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.

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.

Biodiversity increases energy and biomass production but only in younger communities

Preserving biodiversity is important because species diversity affects the productivity of biological communities. Diverse communities can better use available resources and, thus, produce more biomass than species-poor communities. When diversity is high, communities are also more likely to contain very productive species which further increase biomass production.

While these positive biodiversity effects are seen across diverse ecosystems, from tropical forests to agricultural fields, the general mechanism through which biodiversity increases biomass production remains unclear. Energy is what fuels biological production but very few studies have directly measured energy fluxes and even fewer the effects of biodiversity on energy production. Furthermore, biodiversity effects are not fixed but change as communities grow older. So how does diversity affect the relationship between energy and biomass production over time?

We answer this question using marine phytoplankton in a laboratory study. Phytoplankton are an extremely diverse group of unicellular algae of great ecological importance because they sustain 50% of global oxygen production and carbon uptake. Using five phytoplankton species with different characteristics, we set up a total of 50 cultures across three levels of biodiversity (species alone, in pairs and in communities with all five species) and compare their energy and biomass production for ten days. Since phytoplankton reproduce daily, our experiment covers roughly ten generations.

Diversity initially boosts both energy and biomass production, so that five-species communities produce and accumulate more biomass than species alone or in pairs. But as biomass grows, energy production is limited by competition. This limitation occurs in all cultures but is stronger in diverse communities. Therefore, the positive effects of biodiversity decline over time as communities grow older, see below.

Diverse communities (solid lines) produce energy (magenta) and biomass (green) faster than low-diversity communities (dashed lines), thus accumulating more total biomass. But as biomass accumulates, species compete more intensely limiting energy and thus biomass production. These effects are stronger in more diverse communities so that the positive effects of biodiversity progressively reduce as communities grow older. (Image credit: Giulia Ghedini)

In nature, the positive effects of biodiversity might be sustained over much longer periods of time than what we observe because ecosystems are continuously disturbed by storms, arrival of new species or changes in nutrient availability. Since disturbances are so widespread our results help to compare the functioning of ecosystems of different age and with different levels of diversity.

This research was published in the journal Functional Ecology.

Challenging assumptions: how well do we understand how climate change will affect vector-borne diseases?

Diseases such as malaria and dengue fever are spread by intermediaries, in this case, mosquitoes. The health and economic burdens of such mosquito-borne diseases are enormous. We know that mosquitoes are expanding their ranges and invading new habitats in response to warmer temperatures. Accurately predicting changes in both the size and spread of mosquito populations is essential for anticipating changes in disease dynamics.

To model how changing environments will affect mosquito populations, we need to know how quickly a population can grow under different scenarios. To estimate changes in population growth rate scientists input measures of development time, survival, body size and reproductive output into their models. Body size and reproductive output are particularly difficult to measure directly in mosquito populations so researchers traditionally rely on the relationship between wing length, which is easier to measure, body size and reproductive output.

These are the relationships that the Centre for Geometric Biology are challenging. Underlying most models of mosquito distributions is the assumption that there is a directly proportional relationship between wing length, body size and reproductive output, or in other words, wing length and reproductive output increase at the same rate.

Scientists from the Centre analysed a range of existing data and found that this wasn’t true for most mosquito species.

In fact, explains Dr Louise Nørgaard, lead author on the study, larger females contribute disproportionately more to the replenishment of the population so it is not a straight-line relationship. And surprisingly, when we factor in this non-linear relationship smaller females are also contributing more to population replenishment than is assumed in current models.

This is important because increasing temperatures result in smaller females. So, temperatures where populations have been considered unviable, will, in fact, persist.

There is an additional complication when dealing with underlying assumptions of linearity; Jensen’s Inequality. This relates to a counter-intuitive mathematical rule that in non-linear relationships, such as this one, you can’t predict the mean reproductive output from the mean wing length in the same way you can for linear relationships. In fact, reproductive output in warmer climates will be even greater than predicted without accounting for Jensen’s Inequality.

This figure shows how reproductive output changes when the relationship between wing length and reproductive output is modelled as a isometric / linear relationship (blue) or hyperallometric / non-linear relationship (orange) (graph A). In this scenario a 15% reduction in wing length result in a 40% reduction in reproductive output when you consider both the shape of the relationship and Jensen’s Inequality (graph B). This contrasts to the 90% reduction in reproductive output that is predicted from an isometric / linear relationship and the 70% reduction in reproductive output if you don’t also account for Jensen’s Inequality (graph C).

There is another application of population models that will also be affected by these underlying assumptions. In the fight against Dengue fever, mosquitos that carry a bacteria called Wolbachia are bred in the lab and released into the wild to reduce the transmission of the dengue virus. Females released from the lab are bigger than their wild counterparts and so will contribute disproportionately more to the population when they breed. We are likely underestimating the impact of releasing Wolbachia-infected mosquitos in tackling this disease.

The authors conclude that to predict the response of disease vectors like mosquitos to global change we need to better represent the relationship between size and reproductive output.

This research was published in the journal Global Change Biology.

Does metabolic rate drive population size?

All organisms must eat to sustain themselves, but some more so than others. Metabolic rates should determine how much food an organism needs and how quickly it can convert that food into growth. We have long suspected the reason mice populations grow faster than elephant populations has something to do with their different metabolisms – for their size, mice have much higher metabolisms. While higher metabolisms might mean faster population growth, there is a supposed downside – populations with relatively higher metabolisms should exhaust resources at a much lower population biomass due to their higher resource demands. Thus, mice populations can sustain more individuals (have larger population sizes) than elephants, but on a per gram basis, mice have far lower population biomasses. That’s the theory at least – in reality no-one really knows.

Remarkably, despite years of interest in this topic, there have been no experimental tests of how metabolism affects populations – instead we relied on mouse-and-elephant-type comparisons – looking for patterns across organisms of very different sizes. While these comparisons are useful, obviously, mice and elephants differ in far more than their metabolisms alone, and these other factors could easily be driving any differences we observe. What’s needed is an experiment that manipulates the metabolism of whole populations, without changing anything else – a difficult task.

Lukas Schuster and Hayley Cameron along with Craig White and Dustin Marshall, set out to do just that. Using more than 1000 individuals of a common fouling marine creature – which feeds by filtering food particles from the water column – the team created 172 experimental populations that differed in their metabolic rates and population densities. These populations were then hung from plastic panels at a local marina where they were left to grow. The team then followed these populations for their entire lifetimes; measuring survival, growth and reproduction.

As anticipated, populations with higher metabolisms grew more rapidly – but what was unexpected was that populations with moderately high metabolic rates actually supported more individuals than those with lower metabolic rates. The reasons are unclear, but what seems to be happening is that higher metabolisms result in more feeding activity, which allows these populations to access relatively more resources, sustaining a larger number of individuals than expected. Metabolic rates can get too high, however, such that populations with the highest metabolic rates showed the expected decline in population size – probably because they exhausted local resources at a greater rate than these resources could be replenished, such that further increases in metabolism had no effect on resource intake.

The results have some surprisingly far-reaching implications. Many of the assumptions about how climate change will affect the resource consumption of future populations are based on classic, but untested theory. Likewise, fisheries are sometimes managed based on expectations about metabolic rate and resource demands. This research shows that the fundamental theory on how metabolism affects population demography needs revision. Higher metabolisms don’t invariably lead to lower population densities and a key rule of life seems to partially broken – higher metabolism populations can have their cake (grow fast) and eat it too (achieve high densities) – up to point at least.

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

In biology we call the density at which a population stops growing the ‘carrying capacity’. Theoretical models predict that there will be a steady decline in carrying capacity as metabolic rates increases (blue line). But the research team found a more complicated relationship (black line). Initially carrying capacity increased with increasing metabolic rate before starting to decline. At higher metabolic rates the rate of decline in carrying capacity was the same for the team’s experimental data as for theoretical predictions.

Fishing better to fish more

We have benchmarks for how to manage fisheries sustainably but what if the assumptions that go into setting those benchmarks are wrong?

In a previous study led by Diego Barneche and published in 2018, a research team looked at reproduction in more than 300 fish species. They found that contrary to a general perception, reproductive output did not increase in proportion with the size of the fish. Instead, the bigger the fish the disproportionately more eggs it produced. The mathematical term for this is hyperallometry.

This has clear implications for fisheries management and led Dustin and colleagues from Australia, USA and Germany to investigate assumptions around reproduction in fisheries management models.

Thirty-two of the world’s largest fisheries use management models that assume a proportional increase in reproductive output and fish size. These models estimate reproductive output from the total biomass of fish mature enough to spawn. This is because they are assuming that two 1 kg fish have the same reproductive output as one 2 kg fish. But the study led by Diego found that this was unlikely — 95% of the fish species they looked at had a hyperallometric relationship between size and reproduction — meaning the one big fish will produce many more eggs than the two smaller fish put together.

Does it matter? Well, yes. Dustin and his colleagues calculated that, for these fisheries, catches are set too high — in most cases, catches are twice what they should be in order to achieve the desired level of replenishment.

So, what happens when we get the biology right? Importantly, challenging assumptions about reproduction not only has ramifications for setting catch limits but has the potential to reset the way we manage fisheries. The team produced models that looked at retaining the largest and most reproductive members of the stock; through a temporary closure or protected area, or by setting size limits that focused on mid-size fish — leaving juveniles and the largest individuals unharvested.

The benefits of these different approaches vary depending on the species. Atlantic cod, for example, would benefit by switching to a fishery closure (either in space or time) with a predicted increase in long-term catch of ~25%

Dustin and his team acknowledge there are difficulties (both practical and social) with these approaches but also emphasise that any strategy that protects and retains larger fish in a population should provide a pay-off. Their models had some general assumptions of their own, however. When modelling fisheries that included protected areas where no fishing was allowed, they assumed that adult fish moved very little while larvae had the potential to replenish areas a long way away.

To check whether their results were too optimistic they did a more detailed study of the coral trout fishery in the Great Barrier Reef where they input data on larval movement. This reduced the catch increase from ~32% to ~16% suggesting that the more generic approach may overestimate benefits of fishery closures by ~12%.

A benefit of 16% is still non-trivial. Overall the results suggest that including hyperallometric reproduction in fisheries management models allows underused management tools such as marine protected areas to outperform traditional tools. This study highlights the role of reservoirs of large, highly reproductive individuals within a fishery.

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

Mind the gap: a systematic map of light variation in algal aquaculture allows us to identify research gaps

Algae need light to grow but how much light and how it should be delivered are important questions in aquaculture. Even in nature, light does not fall evenly; cloud cover, shading, water movement, water depth can all affect the amount of light algae experience and the time frame it is experienced over. So, what is the best light delivery regime to maximise yield in algal aquaculture? And, importantly, do we have the information to answer this question?

PhD student Belinda Comerford, under the supervision of Nick Paul and Dustin Marshall, has assessed what studies exist that look at variation in light and, crucially, where the gaps in research are. She used a systematic mapping technique where she followed a strict protocol with clearly articulated methods so other researchers will be able to add to the ‘map’ as the field develops.

Once she had settled on her search terms she entered them into the Web of Science database and assessed all the returned studies against the inclusion criteria. From the 10,000 studies returned from initial searches, she retained 212 for further analysis.

Belinda wanted to know what scale light manipulations happened (was it seconds or weeks?) and how long experiments lasted for. What pattern did researchers use when they manipulated light; was it square (light on or off), sinusoidal (gradual increase and decrease in light intensities) or sawtooth (jagged)? How big were the culture vessels used in the experiments? How many generations of algae were subject to the light manipulations?

Once she had coded all the studies that had met the inclusion criteria for her questions of interest, Belinda was able to determine areas where a rich knowledge-base ripe for further synthesis exists and, conversely, where we don’t have enough evidence to reliably assess impacts of variable light regimes on algal yield.

Belinda found that we have a good understanding of light variation on the immediate growth dynamics of microalgae over short time scales – milliseconds to day/night cycles.  But we don’t know the long-term implications of this light variation on algal cultures.

After completing the systematic map, Belinda can confidently point to where research is best directed to inform aquaculture. She recommends future studies focus on larger scale culture vessels and light variation regimes (sinusoidal and sawtooth) that better mimic production settings. Experiments that vary light on the scale of seconds, minutes and hours and that last for multiple months using cultures where biomass is kept relatively constant, through harvesting, will be particularly relevant for industry.

While Belinda recognises that some of these recommendations are demanding, she hopes by identifying the knowledge gaps, it will encourage researchers to tackle the formidable challenge of working at larger scales.

This research is published in the Journal of Applied Phycology.

Belinda recommends that future studies focus on larger culture vessels and light variation regimes that better mimic production settings.

Challenging the karyoplasmic ratio: bigger cells, smaller nuclei

A new publication finally puts paid to a long-held belief that the ratio between nucleus size and cell size (NS:CS) is approximately constant. This is called the karyoplasmic ratio and, while recently it has been recognised that nucleus size and cell size are not inexorably bound, the idea of a constant NS:CS ratio remains pervasive in biology. Not least in cancer biology where the karyoplasmic ratio is used in both diagnosis and prognosis for certain tumour types.

But, Dr Martino Malerba and Prof Dustin Marshall found that bigger cells have relatively smaller nuclei; as cells get bigger the karyoplasmic ratio actually gets smaller.

It all started when Martino and Dustin noticed that their evolved lines of different size algae didn’t show a constant karyoplasmic ratio. This piqued their curiosity; were these cells unusual or had this been observed in other cells? They started compiling data on cell size and nucleus size in a range of species and started reviewing publications for statements about the karyoplasmic ratio. To their surprise, they found many publications referring to a constant ratio between nucleus size and cell size, but the data didn’t support that. It was enough to compel Martino and Dustin to formally assess the karyoplasmic ratio across a wide range of cell types and species.

They continued amassing data on cell size and nucleus size across as many species as they could find. They collected measurements from 879 species, ranging from microbes to mammals. Then they looked for data within a species and assembled 7,929 observations of both nucleus size and cell size in a diverse range of species including yeast, plants and metazoans. Finally, they returned to the artificially size-selected algae (small and large) and tracked nucleus size and cell size across 500 generations of evolution.

What they found was that while, yes, bigger cells had bigger nuclei, in relative terms bigger cells had smaller nuclei. At all three scales of biological organisation that they looked at (among-species, within-species, and among evolved lineages of the same species) they saw a systematic decrease in the karyoplasmic ratio with increasing cell size.

Martino and Dustin measured nucleus size in two different ways to double check that their observation of decreasing karyoplasmic ratio with increasing cell size was not due to the methods they had used. Here we see that regardless of the method of calculating nucleus size the ratio decreases across a range of different species as well as the large and small evolved algal lines of the same species.

Why do larger cells have relatively smaller nuclei? The authors surmise it might tie into the fact that larger cells also have relatively lower metabolisms. So, is it because larger cells, with their lower relative metabolic rates are able to meet all of their functional needs with relatively smaller nuclei? Or, conversely, is it because larger cells, with relatively smaller nuclei are only capable of sustaining relatively lower metabolic rates? We don’t know.

What we do know is that the decreasing karyoplasmic ratio with increasing cell size is remarkably consistent across a wide variety of life forms. Martino and Dustin hope that a universal driver for this relationship will be identified.

This research was published in the journal Evolution Letters.

Plastic responses to changes in environment are not necessarily adaptive

Phenotypic plasticity is a term familiar to evolutionary biologists. It refers to the ability of an organism to respond to a changing environment by changing its physical properties – its phenotype. For example, metabolic rate changes with temperature and resource availability.

We usually assume that such changes are adaptive, that is, changes are in the same direction as selection and so will increase the fitness (reproductive output) of the organism in that environment. But, importantly, we don’t usually test for the adaptive significance of phenotypic plasticity because we don’t typically estimate selection in different environments when we assess plasticity.

Lukas Schuster and his supervisors, Craig White and Dustin Marshall, used the model species Bugula neritina to investigate whether changes in metabolic rates in response to different field environments are an example of adaptive phenotypic plasticity. To their surprise they found that, while Bugula exhibited plasticity in metabolic rate, it was not adaptive.

Bugula is a small filter feeding colonial bryozoan that is often found on the undersides of piers. It is also found on vertical surfaces such as pier pilings, although the increased UV radiation and sedimentation experienced on vertical surfaces combine to make this a more stressful living environment.

Lukas collected mature colonies of Bugula from the field and then spawned them in the laboratory and settled the larvae onto small acetate sheets. This allowed Lukas to deploy the Bugula on vertically or horizontally suspended panels (corresponding to harsh and benign environments respectively) and to return colonies to the laboratory to measure metabolic rates. They did two experimental runs to test the consistency of the results.

As a first step, Lukas and his supervisors had to determine how selection on metabolic rate varies across harsh and benign environments. In other words, they needed to establish the relationship between metabolic rate and reproductive output (fitness) in each environment.

They deployed newly settled Bugula to a common, benign environment for three weeks before returning these colonies to the laboratory to measure metabolic rates. Half of the colonies were then deployed into the harsh environment and half was kept in the benign environment. Growth, survival and lifetime reproductive output were then tracked for each colony; this allowed the team to determine whether there was any fitness advantage associated with particular metabolic rates in each environment.

Surprisingly, they found no differences in selection on metabolic rates in the two environments. Instead, in one experimental run, they found evidence that smaller individuals with lower metabolic rates and larger individuals with higher metabolic rates went on to produce more offspring in both environments. This suggests that metabolic rate is unlikely to evolve independently of other traits.

To measure plasticity Lukas returned all colonies to the laboratory to measure metabolic rates for a second time. Colonies from the harsh environment had overall lower metabolic rates compared to colonies from the benign environment.

In the first experimental run the team found that smaller individuals with lower metabolic rates and larger individuals with higher metabolic rates went on to produce more offspring (red areas in graph) regardless of the environment they were in.
In the first experimental run the team found that smaller individuals with lower metabolic rates and larger individuals with higher metabolic rates went on to produce more offspring (red areas in graph) regardless of the environment they were in.

Given the strong and consistent metabolic response to the different environments that the team observed, it would have been tempting to infer that such a response increases fitness. While this seems intuitive, it is not consistent with what they know about selection on metabolic rate in the different environments. There was no difference in the relationship between metabolic rates and reproductive outputs in the two environments and so, although the changes they saw in metabolic rate with environment show that metabolic rate is plastic, their results show that such plasticity is not always adaptive.

Lukas and his supervisors emphasise the importance of assessing selection on a trait in the different environments before assuming that ‘plastic’ responses to different environments are necessarily adaptive. Instead, metabolic plasticity may merely represent a passive response due to correlations with other traits or there may be limits to physiological plasticity due to biochemical constraints. Nonetheless, further studies are needed in order to understand the drivers and consequences of metabolic plasticity in the field.

This research was published in the journal Oikos.