the maximum observed mass-specific metabolism across prokaryotes, unicellular eukaryotes, aquatic multicellular ectotherms, terrestrial ectotherms, and terrestrial endotherms (linear regressions on double logarithmic scale estimate slopes between −0.02 ± 0.01 and 0.01 ± 0.02, Supplementary Table S7).…”

Section: Evolution At Metabolic Limitmentioning

confidence: 78%

“…Yet, the steady state defines also an important intra-specific allometry that describes the correlation between reproduction and mass across the individuals in the evolving population. To obtain this allometry, insert eqn 25 into the selection gradient ∂r * i /∂ ln w i | wi=w = ψι * − 1 for the multicellular animal in Witting (2017b), and integrate over ln w i to find that the within population variation in the pR-product scale as…”

Section: Natural Selection Timementioning

confidence: 99%

The natural selection of metabolism explains curvature in fossil body mass evolution

2016

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The natural selection of metabolism and mass can explain inter-specific allometries from prokaryotes to mammals (Witting 2017a), with exponents that depend on the selected metabolism and the spatial dimensionality (2D/3D) of intra-specific behaviour. The predicted 2Dexponent for total metabolism increases from 3/4 to 7/4 when the fraction of the inter-specific body mass variation that follows from primary variation in metabolism increases from zero to one.A 7/4 exponent for mammals has not been reported from inter-specific comparisons, but I detect the full range of allometries for evolution in the fossil record. There are no fossil data for allometric correlations between metabolism and mass, but I estimate life history allometries from the allometry (ẇ ∝ wˆẇ) for the rate of evolution (ẇ = dw/dt) in mass (w) in physical time (t).Theŵ exponent describes the curvature of body mass evolution, with predicted values being: 3/2 (2D) for within niche evolution in small horses over 54 million years. 5/4 (2D) and 9/8 (3D) for across niche evolution of maximum mass in four mammalian clades. 3/4 (2D) for fast evolution in large horses, and maximum mass in trunked and terrestrial mammals. 1 for maximum mass across major lifeforms during 3.5 billion years of evolution along a metabolic bound.

“…The age structured population models are given in the appendix, and the contamination levels of the different groups are defined by Desforges et al (2018). to 4.0%, with the variation originating primarily from differences in the fecundity rate (Brault and Caswell 1993). This type of intra-population variation is expected in natural populations from the density dependent interactive competition between the individuals in the population (Witting 1997(Witting , 2017. It implies that the rate of increase in population is approaching the highest intra-population rates when there is minimum interference at low densities.…”

Section: Pcb Groupmentioning

confidence: 99%

Saving killer whale populations from a global collapse: rebuttal against Desforges et al. (2018)

2018

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In their Science paper, Desforges et al. (2018) address PCB pollution in killer whales, predicting a decline in calf survival and an associated collapse of killer whale populations worldwide. I refute the collapse, showing that it follows from a flawed model parametrisation. The result is not questioning contamination problems in killer whales, but only the bold prediction of global collapse.Desforges et al. (2018) divide killer whales into seven groups from the least PCB polluted in Arctic and Antarctic waters (group 1) to the most contaminated in industrialised regions (group 7), and use a predicted decline in calf survival to forecast killer whale populations over a 100 year period. Population dynamics over 100 years include regulations of the growth rate. Apart from density dependence, the regulation will also adjust the PCB level in individuals as the environmental PCB concentration changes over time.Being long-lived top-predators, killer whales have a delayed PCB accumulation relative to most animals. It is essential to address whether their current contamination reflects a persistent ecological exposure, or the accumulation of historical pollution in older animals, with subsequent transmission from mothers to calves. Equally important is an environmental model to predict future ecological exposures to PCB. These regulation issues, however, are not addressed by Desforges et al. (2018), who consider unregulated population growth with constant PCB contamination in the different exposure groups. This makes their study unable to predict realistic killer whale dynamics, but it does not imply that they are unable to estimate current impacts on killer whales worldwide.Their figure 2A illustrates this impact by a calf survival that declines with increased maternal PCB concentration. The response follows from a number of data points, yet these follow from a laboratory response in mink. The predicted population collapse is based on the assumption that we can straightforwardly convert a physiological response in a 0.5 kg terrestrial laboratory animal to natural populations of a free-living marine mammal that is about 10,000 times larger. While it is fair to assume that increased PCB levels will affect calf survival also in killer whales, Desforges et al. (2018) do not address the large degree of uncertainty that is associated with the conversion of a physiological response in mink to a population level response in killer whales.Assuming that the response in mink is applicable to killer whales, might we, as the authors, conclude that only killer whales in the less-contaminated waters of the Arctic and Antarctic today appear to be able to sustain growth. To predict the negative growth of 1