Changing lanes: can we reconcile the ways we measure reproduction so we can make meaningful comparisons across animal species?

Reproduction is perhaps the only truly unambiguous measure of fitness and yet we measure it in different ways. Biologists working on birds tend to measure clutch size as number of eggs per clutch, while mammal biologists focus on litter size measured in mass. These differences only become obvious when researchers want to move out of their accustomed lanes and ask broader questions applicable to a wide range of animal species. One unequivocal measure of reproduction is reproductive mass per year but how often do researchers measure this?

Reproductive mass per year combines the number of offspring per reproductive bout with the mass of the offspring and, importantly, the number of reproductive bouts per year. We know that some species can have a few large offspring and only reproduce once per year whilst other species can produce many small offspring numerous times per year. So which species puts in the most resources to reproduction? Only by combining measures of offspring mass over time can we really compare reproductive effort across species.

How often are all three components of reproductive mass per year – number of offspring per reproductive bout, offspring mass, and the number of reproductive bouts per year – provided for animals?

PhD student, Sam Ginther, is interested in the energetic costs of reproduction and wondered how feasible it would be to collate reproduction data for a wide range of species. Could he translate the existing data into a consistent and biologically relevant measure of reproductive mass per year?

Sam and supervisors Dustin Marshall, Craig White and Hayley Cameron created a ‘systematic map’ of reproductive trait data that exist in online databases. They used this unbiased approach to collate and describe:

  1. how common is the measure reproductive mass per year in databases, and
  2. how well did more ambiguous reproductive measures (i.e., fecundity per bout, fecundity per year, and reproductive mass per bout) represent a truly comparable measure of reproductive effort – reproductive mass per year.

So, can we use other measures as proxies for reproductive mass per year? While most reproductive measures are poor predictors of reproductive effort, reproductive mass per bout is the exception.

Reproductive databases are amazing resources and represent centuries of work in the field of reproductive biology. However, to unlock their full potential Sam and his colleagues feel that the best way forward is to encourage researchers to measure reproduction in a way that allows us to reconstruct reproductive mass per year; that is, tie reproductive measures to temporal- and volumetric-dimensions. But where this is unrealistic in terms of time and effort then measuring reproductive mass per bout is the next best thing.

This research is published in the journal Global Ecology and Biogeography.

Avoiding growing pains in reproductive trait databases: the curse of dimensionality

Authors: Samuel C Ginther, Hayley Cameron, Craig R White, and Dustin J Marshall

Published in: Global Ecology and Biogeography


Aim: Reproductive output features prominently in many trait databases, but the metrics describing it vary and are often untethered to temporal and volumetric dimensions (e.g., fecundity per bout). The use of such ambiguous reproductive measures to make broad-scale comparisons across taxonomic groups will be meaningful only if they show a 1:1 relationship with a reproductive measure that explicitly includes both a volumetric and a temporal component (i.e., reproductive mass per year). We sought to map the prevalence of ambiguous and explicit reproductive measures across taxa and to explore their relationships with one another to determine the cross-compatibility and utility of reproductive metrics in trait databases.

Location: Global.

Time period: 1990–2021.

Major taxa studied: We searched for reproductive measures across all Metazoa and identified 19,785 vertebrate species (Chordata), and 440 invertebrate species (Arthropoda, Cnidaria or Mollusca).

Methods: We included 37 databases, from which we summarized the commonality of reproductive metrics across taxonomic groups. We also quantified scaling relationships between ambiguous reproductive traits (fecundity per bout, fecundity per year and reproductive mass per bout) and an explicit measure (reproductive mass per year) to assess their cross-compatibility.

Results: Most species were missing at least one temporal or volumetric dimension of reproductive output, such that reproductive mass per year could be reconstructed for only 4,786 vertebrate species. Ambiguous reproductive measures were poor predictors of reproductive mass per year; in no instance did these measures scale at 1:1.

Main conclusions: Ambiguous measures systematically misestimate reproductive mass per year. Until more data are collected, we suggest that researchers should use the clade-specific scaling relationships provided here to convert ambiguous reproductive measures to reproductive mass per year.

Ginther SC, Cameron H, White CR, Marshall DJ (2022) Avoiding growing pains in reproductive trait databases: the curse of dimensionality. Global Ecology and Biogeography PDF DOI

Carry-over effects and fitness trade-offs in marine life histories: The costs of complexity for adaptation

Authors Dustin J Marshall and Tim Connallon

Published in Evolutionary Applications


Most marine organisms have complex life histories, where the individual stages of a life cycle are often morphologically and ecologically distinct. Nevertheless, life-history stages share a single genome and are linked phenotypically (by “carry-over effects”). These commonalities across the life history couple the evolutionary dynamics of different stages and provide an arena for evolutionary constraints. The degree to which genetic and phenotypic links among stages hamper adaptation in any one stage remains unclear and yet adaptation is essential if marine organisms will adapt to future climates.

Here, we use an extension of Fisher’s geometric model to explore how both carry-over effects and genetic links among life-history stages affect the emergence of pleiotropic trade-offs between fitness components of different stages. We subsequently explore the evolutionary trajectories of adaptation of each stage to its optimum using a simple model of stage-specific viability selection with nonoverlapping generations.

We show that fitness trade-offs between stages are likely to be common and that such trade-offs naturally emerge through either divergent selection or mutation. We also find that evolutionary conflicts among stages should escalate during adaptation, but carry-over effects can ameliorate this conflict.

Carry-over effects also tip the evolutionary balance in favor of better survival in earlier life-history stages at the expense of poorer survival in later stages. This effect arises in our discrete-generation framework and is, therefore, unrelated to age-related declines in the efficacy of selection that arise in models with overlapping generations.

Our results imply a vast scope for conflicting selection between life-history stages, with pervasive evolutionary constraints emerging from initially modest selection differences between stages. Organisms with complex life histories should also be more constrained in their capacity to adapt to global change than those with simple life histories.

Marshall DJ, Connallon T (2022) Carry‐over effects and fitness trade‐offs in marine life histories: The costs of complexity for adaptation. Evolutionary Applications PDF DOI

The Conversation: Why are bigger animals more energy-efficient? A new answer to a centuries-old biological puzzle

By Craig White and Dustin Marshall

This article is republished from The Conversation under a Creative Commons licence.

If you think about “unravelling the mysteries of the universe”, you probably think of physics: astronomers peering through telescopes at distant galaxies, or experimenters smashing particles to smithereens at the Large Hadron Collider.

When biologists try to unravel deep mysteries of life, we too tend to reach for physics. But our new research, published in Science, shows physics may not always have the answers to questions of biology.

For centuries scientists have asked why, kilo for kilo, large animals burn less energy and require less food than small ones. Why does a tiny shrew need to consume as much as three times its body weight in food each day, while an enormous baleen whale can get by on a daily diet of just 5-30% of its body weight in krill?

While previous efforts to explain this relationship have relied on physics and geometry, we believe the real answer is evolutionary. This relationship is what maximises an animal’s ability to produce offspring.

How much do physical constraints shape life?

The earliest explanation for the disproportionate relationship between metabolism and size was proposed nearly 200 years ago.

In 1837, French scientists Pierre Sarrus and Jean-François Rameaux argued energy metabolism should scale with surface area, rather than body mass or volume. This is because metabolism produces heat, and the amount of heat an animal can dissipate depends on its surface area.

In the 185 years since Sarrus and Rameaux’s presentation, numerous alternative explanations for the observed scaling of metabolism have been proposed.

Arguably the most famous of these was published by US researchers Geoff West, Jim Brown and Brian Enquist in 1997. They proposed a model describing the physical transport of essential materials through networks of branching tubes, like the circulatory system.

They argued their model offers “a theoretical, mechanistic basis for understanding the central role of body size in all aspects of biology”.

These two models are philosophically similar. Like numerous other approaches put forward over the past century, they try to explain biological patterns by invoking physical and geometric constraints.

Evolution finds a way

Living organisms cannot defy the laws of physics. Yet evolution has proven to be remarkably good at finding ways to overcome physical and geometric constraints.

In our new research, we decided to see what happened to the relationship between metabolic rate and size if we ignored physical and geometric constraints like these.

So we developed a mathematical model of how animals use energy over their lifetimes. In our model, animals devote energy to growth early in their lives and then in adulthood devote increasing amounts of energy to reproduction.

Animals allocate more energy to reproduction after they reach maturity.

We used the model to determine what characteristics of animals result in the greatest amount of reproduction over their lifetimes – after all, from an evolutionary point of view reproduction is the main game.

We found that the animals that are predicted to be most successful at reproducing are those that exhibit precisely the kind of disproportionate scaling of metabolism with size that we see in real life!

This finding suggests disproportionate metabolic scaling is not an inevitable consequence of physical or geometric constraints. Instead, natural selection produces this scaling because it is advantageous for lifetime reproduction.

The unexplored wilderness

In the famous words of Russian-American evolutionary biologist Theodosius Dobzhansky, “nothing makes sense in biology except in the light of evolution”.

Our finding that disproportionate scaling of metabolism can arise even without physical constraints suggests we have been looking in the wrong place for explanations.

Physical constraints may be the principal drivers of biological patterns less often than has been thought. The possibilities available to evolution are broader than we appreciate.

Why have we historically been so willing to invoke physical constraints to explain biology? Perhaps because we are more comfortable in the safe refuge of seemingly universal physical explanations than in the relatively unexplored biological wilderness of evolutionary explanations.
The Conversation

Metabolic scaling is the product of life-history optimization

Authors: Craig R White, Lesley A Alton, Candice L Bywater, Emily J Lombardi and Dustin J Marshall

Published in: Science


Organisms use energy to grow and reproduce, so the processes of energy metabolism and biological production should be tightly bound. On the basis of this tenet, we developed and tested a new theory that predicts the relationships among three fundamental aspects of life: metabolic rate, growth, and reproduction.

We show that the optimization of these processes yields the observed allometries of metazoan life, particularly metabolic scaling. We conclude that metabolism, growth, and reproduction are inextricably linked; that together they determine fitness; and, in contrast to longstanding dogma, that no single component drives another.

Our model predicts that anthropogenic change will cause animals to evolve decreased scaling exponents of metabolism, increased growth rates, and reduced lifetime reproductive outputs, with worrying consequences for the replenishment of future populations.

White CR, Alton LA, Bywater CL, Lombardi EJ, Marshall DJ (2022) Metabolic scaling is the product of life-history optimization. Science DOI

When is a change for the better?

Metamorphosis — the process by which animals undergo substantial changes to become an adult — can involve a complete redesign of the body plan. While not all transitions are as dramatic as the metamorphosis from larvae to adult, animals that undergo transitions as they move toward the adult stage are described as having complex life cycles. But why did animals evolve such complex life cycles?

In 1986 a scientist named Earl Werner proposed an explanation that has been widely cited since. Werner said that as an individual grows, energy demands increase faster than energy uptake. But, switching body plans (which usually means better access to food sources) enables individuals to continue growing and to reach the reproductive stage more efficiently and in better health. Werner’s model predicts the size at which an individual will transition as the size that minimises the chance of dying compared to its growth rate. Werner assumed the mass of the individual won’t change as a result of transition to the new phase of the life cycle.

Emily Richardson and her supervisors Dustin Marshall and Craig White wondered if this was true. If not, and there was a cost/benefit of transition, then the optimal size to change body plan might differ from Werner’s predictions.

Emily set about reviewing the literature to find out if there were any changes in mass that related to transitioning from one life stage to the next. Emily found data for 100 species and 343 life stages where she was able to record changes in growth rate and mass.

It turned out that across all taxa, as Werner predicted, growth rates were maintained or increased after switching to a new phase. But Werner hadn’t accounted for the change in mass that Emily and her supervisors observed in most taxa. On average amphibians lost 28%, insects 32% and crustaceans 8% of their mass during metamorphosis. During changes from one larval stage to another, fish and crustaceans actually gained mass and fish gained even more mass during metamorphosis. These increases in mass are likely to reflect more subtle life-history transitions where feeding is possible and transitioning is not as energetically costly.

These plots show Werner’s hypothetical predictions for the mass at which an individual should switch body plans when (A) growth rate is included and (B) when mortality is also included – note the optimal size at switching is larger when mortality is included. The dashed green curve in (C) represents one possible outcome if Werner’s model incorporates change in mass during transition to a new phase—in this example, the new curve shifts to the right and size at switching predicted by the new model is larger than predicted by Werner’s original model.

Either way, the team found that when changes in mass during transition are accounted for, the optimal size for transition will deviate from Werner’s predictions. For species that gain mass during a transition to a new phase, individuals should switch at a smaller size, while for species that lose mass, Emily and her supervisors predict they will transition at a larger size compared to Werner’s current theory.

To better understand the optimal size where transition will maximise fitness, we need to incorporate the change in mass that happens during this transition. And to test Werner’s theory further, the team highlight the need to estimate mortality rates in the field, including the risk of dying when transitioning to a new stage.

This research is published in the journal Functional Ecology.

Travelling in time: an experimental evolution experiment challenges what we thought we knew about size and the cost of production

Time travel has been made possible by a long-term evolution experiment with the bacteria Escherichia coli. In 1988 a biologist at Michigan State University, Richard Lenski, set up 12 flasks of E. coli and his group has maintained and followed their evolution ever since. Periodically, subsamples are frozen enabling scientists to compare the bacteria at different points in time by bringing them back to life.

Over time, the evolving E. coli have grown bigger; after 60,000 generations, cells are roughly twice the size of their ancestors. But has this increase in size been accompanied by changes we expect to see in metabolism and population size and growth rates? Researchers at the Centre for Geometric Biology have collaborated with Richard Lenski to find out.

Metabolism dictates the rate at which organisms transform energy into maintenance and production. While larger species have higher metabolic rates, they are actually more efficient and so have lower metabolic rates relative to their size. So, while smaller species have higher population densities and can reach those densities faster, total population mass is greater in larger species (think mice and elephants).

But does the above hold true within a species? Often the size range within a species isn’t particularly large, making inferences about size difficult to test. The aptly named ‘Lenski Lines’ circumvent this problem. Richard’s lab sent frozen samples of the original E. coli – the ancestors, plus samples from 10,000 and 60,000 generations of evolution. Project leads at Monash University, Dustin Marshall and Mike McDonald, set about reviving the cells and measuring cell size, metabolism, population size and population growth.

The team found that as the cells grew bigger through evolutionary time, metabolic rates increased but were lower relative to their size, as predicted by theory. Also anticipated by theory, populations of larger cells had lower population densities but higher biomass’ than their smaller ancestors. The big surprise and in stark contrast to theory, was that populations of larger cells, despite their relatively lower metabolism grew faster than smaller cells.

The research team found that, as expected, larger cells had lower population densities (b) but greater biomass (c and d) but to their surprise larger cells also had a faster rate of population growth than the smaller cells (a).

We often assume that the energy required to produce a new individual is directly proportional to its mass but as this experiment has shown it is not necessarily the case. Why then, would a larger cell be cheaper to build and maintain

E. coli cells use up a lot of energy maintaining ion gradients across cell membranes. As larger cells have smaller surface areas relative to mass they should also have lower maintenance costs than smaller cells. The evolved cells also have slightly smaller genomes than the smaller ancestral cells so the costs of genome replication are lower for larger cells. What is more, the evolved cells have fine-tuned their genetic components in this highly predictable environment, reducing the costly expression of unneeded transcripts and proteins.

Remarkably, it seems evolution can decouple the costs of production from size; there is no downside to increasing growth rates for the larger evolved cells in terms of yield.

This research is published in Proceedings of the National Academy of Sciences of the United States of America.

Long-term experimental evolution decouples size and production costs in Escherichia coli

Authors: Dustin J Marshall, Martino Malerba, Thomas Lines, Aysha L Sezmis, Chowdhury M Hasan, Richard E Lenski, and Michael J McDonald

Published in: Proceedings of the National Academy of Sciences of the United States of America (PNAS)


Populations of larger organisms should be more efficient in their resource use, but grow more slowly, than populations of smaller organisms.

The relations between size, metabolism, and demography form the bedrock of metabolic theory, but most empirical tests have been correlative and indirect.

Experimental lineages of Escherichia coli that evolved to make larger cells provide a unique opportunity to test how size, metabolism, and demography covary. Despite the larger cells having a relatively slower metabolism, they grow faster than smaller cells. They achieve this growth rate advantage by reducing the relative costs of producing their larger cells.

That evolution can decouple the costs of production from size challenges a fundamental assumption about the connections between physiology and ecology.


Body size covaries with population dynamics across life’s domains. Metabolism may impose fundamental constraints on the coevolution of size and demography, but experimental tests of the causal links remain elusive.

We leverage a 60,000-generation experiment in which Escherichia coli populations evolved larger cells to examine intraspecific metabolic scaling and correlations with demographic parameters.

Over the course of their evolution, the cells have roughly doubled in size relative to their ancestors. These larger cells have metabolic rates that are absolutely higher, but relative to their size, they are lower.

Metabolic theory successfully predicted the relations between size, metabolism, and maximum population density, including support for Damuth’s law of energy equivalence, such that populations of larger cells achieved lower maximum densities but higher maximum biomasses than populations of smaller cells. The scaling of metabolism with cell size thus predicted the scaling of size with maximum population density. In stark contrast to standard theory, however, populations of larger cells grew faster than those of smaller cells, contradicting the fundamental and intuitive assumption that the costs of building new individuals should scale directly with their size.

The finding that the costs of production can be decoupled from size necessitates a reevaluation of the evolutionary drivers and ecological consequences of biological size more generally.

Marshall DJ, Malerba M, Lines T, Sezmis AL, Hasan CM, Lenski RE, McDonald MJ (2022) Long-term experimental evolution decouples size and production costs in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America PDF DOI

A comparative analysis testing Werner’s theory of complex life cycles

Authors: Emily L Richardson, Craig R White, and Dustin J Marshall

Published in: Functional Ecology


A popular theoretical model for explaining the evolution of complex life cycles (CLCs) was provided by Earl Werner. The theory predicts the size at which an individual should switch stages to maximise growth rate relative to mortality rate across the life history.

Werner’s theory assumes that body size does not change during the transition from one phase to another (e.g. from larva to adult) — a key assumption that has not been tested systematically but could alter the predictions of the model.

We quantified how growth rate and mass change across larval stages and metamorphosis for 105 species of fish, amphibians, insects, crustaceans and molluscs. Across all taxonomic groups, we found support for Werner’s assumption that growth rates are maintained or increase around transitions. We found that changes in growth and mass were greatest during metamorphosis, and change in growth correlated with development time. Importantly, most species either gained or lost mass when switching to a new stage — a direct contradiction of Werner’s assumption. When we explored the consequences of energy loss and gain in a numerical model, we found that individuals should switch stages at a larger and smaller size, respectively, relative to what Werner’s standard theory predicts.

Our results suggest that while there is support for Werner’s assumption regarding growth rates, mass changes profoundly alter the timing of transitions that are predicted to maximise fitness, and therefore the original model omits an important component that may contribute to the evolution of CLCs. Future studies should test for conditions that alter the costs of transitions, so that we can have a better understanding of how mass loss or gain affects fitness.

Richardson EL, White CR, Marshall DJ (2022) A comparative analysis testing Werner’s theory of complex life cycles. Functional Ecology PDF DOI

Winners and losers: how does metabolic rate affect the outcomes of competition?

An individual’s success in competitive environments is often closely aligned with its metabolic rate. When resources are scarce, individuals with lower metabolic rates are expected to grow larger and dominate while individuals with higher metabolic rates will struggle if their energy demands cannot be met. Increasing population density can increase competition for a finite pool of resources and so lower metabolic rate individuals may do better in more competitive environments.

Lukas Schuster and supervisors Craig White and Dustin Marshall noted that investigations into the relationships between metabolic rate and competitive interactions have mainly taken place in the laboratory. They wanted to know how metabolic rate affected competitive interactions in a more realistic field situation. So, they designed an experiment using the model species Bugula neritina, a colonial marine animal that, crucially, does not move allowing survival, growth, and reproductive output to be easily measured in the field.

Lukas settled Bugula larvae on to acetate sheets and after two weeks of growth in the field he brought them back to the lab to measure metabolic rates. Each colony with a known metabolic rate was then assigned to become either a ‘focal’ colony or a ‘neighbour’ colony. Colonies were glued on to small plates and focal colonies either had a neighbour colony placed 1 cm away, or were left alone on the plate to determine the baseline relationship between metabolic rate and performance. Plates were distributed across 5 panels and returned to the field site and Lukas monitored focal individuals bi-weekly for survival, growth and reproductive output.

To the teams surprise they found a range of responses on the different panels despite their relative proximity. They concluded that each panel experienced a different microenvironment that, in turn, influenced the effects of metabolic rates on competitive outcomes. While the presence of a neighbour did reduce performance of focal colonies on most panels, the effects of metabolic rates of both focal colonies and neighbours were much more complex.

Bugula neritina individuals were attached to small plates either alone or with a neighbour and these plates were then attached to larger panels and hung in the marina. Despite their proximity, the team suspect differences in microclimate around each panel contributed to the variable outcomes of metabolic rate on competitive interactions.

Low metabolic rate colonies were larger overall, presumably because of their lower maintenance costs but, in general, the metabolic rate of the neighbour seemed more important to performance of the focal colony than its own metabolic rate. Lukas and his supervisors speculate that focal colonies benefited from being adjacent to fast-growing, low metabolic rate neighbours on panels where flow was higher because the larger neighbours slowed down currents allowing greater access to resources for the focal individual. In low flow environments the opposite may be true; resources are not replaced quickly enough and so low metabolic rate larger neighbours reduce resource access.

Lukas, Craig and Dustin recommend that future studies on the ecological effects of metabolism look at competition both within and among species and are field experiments wherever possible.

While they can’t say for sure, they suspect the variable outcomes of metabolic rate on competition relate to differences in current regimes and the delivery of resources.  Future studies manipulating food availability along with metabolic rates will help address this possibility.

This research is published in the journal Ecology and Evolution.