Functional
Ecology 1989,
3, 259-268
ESSAY REVIEW
evolution
Trade-offsin life-history
S. C. STEARNS
Zoological Institute,Universityof Basle,
Reinsprung9, CH-4051 Basle, Switzerland
Introduction
259
Trade-offsrepresentthe costs paid in the currency
of fitnesswhen a beneficial change in one traitis
linked to a detrimentalchange in another.Ifthere
then selection would drive all
were no trade-offs,
traitscorrelatedwith fitnessto limitsimposed by
historyand design. However, we find that many
life-history
traitsare maintainedwell withinthose
limits.Therefore,trade-offsmust exist.
Trade-offshave played a central role in the
theory,fromGadgil &
development of life-history
Bossert (1970), Charnov & Krebs (1973), Schaffer
(1972, 1974a, b) and Bell (1980) on to the present.
They have been measured through field observations (e.g. Clutton-Brock,Guinness & Albon,
1982, 1983), throughexperimentalmanipulations
in laboratory(e.g. Partridge& Farquhar,1981) and
field (e.g. Askenmo, 1979), through-phenotypic
correlationsin the laboratory(e.g. Bell, 1984a, b)
and through genetic correlations (e.g. Rose &
Charlesworth,1981a, b), to mentiononly a few of
the more prominent studies. They have been
reviewed by Stearns (1976, 1977), Bell (1980),
Charlesworth (1980), Warner (1984), Reznick
(1985), Partridge& Harvey (1985, 1988) and most
thoroughlyby Bell & Koufopanou (1986). In addihave
tion,the methods used to measure trade-offs
been the subject of criticism (Tuomi, Hakala &
Haukioja, 1983; Partridge,1987) and controversy
(Reznick,Perry& Travis, 1986; Bell, 1986).
The most prominent life-history trade-off
involves the cost ofreproduction.It has two major
components,costs paid in survival and costs paid
in future reproduction. Two approaches to
analysingthose costs were suggestedby Williams:
genetic costs represented by antagonistic pleiotropy(Williams, 1957) and phenotypic costs representedby negativecorrelationsbetween current
reproductiveeffortand futuresurvival and reproduction (Williams, 1966a, b). A third, physiological approach to trade-offshas been developed
by Hirshfield& Tinkle (1974) and Calow (1979),
among manyothers(cf.Townsend & Calow, 1981).
In this extensive discussion, a few points have
not always received the attentionthey deserve:
(1) That trade-offs can be measured and
analysed at the level of the genotype,the phenotype and what lies between (intermediatestructure) is well known and uncontroversialbut it has
not always been emphasized that each of those
levels makes an essential contribution to our
understanding.It is not a question ofeithergenetic
correlationsor phenotypic correlationsor physiological trade-offsbut of how such measurements
combine to deliver informationabout potential
evolutionaryresponses. A studyconducted at just
one of these levels is likelyto be of as littleuse as
the informationon the nature of the elephant
delivered by one blind man holding its tail.
(2) One can draw a useful distinctionbetween
intraindividualtrade-offs- forexample, between
the reproductive effortmade by a female in one
season and the probabilitythatshe will survive to
the next season - and intergenerationaltrade-offs
- forexample, between a female's reproductive
effortand the probabilitythat her offspringwill
survive to the next season. Intraindividualtradeoffs(and only some of them) have received most
attentionbut intergenerationaltrade-offs,which
are arguablyjust as important,have been relatively
ignored.They deserve more attention.
(3) The genetic structureof a population, in
particularthe genetic variance-covariance matrix
fora set ofimportantlife-history
traits,reflectsthe
very recent past, describes the present and predicts the near-termfuture.There is no logical or
directway to use the currentgeneticstructureofa
thatconstrained
population to inferthe trade-offs
the past approach to the currentstate even ifthey
occurred as recentlyas a few tens of generations
ago (J.Travis, personal communication).
(4) Our understanding of a trade-offcan be
described as firstorder (slope known), second
order(curvatureknown) or thirdorder(all details,
including interaction effects,known). In a few
cases we have reliable informationabout firstordereffects.In no case known to me do we have
reliable information on second-order effects,
which are importantin the theory(e.g. Schaffer,
1974a). Measurement of third-order effects,
however desirable (Pease & Bull, 1988), remains a
matterforfutureresearch.
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260
Levels of analysis
S. C. Stearns
at the phenotypiclevel,the
Thereare trade-offs
genotypiclevel and in intermediatestructure,
whichlies betweentheothertwo.By phenotypic
on
measurements
level,I mean whole-organism
and
traitsdirectlyconnectedwithreproduction
survival.By genotypiclevel,I mean all typesof
evidenceclaimedtobe genetic,whetherarrivedat
throughquantitative,Mendelian or molecular
I mean all
structure,
genetics.By intermediate
mechanismsconnectingthe genotypicto the
levels,as heredefined.These include
phenotypic
physiologicaland developmentalmechanisms
controlthatresultin the
underendocrinological
allocationof resourcesamong the functionsof
growth,maintenance,storageand
reproduction,
survival.
Knowledgeofall threeoftheselevelsis necesworks.Natural
how a trade-off
saryto understand
selectionacts on phenotypes;phenotypictradepresented
ofcovariation
thepattern
offsdetermine
to naturalselection.The responseto selection
depends on geneticvariation;genetictrade-offs
measured by genetic covariance determine
whether,and in what direction,a responseto
modustructure
selectionwilloccur.Intermediate
it can
lates the expressionof genetictrade-offs;
changetheexpressionofgeneticcovariancesfrom
positiveto negativeacross a rangeof environmentalconditions.
betweentwo
If a negativegeneticcorrelation
existsbut thepopulationis growingunder
Lraits
conditionsin which mostof the
environmental
variationwithinand betweenthe two traitsis
induced and the geneticdifferenvironmentally
then
encesare not expressedin thephenotypes,
will notbe presentin thephenotype
thetrade-off
& Dingle,
andno selectionwilloccur(e.g.Groeters
1988). On the otherhand, if thereare strong
negativephenotypiccorrelationsbetween two
traitsthathave,however,no geneticbasis whatsoever,a selectionpressuremay exist but no
evolutionaryresponse is possible. Further,if
mechanismsassociatedwithgrowthand phenomodulatetheexpressionofgenetic
typicplasticity
covariation
betweentwo traits,so thatthe correlation is positive under some conditionsand
negativeunderothers,thenwe would perceivea
trade-off
as existingunderthelatterbutnotunder
the formercircumstances(Gebhardt& Stearns,
1988).Finally,whenevera variableenvironmental
factoraffectsone traitmore than another,the
betweenthetwotraitswill
correlation
phenotypic
andwillalterthe
environments
across
also change
selection gradients.For example, the phenotypic
correlationbetween developmental rate and size
at metamorphosis in frogs can be altered from
positive to negative within a full-sib family by
varyingfood level and distribution(Travis, 1984).
These effectsare all important.They determine
whethera trade-offis exposed to selection in the
phenotype,whetherthe geneticresponse to selection behaves like a trade-offand whether the
trade-off appears at all, depending on the
environment.
Phenotypic and genetic trade-offsare both well
anchored in the literature. That intermediate
structurecan modulate the expression of genetic
correlationsis not as well appreciated. This effect
is thereforeillustratedhere fortwo trade-offs:(1)
age vs size at maturityand (2) reproductiveinvestment vs survival. (These examples are intended to
illustratewhat can happen, notwhat musthappen
in every case. Necessary conditions are not yet
known.)
Age vs size at maturity.Consider a population
living in an environment heterogeneous for
growth conditions. In some places and at some
times, growthis rapid; in others,growthis slow.
Organisms living in such an environmentshould
evolve a reaction norm forthe maturationevent.
The commonesttype of reactionnormforage and
size at maturityis one in which the organisms
mature early at a large size when growthis rapid
and later at a smaller size when growthis slow
(Stearns & Koella, 1986). Each polygenicgenotype
will have a differentreaction norm. There are
many relations among reaction norms that will
produce a sign change in geneticcorrelations.Two
of these are not uncommon and could explain
many observationsof sign change:
(a) the largest and latest maturing genotype
under good growth conditions is the largestbut
earliest maturing genotype under poor growth
conditions,and the restofthe genotypespreserve
this ordering (Fig. 1). The reaction norms may
cross, as depicted here but this is not a necessary
condition;
(b) growth rates have negative genetic correlations with maximal size, i.e. the fastest-growing genotypesproduce the smallest adults, so that
the extrapolated growth curves of the different
genotypeswould intersectiffollowed farenough.
Under this assumption, the reaction norms of the
different
genotypesneed not intersect.(Iftheydo,
their intersection strengthensthe effect.)Fig. 2
plots growth curves and reaction norms for the
maturationevent forthreegenotypes.The growth
curves change theirrank order,measured earlyin
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261
in
Trade-offs
0J~~~~s
~~correlation
life-history
evolution
curves
l1orGrowth
3 genotypes
Positive
genetic
2
3
//
-
in "good"
nvironment
Crossingreactionnorms
forage and size at
\
Size
maturity
2 Growthcurves
for 3 genotypes
in itpoori
environment
/
Negative
genetic
correlation
Age
a changein signof
forproducing
Fig.1. One mechanism
geneticcovariance:thereactionnormsofthegenotypes
growthcurvesforthreegenotypes
cross.Hypothetical
rapidandslowgrowth.
aredrawnfortwoenvironments;
foreach
The squaresdenotetheage and size at maturity
The word 'genotype'
genotypein each environment.
should be takento mean the polygenicgenotypeof
the whole genome,
genetics,representing
quantitative
two-allelegenotypeofpopulation
notthe single-locus,
genetics.
life,across environmentsbut the reaction norms
do not. The heavy points indicate maturation
events foreach genotypeunder each growthcondition. The genetic correlationbetween age and
size at maturitychanges frompositive under good
growthconditions, at the left,to negative under
poor growthconditions,at the right.
Thus genetic covariance changes frompositive
to negative eitherwhen the growthcurves of the
genotypescross orwhen theirreactionnormshave
differentslopes or ranges. When growth condi-
Reaction norms
Ail
=
Growth curves
$3 Good
~~~~~~~2
habitat
Size
Correlation
Poor
habitat
Age
Fig. 2. A secondmechanismforproducinga changein
sign of geneticcovariance:the growthcurves cross
size have negativegenetic
rateand asymptotic
(growth
curvesforthreegenogrowth
correlations).
Hypothetical
(2) and slow (3) growthtypes- fast(1),intermediate
Thesquaresdenotethe
areplottedfortwoenvironments.
foreach genotypein each
age and size at maturity
the heavy lines representthe reaction
environment;
normsofdifferent
genotypes.
tions vary, we can expect genetic covariances to
varyfrompositive to negative as well.
This effectis more than a theoreticalpossibility.
Giesel, Murphy & Manlove (1982) measured large
changes in genetic correlationsbetween various
life-historytraits in Drosophila melanogaster
Meigen raised at three differenttemperatures.
Preliminaryevidence indicates that the genetic
covariance between age and size at hatching in
Drosophila mercatorum Patterson & Wheeler
changes frompositive when larval growthis rapid
on a rich diet to negative when larval growthis
slow on a poor diet (Gebhardt& Stearns,1988). G.
de Jong(personal communication and in preparation) has analysed both an explicit two-locus
model and a polygenic model and concludes that
'the change in sign of genetic covariance is much
easier to obtain than to avoid' when genes have
unstructuredpleiotropic effectson growth rates
and reaction norms.
Crossingreactionnormsare a particularlystrong
case ofgenotypex environmentinteractions.Such
interactions are expected to be pervasive for
componentsoffitness,forwhen reactionnormsdo
not cross, when one always lies above the other,
then that reaction norm is superior across all
environments and will be fixed. This process
should continue untilthe only reactionnormsleft
in the population are those that cross, i.e. where
genotypes reverse ranking across environments.
When reaction norms cross, genetic correlations
are likelyto reversesign.
Reproductiveinvestmentvs survival. A similar
analysis can be constructedforany othertrade-off
among traits that are phenotypically plastic.
Reaction norms can also be predicted for the
trade-offbetween reproductive investment and
survival,one ofthe two classical costs ofreproduction (Uchmanski& Stearns,in preparation).Where.
genetic variation exists for such reaction norms,
one expects only the crossing norms to be maintained. In Fig. 3, the overall phenotypictrade-off,
measured across a range of environmentalconditions and for many genotypes, is negative: an
ellipse drawn around all points observed would
have a majoraxis runningfromtheupper leftto the
lower right. However, within that phenotypic
variation are importanthidden details that make
all the difference.Genetic variation in reaction
norms, simplified here as linear norms for only
threegenotypes,is expressed in crossinggenotype
x environmentinteractions.
In the left-handportionofthe diagram,withlow
survival and high investment,the genetic correlation between reproductive investment and
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262
S. C. Stearns
To thatit should be added thatenergeticinvestment must be measured but almost never has.
Exemplary attemptshave been made by Calow &
Positive genetic
\
/
\\
w t)
.
Woollhead (1977), Hirshfield(1980) and Rekkie &
?t
correlation
Bazzaz (1988a, b). The most commonly used
measures of 'reproductive effort,''reproductive
0-u
allocation' and 'reproductive allotment' are indirectand hard to interpret.
No genetic
Figs 1-3 depict a type of interactionbetween
correlation
genetic and phenotypic variation and covariation
in which physiological mechanisms make a big
Adult Survival to the next Age Class
difference,for they embody the constraintsthat
betweenreproductive forcethe reactionnormsor growthcurves to cross,
Fig.3. Ifone measuresa trade-off
at a certainage and survivalto thenextage that cause changes in rank of genotypes across
investment
theoverall environments.We have almost no informationon
conditions,
acrossa rangeofenvironmental
maybe negativebutwithinthat
correlation
phenotypic
thatlead to those changes in rank;
phenotypicvariationthe expressionof geneticcorre- the mechanisms
in rank are common, that inforchanges
if
such
or
zero
environments,
some
in
negative
lationsmaybe
mation will become crucial.
in someand positivein others.
non-significant
Ne ative genetic correlation
o
Reaction norms
survival is negative. In the right-handportion of
the diagram, with high survival and low investment the genetic correlation is positive. In the
middle,thereis no geneticcorrelationbetween the
two traits.Although that portion of the diagram
lies in the centreofa phenotypiccorrelationthatis
negative,no geneticcorrelationis expressed under
those environmentalconditions and therewill be
no response to selection in environmentscorresponding to the middle ofthe diagram.
People who are tryingto measure trade-offs
throughexperimentalmanipulations may want to
ask themselves whether the variation in the
environmentcreated by the manipulation takes
place entirelyon the leftor the rightof Fig. 3 or
whetherthe manipulations create a mixtureofthe
two extremes.In the lattercase, the resultswill be
difficultto analyse unless an unusual amount is
known about both genetic and environmental
sources of variation.
Fig. 3 depicts one ofthe simplest possible types
of genotype x environmentinteractions.In real
data sets, reaction norms may cross and recross
each other several times and genetic correlations
between traits may change back and forthfrom
positive to negative repeatedly as an environmental factor changes continuously across its
normal range. In some species, genetic correlationswill be negative in well fed and positive
in poorly fed populations. In other species with
different
physiologies,genetic correlationswill be
positive in well fed and negative in poorly fed
populations. Fig. 3 depicts only one of these
possibilities; theyare not exhaustive and can only
be distinguishedby experiment.
oftrade-offs
The measurement
Genetic methods
In their thorough review of trade-offs,Bell &
Koufopanou (1986) noted a patternwithimportant
implications. When people have triedto measure
genetic correlations with breeding designs,
crosses or
usually variations on full-sib/half-sib
di-allele crosses, the results have been mixed.
Sometimes the genetic correlationsare negative,
sometimes positive, sometimes insignificant.
However, when the sign of the correlations has
been measured throughthe correlatedresponse of
one trait to selection on another, negative correlations,genetic trade-offs,have been measured
much more frequently.The results of breeding
designs and selection experimentscarried out on
the same traitsand oftenthe same species have not
been consistentwith each other.
Given the statistical problems inherent in
breeding designs, this may not be surprising.It is
usually difficultin practice to raise enough families to avoid sampling problems, to which the
estimates of covariances are especially sensitive.
In selection experiments,it may be easier to start
with and to maintain a largereffectivepopulation
size. This could explain why the results of a large
number of such experimentsare more consistent.
Many people are now attemptingto measure
genetic trade-offs.The pattern noted by Bell &
Koufopanou (1986) suggeststhatapproaches using
correlated responses to selection could be preferredto breeding designs because they produce
more consistent, replicable results. Selection
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263
Trade-offsin
life-history
evolution
experimentsalso have an inherentlogical advantage: they predict correlatedresponses to natural
selection directlythroughcorrelatedresponses to
artificialselection ratherthan indirectlythrough
breedingdesign. However, ifFigs 1-3 are correctit
will also be necessary to measure correlated
responses to selection under at least two and
preferablythree environmental conditions representingthe range naturallyencountered.
Anotherresponse to the inconsistencyofresults
frombreeding designs can also be justified. The
results mentioned mightnot be due to sampling
variationbut an accurate reflectionofreal patterns.
If so, then some inherentadvantage of breeding
designs over selection experimentsshould not be
the informationextractedper individual
forgotten:
is higher; it is easier to control environmental
conditionsforthe durationofthe experiment;and
some breeding designs can be completed more
rapidly than some selection experiments.
Physiological mechanisms
or
Whetherone perceives a physiological trade-off
not depends on the type of organism examined.
That successive reproductive attemptsshare the
same resource pool is unlikely in small birds and
mammals,in which high metabolic rates leave no
room forstorage.A shrew or a hummingbirdmust
use what ithas eaten in thelast fewhours to suckle
or produce its eggs. They are 'income
its offspring
breeders', using currentincome forreproductive
investment(Thomas, 1986). Income breedershave
a very short-term'physiological memory'. The
suctorian Tokophrya is a particularly clear example of an income breeder. It feeds on Paramecium and produces one offspring for each
Paramecium eaten. Kent (1981) could constructa
wide range of fecundityschedules by varyingthe
amount offood offered.
Greattitsmay be income breeders in one sense
but not in another. Pettifor,Perrins & McCleery
(1988) found no evidence fora trade-offbetween
clutch size and adult survival in great tits, but
Tinbergen (1987), Smith, Killander & Nilsson
(1987) and Linden (1988) all found a trade-off
between clutch size and success ofsecond broods.
While Tinbergen(1987) and Linden (1988) found
no effectofincreased clutch size on adult survival,
Nur (1984) found such evidence in blue tits. One
might define tits,where short-termcosts within
costs are not at all
seasons are clear but longer-term
clear, as capital breeders within seasons but
income breeders between seasons. Sibly & Calow
(1984, 1986) referto income breeders as having
'direct costing' - the cost of reproductionbeing
drawn out of currentrevenue.
On the other hand, 'capital breeders' store
energythat can be mobilized later for reproduction. This creates a mechanical linkage between
current and future and reproduction. Good examples of capital breeders include red deer
(Clutton-Brock,Guinness & Albon, 1983) and
Daphnia (Goulden & Hornig,1980).
Sibly & Calow (1984, 1986) refer to capital
breedersas having 'absorption costing' - the cost
of reproduction is drawn against physiological
savings previously made. The mathematics of
direct and absorption costing differ;most lifehistorytheoryassumes absorption costing,which
involves primarily physiological mechanisms.
The direct costing used by income breeders is
more easily related to behavioural mechanisms,
such as decisions on how long to forageeach day,
than it is to costs of reproduction.
Income and capital breeders represent the
extreme points on a continuum. It would be
interestingto know whether disruptive selection
tends to shape any given species towards specialization for one type or the other but not some
intermediate mixture. Some data bear on this
point. If kestrels are allowed through manipulations to increase their income, they start
breeding earlier, lay the larger clutches that are
normally found earlier in the year, and also get
fatter(Meijer,Daan & Dijkstra,1989). They appear
to mixthe two alternatives.The poeciliid fishesare
a family that contains the full spectrum from
'capital breeders', such as guppies (Poecilia) and
swordtails (Xiphophorus), to 'income breeders',
such as Heterandria formosa Agassiz (Turner,
1937). Comparative studies among species within
this familycould get at the reasons forthe evolution of the differencebetween capital and income
breeding(J.Travis, personal communication).
Most physiological decisions on growth,storage
and reproduction are mediated by hormones.
Hormones affectmany processes simultaneously,
creating patterns of correlations among many
responses. Of course, these patternsdepend both
on the concentrationofhormonesin the body and
cell typesto those
on the sensitivityofthe different
hormones.Both can be changed in evolution. That
sex hormones mediate trade-offsis stronglysuggested by evidence fromthe human reproductive
system:female sex hormones thatpromotereproduction early in life are also involved in disturbances to calcium metabolism later in life. Could
this be an example ofhow antagonisticpleiotropy
shapes the evolution of senescence? Endocrine
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264
S. C. Stearns
regulation is an obvious aspect of intermediate
structurewith impact on phenotypic correlations
that has received almost no attention in lifehistoryresearch.Workin this area should become
a higher priority. Endocrine mechanisms may
prove to be only the tip of an iceberg of physiological mechanisms thatmodulate the expression
ofgenetic covariance.
Whymight we get positive correlations when we
expect negative ones?
Life-historytheorypredicts negative correlations
among certain traits;however, observations indicate thatthese traitsare oftenpositivelycorrelated,
especially when the measurements are made on
individuals within populations (Bell & Koufopanou, 1986). van Noordwijk & de Jong (1986)
have pointed out a simple interactionthatcan lead
to positive, zero or negative correlationsbetween
two traits that are nevertheless involved in a
classical physiological trade-off.Suppose thatthe
energy acquired in food, A, is strictlyallocated
withineach individual eitherto reproduction,R or
to survival, S:
A = R + S.
This is just the Principle ofAllocation asserted by
Levins (1968) and Sibly & Calow (1986). However,
as stated it does not make explicit the variation
among individual organisms that always occurs.
In this context,individuals vary in the amount of
energy acquired, A, and in the fraction of the
energyacquired thatis allocated to reproduction,
B. If we indicate each individual by a subscripti,
then we have
Ifwe observea positive correlationwhere theory
tells us to expect a negative one, this analysis
suggests the critical empirical questions that
should be asked. Do the organisms vary more in
the amount ofenergyacquired than in the fraction
ofenergyallocated to each function?How do those
two variances (in A and B) change across environments in nature?The answers to those two quesis
tionswill help us to decide whetherthe trade-off
importantin naturalpopulations or not.
S
(b)
(a)
3
B = 033
2
2
s
3 50
B
1
A1
1
=0~~~
~~.66(c
A2
A3
2
3
R
i
R
S
C
R
(R) and
betweenreproduction
Fig. 4. Considera trade-off
survival(S). The totalamountofenergyacquired is set by
A, where A = R + S. The fractionof energyallocated to
reproductionis determinedby B, so Ri = BiAi In (a), the
general possibilities are sketched. In (b), the variation
among individuals in energyacquired (A) is largebut the
variation in fraction allocated to reproduction (B) is
small. The result is a positive correlation between
reproduction and survival. In (c), the variation in A is
small but the variation in B is large. The result is a
negativecorrelationbetween reproductionand survival.
Characterizingtrade-offs
Ri = BiAi,
Intraindividualand intergenerational
Si = (1 -Bi)Ai
have received attentioncommenNot all trade-offs
surate with their probable importance. Intraindividual trade-offshave played a much larger
role to date than intergenerationaltrade-offs.
However,to takejust one example, any change in a
female's allocation strategyis likelyto have implications forjuvenile mortalityin those organisms,
like birds,manyfish,insectsand mammals,where
parental care or provisioningis important.Such
intergenerationaleffectscan be quite real. In the
poeciliid fish Heterandria formosa, there is a
between numberofbroods and offspring
trade-off
size, and larger offspringhave better survival
(Henrich, 1988). Similar effectscan be found in
wild radishes (Stanton, 1984). Here an intraindividual trade-offbetween brood number and offspringsize becomes an intergenerationaltrade-off
between brood intervaland juvenile survival.
The point thatvan Noordwijk & de Jongnoticed
is thatthe sign ofthe correlationbetween R and S
will depend on the relative degree to which each
varies among individuals. This is most easily seen
in a graph (Fig. 4). van Noordwijk & de Jong(1986,
p. 141) explain this result with an analogy to
economics: 'ifthebudgetis fixed,people spending
moreon housingshould spend less on cars. In fact,
the amount of expendable income is variable, and
in many situations positive correlations are
observed between the per-familyexpenses on
housing and on cars. There is little problem in
identifyingrich and poor familieson this basis...
Where biologists have observed positive correlations between life-historytraits, they have
oftenalso identifiedindividuals thatperformwell
or poorly.'
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265
Trade-offsin
life-history
evolution
Consider Table 1, whose entries number the
As an exception to the widespread concenpairwise trade-offs
forconvenientreference.Note
trationon only a few trade-offs,10 trade-offs(13,
thatsome entriesare notindependent (CR = NO x
17 and 39 plus seven others involving age at
SO), that not all possible trade-offsare listed, and
maturityand interbroodintervalnot listed in the
that no such general description can cover all
table) were systematically analysed in Sibly &
types of organisms.
Calow (1986, Chapter4) using methods developed
Of the 45 possible trade-offsamong these 10
in Sibly & Calow (1982).
traits, only five receive much attention in the
literature: numbers 1 (current reproduction vs
The scope of application ofgenetic structure
survival); 2 (current vs future reproduction); 3
(currentreproductionvs parental growth);4 (curMany people have been measuring genetic corrent reproduction vs parental condition) and 36
relations among life-historytraits to get at con(numbervs size ofoffspring).
Othersare notas well
straintson evolution. For a numberofreasons, the
investigatedas the five discussed but could procurrentgenetic structureof the population may
duce importanteffects.For example, does parental
not tell us much about the past conditions that
growthtrade-off
with offspring
condition (number
could have been decisive forthe life-historypat29)? That would seem plausible in organismswith
ternseen today. For example, ifthe currentstateof
indeterminate growth and parental care and
the population has been produced by strongselecwould be a type of parent-offspring
tion in small, inbred populations, then epistatic
conflict.Does
parental survival trade-off
with offspringsurvival
and dominance interactions probably played a
(number17)?
more importantrole than additive variances and
The normal assumption for analyses of the
covariances. Genetic covariances are especially
trade-offbetween offspringsize and offspring sensitiveto changes in gene frequenciesand to the
number is that a certain level of reproductive
substitution of genes with differentpatterns of
investmenthas already been fixed,thus fixingthe
pleiotropic effects. Furthermore,if genotype x
relationship between reproduction and parental
environment interactions of the sort discussed
above were present, a simple change in the
survival,and thatthe trade-offoccurs within that
framework.There is no reason, a priori,to believe
environment could strongly modify the exthis is the case. If reproductive investment, pression ofgenetic covariances.
For all these reasons and more, one should
parental survival, offspringsize and offspring
approach with caution any claim that negative
survival define a four-dimensionalsurface,then
genetic correlations measured today have relparental and offspringsurvival can be seen as
evance forthe historicalpath thatled to the current
elements of a two-traittrade-off
with just as much
justificationas reproductive investmentand off- state. We can ask two questions about life-history
patterns: what constrains future evolution and
springsurvival.
Table 1. An incomplete trade-off
matrixforlife-history
traits.Parental survival and parentalgrowth:fromthe current
reproductiveepisode to the next.Parentalcondition:takenbetween thisreproductiveepisode and the next,preferably
on a standard date criticalforsurvival (e.g. late winter).Size of offspring:
to be reportedas a series ofsizes forspecies
with parental care (birth,fledging,independence). Additional columns could be added to representthe trade-offs
associated with maturation.Intraindividual trade-offsare indicated in bold type. Intergenerationaltrade-offsare
indicated in italics.
Trait 2
Trait 1
Currentreproduction
Parental survival
Future reproduction
Parental growth
Parental condition
Number of offspring
Size of offspring
Offspringgrowth
Offspringcondition
Offspringsurvival
CR
PS
FR
PG
PC
NO
SO
OG
OC
OS
PS
FR
PG
PC
1
-
2
10
-
3
11
18
-
4
12
19
25
-
NO
5
13
20
26
31
-
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SO
6
14
21
27
32
36
-
OG
OC
7
8
15
22
28
33
37
40
-
16
23
29
34
38
41
43
-
OS
9
17
24
30
35
39
42
44
45
266
S. C. Stearns
what produced the patterns we see today?
Measurement of genetic trade-offscertainlyilluminatesthe firstbut may not be as relevantforthe
second as might be hoped (J. Travis, personal
communication).
This comment is not so much a council of
despair as an indication of a research program.
Geneticcovariances could be measured in a set of
closely related species. If the structure of the
correlationmatriceswere similar,it would count
Forthe long-termstabilityof trade-offstructures
within populations and against the cautionary
commentabove. If they differedin closely related
species that had originated recently, it would
The speciation events
countforthisinterpretation.
in the study group should be recent and datable
and breedingor selection experimentswould have
to be possible on a scale thatwould yield reliable
estimatesof differences.
functions
Signs and shapes of trade-off
A negative relationshipbetween two traitscan be
linear,convex, or concave, to mentionthe simplest
possibilities. Schaffer's (1974a) theoretical work
demonstratesthat such differencesin shape can
strongly influence the predicted evolutionary
responses. Thus we always have three important
questions to ask about the relationship between
two traits:(1) is it positive or negative?;(2) what is
its shape?; (3) what are its interactionswith other
traitsand environmentalfactors?
The firsttwo questions would be answered ifwe
knew the signs of the firstand second partial
derivativesof the second traitwith respect to the
first.The matrixof firstpartial derivativesdefines
the first-ordertrade-offstructure,the matrix of
second partial derivatives defines the secondordertrade-off
structureand the matrixcontaining
a graph in each cell definesthe complete two-way
trade-of structure.Any trade-offsthat are nonlinear in a complex fashion can only be properly
described by the thirdmatrix,the complete tradeoffstructure.Interactioneffectsamong trade-offs
would go beyond that (Pease & Bull, 1988). The
graph in each cell for the complete trade-off
structuremighthave to describe a complex surface
in threeor more dimensions,ratherthan a line in
two dimensions,to show interactioneffectsamong
two or more trade-offs.
These matricesrepresentthe manner in which
the organism is integratedwith respect to lifehistory traits. They abstract the developmental
physiological and ecological implications that
each traithas forthe others.Schaffer's(19 74a) and
Bell's (1980) analyses of the trade-offsbetween
reproduction and survival and between current
and futurereproductionsuggestedthatthe curvature of some trade-offsis a critical piece of
information.Whetheran organism should invest
everythingin currentreproduction,to the detriment of growthand survival, or choose an intermediate level of reproductive investment,
reservingsomething for the future,depends on
whether those trade-offsare convex or concave.
This is a qualitative condition - we just want to
know whetherthe lines bend up or down - but it
has proven to be quite elusive. Bell & Koufopanou
(1986) summarized the few attemptsto measure
thatcurvature(e.g. rotifers,annual meadow grass)
and in every case concluded that the patternwas
not detectablydifferent
froma straightline.
Trade-offsor dynamic linkages?
If genotype x environmentinteractionsare pervasive and iftheyhave the sortsofeffectsdepicted
in Figs 1-4, then it is natural to ask whether we
should continue to use the word 'trade-offs'at all.
Trade-offs are usually conceived of as static,
dependable relationships:moreofthis,less ofthat.
They certainlyapply to energeticrelationswithin
individuals. When we move fromthe individual to
the population, things become more complex. If
interactionsthere are as dynamic and flexibleas
Figs 1-4 would suggest, and the relationship
between two traitscould easily change frompositive to negative over the normally encountered
environmental range, then the more general
phrase 'dynamic linkage' mightbe preferableto
'trade-off' as a descriptor of the underlying
mechanisms. Population-level trade-offs differ
qualitatively from trade-offsamong functions
withinindividuals.
Conclusion
Measuring the cost of reproductionhas proven to
be difficult.The perspective advanced here will
not make it any easier. Both phenotypic and
genetic correlations among traits must be
measured across the range ofenvironmentsnaturally encountered beforeone can claim that costs
and selection gradientshave been estimated.Genetic correlations are more reliably measured in
selection experimentsthan withbreedingdesigns.
Correlated responses to selection must also be
measured across a range of environmentalconditions to estimate reliably the impact of genetic
correlationson evolutionarychange. Much more
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267
Trade-offsin
life-history
evolution
attentionneeds to be givento the mechanical basis
of trade-offsin aspects of intermediatestructure.
The relationship between endocrine action and
genetic and phenotypic correlationin particular
deserves more attention.
Acknowledgments
This work was supported by the Swiss Nationalfonds. Comments by Jacqui Shykoff,Martin Gebhardt, Bruno Baur, Elke Hillesheim, Arie van
Noordwijk,JoostTinbergen,David Reznick, Peter
Calow, Joe Travis and Paul Harvey improved the
presentation. The ideas in Figs 1 and 2 were
developed in cooperation with Jacob Koella and
MartinGebhardt.
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