Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life

Abstract

The diversification of life involved enormous increases in size and complexity. The evolutionary transitions from prokaryotes to unicellular eukaryotes to metazoans were accompanied by major innovations in metabolic design. Here we show that the scalings of metabolic rate, population growth rate, and production efficiency with body size have changed across the evolutionary transitions. Metabolic rate scales with body mass superlinearly in prokaryotes, linearly in protists, and sublinearly in metazoans, so Kleiber's 3/4 power scaling law does not apply universally across organisms. The scaling of maximum population growth rate shifts from positive in prokaryotes to negative in protists and metazoans, and the efficiency of production declines across these groups. Major changes in metabolic processes during the early evolution of life overcame existing constraints, exploited new opportunities, and imposed new constraints.energetic constraints | production efficiency | r max | endosymbiosis | multicellularity T he 3.5 billion year history of life on earth was characterized by dramatic increases in the size, complexity, and diversity of living things. The first organisms were microbes with relatively simple body plans and metabolic networks. A few major transitions in form and function occurred during the subsequent evolution of life (1). The resulting diversity of contemporary organisms ranges from minute, relatively simple unicellular prokaryotes to giant, complex animals and plants containing multiple differentiated organelles, cells, tissues, and organs.Two of the largest transitions were from simple prokaryotic to complex eukaryotic cells, and from unicellular to multicellular eukaryotes. Each transition required the integration of multiple individual organisms into a new higher-level unit of organization and selection (1, 2). These transitions involved dramatic changes in structure and function, and several orders of magnitude increase in body size (3). As all organisms share a common set of molecules and biochemical reactions (4, 5), the increases in size and organizational complexity were accomplished by assembling these universal components in new ways (6). Major changes in genetic systems made these transitions possible (1, 2), and complementary changes in metabolic systems supplied the energy and materials to grow larger and support more complex morphologies and physiologies (7,8).Scaling relations offer powerful insights into the fundamental processes that constrain and regulate biological structure and function. Nearly all characteristics of organisms, from use of energy to the population growth it fuels, vary with body size. Most of the variation can be described by allometric equations or power functions of the following form:where Y is the trait of interest, Y 0 is a normalization constant, M is body mass, and α is the scaling exponent. There is a large and longstanding literature on these biological scaling relations in plants and animals but that are fewer focused on unicellular prokaryotes and prot...

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

“…Some of these are (1) metabolism as a proxy for the rate at which organisms assimilate, transform and expend energy (e.g., Brown, Gillooly, Allen, & Savage, 2004; Calder, 1984; Humphries & McCann, 2014); (2) a biological time‐scale as the inverse of mass‐specific metabolism (e.g., Brody, 1945; Pearl, 1928); (3) that advanced metabolism is dependent upon a cell where the molecules of the metabolic pathways can concentrate (e.g., Maynard Smith & Szathmáry, 1995; Miller & Orgel, 1974; Oparin, 1957); (4) that natural selection is driven by the biochemical energetics of self‐replication (e.g., Brown, Marquet, & Taper, 1993; Lotka, 1922; Odum & Pinkerton, 1955; Van Valen, 1976); (5) that it is constrained by physiological trade‐offs and constraints (e.g. Charlesworth, 1980; Roff, 1992; Stearns, 1992), including a metabolism that depends on mass in self‐replicators with almost no mass (DeLong et al., 2010); (6) that it proceeds toward attractors like continuously stable strategies (e.g., Eshel & Motro, 1981; Maynard Smith & Price, 1973; Taylor, 1989); (7) that it is dependent upon the feed‐back ecology of density dependence (e.g., Anderson, 1971; Heino, Metz, & Kaitala, 1998; Rankin, 2007), including the density dependence of interactive competition (e.g., Abrams & Matsuda, 1994; Witting, 1997) that makes arms race models (e.g., Dawkins & Krebs, 1979; Maynard Smith & Brown, 1986; Parker, 1979) realistic; (8) that the unit of selection is the interacting unit that makes replication differential (Hull, 1980); (9) that higher‐level selection trade‐off fitness at the lower level for increased fitness at the higher level (Buss, 1987; Michod, 1999); (10) that the resulting short‐term evolution is contingent upon the current state of biology and the available mutations; and (11) that long‐term evolution is more like a deterministic path (Witting, 1997, 2008) that is laid down by the selection attractors that unfold from the origin of replicating molecules, including allometric exponents that evolve by the ecological geometry of optimal density regulation (Witting, 1995, 2017). …”

“…Patterns of mass‐specific metabolism within and across major taxa from prokaryotes to mammals have been studied by DeLong et al. (2010), Kiørboe and Hirst (2014), Makarieva, Gorshkov, and Bai‐Lian (2005), and Makarieva et al. (2008), with the overall pattern (Figure 4) resembling the theoretical expectation of ${\hat{\mathrm{\beta }}}_{\mathrm{normal\beta }}=1/2d$ (Figure 3, right, c).…”

“…By correcting estimated exponents to their reduced major axis values to account for uncertain estimates of mass, DeLong et al. (2010) estimated empirical exponents in heterotrophic prokaryotes around 0.84 (0.96 ± 0.18 for active; 0.72 ± 0.07 for inactive) for mass‐specific metabolism and 0.73 for the rate of population growth. While these estimates may be preliminary, they agree with the predicted value of 0.83.…”

“…(2008), with RMA lines from DeLong et al. (2010) for prokaryotes and protozoa, and least‐squares lines for other taxa. Prokaryotes: $\stackrel{false^}{\mathrm{normal\beta }}=0.72$, n  = 123, for passive ($\stackrel{false^}{\mathrm{normal\beta }}=0.96$ for active); protozoa: $\stackrel{false^}{\mathrm{normal\beta }}=-0.03$, n  = 52; aquatic invertebrates: $\stackrel{false^}{\mathrm{normal\beta }}=-0.24$, n  = 808; insects & ectotherm vertebrates: $\stackrel{false^}{\mathrm{normal\beta }}=-0.27$, n  = 982; birds & mammals $\stackrel{false^}{\mathrm{normal\beta }}=-0.31$, n  = 948.…”

Energetic differences between bacterioplankton trophic groups and coral reef resistance

“…For microbes, individual metabolic rate scales super-linearly (as opposed to sub-linearly) with body mass [31]. The effects of temperature and trophic strategy on individual metabolic rate are accounted for by e 2E/kT [32,33], where E is the activation energy, k is Boltzmann's constant (8.62 Â 10 25 eV K 21 ) and T is the water temperature at the site at the time of collection (in Kelvin).…”