Life Histories and Evolutionary Trade-offs

Strap a harness carrying 3 to 4 grams onto a breeding skylark, about a tenth of its body mass, and its nest tends to produce fewer fledglings. Nobody took any food away. So where did the chicks go?

One reading is that they went into the extra flying. A bird that pays more for each foraging trip may have less left for its young, its own upkeep, or both. That is the core of life-history theory: every organism works from a limited budget, and the way the budget is split across a lifetime is something selection acts on. This lesson builds the budget, follows two classic ways of sorting the splits, and then asks the question that decides whether any of it is true: how do you test a trade-off?

The idea: a fixed budget forces trade-offs

An organism’s life history is its schedule: how fast it grows, when it first breeds, how many offspring it makes and how large they are, how often it breeds, and how long it lives. Each of these affects fitness, an individual’s lifetime reproductive success relative to others in the same environment (the measure you met in selection in ecological context).

Think of a monthly paycheck. Rent, food, savings and fun all come out of the same pay, so more of one means less of another. In an organism, what is acquired as energy and nutrients is split among growth, maintenance and repair of the body, storage, and reproduction. A trade-off is the negative link this creates: more allocated to one trait leaves less for another. Resource-allocation models make this explicit. In the determinate-growth model of Dańko, Burger and Kozłowski (2017), what remains after maintenance and repair goes to growth before maturity and to reproduction afterward, so more spent on repair means less for growth.

Two trade-offs come up again and again:

• Current versus future reproduction. Breeding heavily now can lower survival or reproduction later, because what is spent on this year’s young is not spent on the parent.
• Number versus size of offspring. A fixed amount put into a clutch can make many small eggs or a few large ones.

These are hypotheses about allocation, and a model only says what follows if its budget is fixed. Real organisms differ in how much they acquire, and that, as you will see, can hide a trade-off completely. How heritable life-history traits are, and how to estimate it, belongs to complex traits and GWAS.

Semelparity, iteroparity and the cost of reproduction

Some organisms breed once. Semelparity is a single reproductive episode in a lifetime, usually followed by death: many annual plants, most Pacific salmon (genus Oncorhynchus), and century plants (Agave) are standard examples. Iteroparity is repeated reproduction, as in most birds, mammals and long-lived trees.

It is tempting to say that a salmon dies because it spent everything on spawning. That turns a physiological description into an explanation it does not have. The evolutionary question is comparative: in a given environment, did individuals that put more into one bout leave more descendants than those that held some back to survive and breed again? The payoff from holding back depends on how likely an adult is to survive to the next season. Where adult survival is low, the expected return from future breeding is small, and models predict that a larger single effort can be favoured. Where adult survival is high, repeated breeding can pay.

The cost of reproduction is the reduction in survival or future reproduction that follows from reproducing now. It is easy to assume and harder to show, because the cost need not run through a single shared budget. Dietary restriction lengthens life and lowers egg production in laboratory female Drosophila melanogaster, which looks like a classic trade-off. Grandison, Piper and Partridge (2009) added single nutrients back to the restricted diet. With methionine alone, fecundity rose to the fully fed level and lifespan did not shorten. In these flies, long life did not require low reproduction; the link ran through the balance of particular nutrients. The signalling pathways that sense such nutrients are in mTOR and AMPK. This is one species in the laboratory, and it gives no basis for any advice about human diet, fasting or supplements.

r and K, then the fast-slow continuum

Two symbols from population growth name the classic scheme. r is the maximal intrinsic rate of increase, the rate at which a population grows per individual per unit time when nothing limits it. K is the carrying capacity, the population size the environment can sustain. The growth models that use them are taught in full later in this course, under population growth.

Robert MacArthur and E. O. Wilson introduced the terms r-selection and K-selection in 1967. Eric Pianka’s 1970 paper in The American Naturalist listed what each should favour. Where populations often grow freely (r-selection), he listed early reproduction and little investment in each offspring among the correlates. Where populations sit near K (K-selection), he listed delayed reproduction and greater investment per offspring. He set semelparity against iteroparity in the same comparison. In a 1979 commentary, Pianka called the two ends of a spectrum, not a true dichotomy.

The scheme is best read as contrasting predictions, not a filing system. A species with early maturity and many small offspring has not thereby been shown to be under r-selection; other regimes can produce the same bundle. Field tests of particular causes have done better. Reznick, Bryga and Endler (1990) studied guppies (Poecilia reticulata) in Trinidad that Endler had moved in 1976 from a high-predation site to a low-predation one in a tributary of the Aripo River. Within 11 years, an estimated 30 to 60 generations, the introduced population had changed in the life-history traits in the direction the predation contrast predicted. Later work found that high-predation guppies’ fitness was more sensitive to rising density than low-predation guppies’ (Bassar and colleagues, 2013), so predation and crowding act together.

That matters because the textbook rule “more extrinsic mortality selects earlier maturity” is not unconditional. In Dańko and colleagues’ 2017 model, extra deaths that strike all ages alike left the stable allocation unchanged when crowding reduced survival evenly, selected earlier maturity when crowding reduced fertility, and selected later maturity when it reduced the rate of production.

The modern descendant of r/K is the fast-slow continuum: across species, early maturity, high annual fecundity and short life tend to go together, as do the opposites. Van de Walle and colleagues (2023, Proceedings of the Royal Society B) used long-term individual records from 17 bird and mammal species. Across species, after correcting for shared ancestry, the first axis of trait variation described 77% of it using their average phylogeny. Within the sampled populations, though, individual differences did not line up with that axis. A pattern among species is not a rule for individuals.

Testing a trade-off: correlation versus manipulation

If breeding is costly, you might expect birds with big clutches to die sooner. In many populations they survive better. Van Noordwijk and de Jong (1986) explained why this does not refute a trade-off. Return to the paycheck: people with large incomes tend to have both bigger houses and newer cars, though for any one income, money spent on the house is not available for the car. Individuals that acquire more can afford more of everything, so variation in acquisition produces a positive correlation even when allocation imposes a negative one.

The fix is to change one thing on purpose: enlarge broods, add a handicap, or add back one nutrient. A 2025 meta-analysis in eLife combined 78 effect sizes from 46 papers on 30 bird species. Naturally larger clutches went with better parental survival; experimental brood enlargement lowered survival on average, with a small effect. The raw estimate per added chick, −0.0522 on the log-odds scale, had a 95% interval from −0.1406 to 0.0363, which includes zero. So “each extra egg costs X% survival” is not supported.

Worked problem. Hegemann, Matson, Flinks and Tieleman (2013) gave breeding skylarks (Alauda arvensis) in the Netherlands (Aekingerzand; experiments in 2007 and 2008) a harness plus 3.0 to 3.9 g of extra load, about 10% of body mass. Pairs were assigned alternately; controls carried neither harness nor load. In first broods, 19 of 25 control nests succeeded and 8 of 17 handicapped nests did. Mean fledglings per nest were 2.0 for controls and 1.2 for handicapped pairs.

1. Control nest success. 19 / 25 = 0.76, or 76.0%.
2. Handicapped nest success. 8 / 17 = 0.4706, or 47.1%.
3. Difference. 47.1% − 76.0% = −28.9 percentage points.
4. Relative reduction. (0.76 − 0.4706) / 0.76 = 0.381, or 38.1%. This is a different number from step 3, because it is measured against the control rate. Percentage points compare two rates directly; percent compares one to the other.
5. Fledglings. 1.2 / 2.0 = 0.60, so handicapped nests produced 40% fewer fledglings per nest.
6. Read the statistics. The nest-success comparison gave P = 0.055 and the fledgling comparison P = 0.042. The adult return rate did not differ significantly.

Successful nests among 25 controls 19 Successful nests among 17 handicapped 8

Show the gap in nest success as

percentage points percent of the control rate

Control nest success

Handicapped nest success

Gap

Fledglings per nest, 1.2 / 2.0

In this sketch: arithmetic only. The fledgling bars are the published means (2.0 and 1.2) and do not follow the sliders. The harness and the load cannot be separated, and the counts do not measure a survival cost to the adults. Counts other than 19 of 25 and 8 of 17 are what-ifs, not the study’s data.

Percentage points compare two rates directly; percent compares one rate with the other, and is measured against the control.

relative reduction = (control rate − treated rate) / control rate

The conclusion: the combined harness and load reduced reproductive output in this population, and the study did not establish a survival cost to the adults. Harness and load cannot be separated, because no group wore a harness alone, and alternation is not concealed randomization. A harness-only group would fix the first problem. For how to frame such a test in general, see testing evolutionary explanations.

Design problem

Canal, Garcia-Gonzalez and Garamszegi (2021) gave laboratory female seed beetles (Callosobruchus maculatus) either 5 or 40 unused beans per female to lay eggs on (the restricted group’s other beans had already been used), then plenty of beans for both groups. In this species adults draw their reproductive and survival resources from what they ate as larvae inside a seed. Restricted females laid fewer eggs early and more just after release, and lived slightly shorter lives: mean 8.68 versus 9.27 days (SEM 0.21 days each). Does this show that delaying reproduction costs lifespan? Design a better experiment.

One way to do it

Not yet. The treatment changed two things at once. Five unused beans per female restricts where she can lay, and the already-used beans in the container carry other eggs, which may change contact with other beetles or competition for laying sites. Either could shorten life. And the shorter life might come from the burst of laying after release rather than from the delay itself.

Separate the factors with a crossed design: bean number (5 or 40) crossed with the number of females per container (one or several). Rear all females on the same larval host and density so they start with equal reserves, and assign them at random. For each female, record eggs laid each day, the number of offspring that reach adulthood (eggs are not realized fitness), and lifespan from daily checks.

State the predictions first. If delay itself is costly, low bean number shortens life even for solitary females. If competition is the cause, the female-number factor shortens life whatever the bean number. If the compensatory burst carries the cost, lifespan should fall with the number of eggs laid after release, and a third group released gradually, without a burst, should live as long as controls.

Keep the scope: this is one laboratory population of one capital-breeding beetle, and it does not tell you how wild beetles, let alone other animals, allocate.