Empirical tests of life-history evolution theory using phylogenetic analysis of plant demography

SPECIAL FEATURE

ADVANCES IN PLANT DEMOGRAPHY USING MATRIX MODELS

Empirical tests of life-history evolution theory using

phylogenetic analysis of plant demography

Jean H. Burns1*, Simon P. Blomberg2, Elizabeth E. Crone3, Johan Ehrlen4,

Tiffany M. Knight5, Jean-Baptiste Pichancourt6, Satu Ramula7, Glenda M. Wardle8

and Yvonne M. Buckley2,9

1Center for Population Biology, University of California, Davis, CA 95616, USA; 2School of Biological Sciences,

The University of Queensland, Brisbane, Qld 4072, Australia; 3Department of Ecosystem and Conservation Sciences,

College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA; 4Department of Botany,

Stockholm University, SE-106 91 Stockholm, Sweden; 5Biology Department, Washington University in St. Louis, St.

Louis, MO 63130, USA; 6CSIRO Entomology, Indooroopilly, Qld 4068, Australia; 7Section of Ecology, University of

Turku, 20014 Turku, Finland; 8School of Biological Sciences, The University of Sydney, Sydney, NSW 2006, Australia;

and 9CSIRO Sustainable Ecosystems, 306 Carmody Rd, St Lucia, Qld 4067, Australia

Summary

1. A primary goal of evolutionary ecology is to understand factors selecting for the diversity of life

histories. Life-history components, such as time-to-reproduction, adult survivorship and fecundity,

might differ among species because of variation in direct and indirect benefits of these life histories

in different environments or might have lower-than-expected variability because of phylogenetic

constraints. Here, we present a phylogenetic examination of demography and life histories using a

data base of 204 terrestrial plant species.

2. Overall, statistical models without phylogeny were preferred to models with phylogeny for vital

rates and elasticities, suggesting that they lacked phylogenetic signal and are evolutionarily labile.

However, the effect of phylogeny was significant in models including sensitivities, suggesting that

sensitivities exhibit greater phylogenetic signal than vital rates or elasticities.

3. Species with a greater age at first reproduction had lower fecundity, consistent with a cost of

delayed reproduction, but only in some habitats (e.g. grassland). We found no evidence for an indi-

rect benefit of delayed reproduction via a decrease in variation in fecundity with age to first repro-

duction.

4. The greater sensitivity and lower variation in survival than in fecundity was consistent with buf-

fering of more important vital rates, as others have also found. This suggests that studies of life-his-

tory evolution should include survival, rather than only fecundity, for themajority of species.

5. Synthesis. Demographic matrix models can provide informative tests of life-history theory

because of their shared construction and outputs and their widespread use among plant ecologists.

Our comparative analysis suggested that there is a cost of delayed reproduction and that more

important vital rates exhibit lower variability. The absolute importance of vital rates to population

growth rates (sensitivities) exhibited phylogenetic signal, suggesting that a thorough understanding

of life-history evolution might require an understanding of the importance of vital rates, not just

their means, and the role of phylogenetic history.

Key-words: buffering, delayed reproduction, evolution, life history, matrix population models,

phylogenetic signal, phylogeny, plant demography, projection matrices

Introduction

A primary goal of evolutionary ecology is to explain the diver-

sity of life histories observed in nature (Cole 1954; Reznick*Correspondence author. E-mail: [email protected]

Journal of Ecology 2010, 98, 334–344 doi: 10.1111/j.1365-2745.2009.01634.x

� 2010 The Authors. Journal compilation � 2010 British Ecological Society

1985; Stearns 1989; Young 1990; Stearns 1992; Silvertown

1996; Roff 2002; Lesica & Young 2005; Rees et al. 2006). By

explicitly incorporating life-history traits, demographic matrix

models provide a powerful analytical tool (Metcalf & Pavard

2006). Variation in life histories is the result of differences in

selection caused by differences in the surrounding environment

and may be constrained by trade-offs between different vital

rates, rates that describe the movement of individuals through

the life cycle such as survival and fecundity (Caswell 2001), and

by phylogeny. To date, the extent of phylogenetic constraints

on life history across terrestrial plants has not been quantified

(Partridge &Harvey 1988; but see Franco & Silvertown 1997).

Recent advances in the estimation of plant phylogeny (Stevens

2009), including a much broader sampling of the plant phylog-

eny, and the addition ofmany demographic studies to the liter-

ature since recent reviews (e.g. Franco & Silvertown 2004),

including more multiyear studies, have made it possible to

study life-history evolution in a broader and more explicitly

phylogenetic perspective than has previously been the case.

Here, we present an examination of plant demographic and

life-history evolution in the context of a phylogeny.

Demographic data provide a basis for testing some predic-

tions of life-history theory. Demographic matrix models quan-

tify important life-history traits, such as fecundity and

survival, and permit the relatively easy calculation of other life-

history traits, such as the age at first reproduction. There have

been a number of reviews of plant demography, although they

have rarely been used to test life-history hypotheses in a phylo-

genetic context (e.g. Silvertown et al. 1993; Harper, Silvertown

& Franco 1996; Franco & Silvertown 2004; Morris et al. 2008;

but see Franco& Silvertown 1997).

One of the major life-history challenges facing any organism

is the optimal allocation of resources to reproduction vs. to

other functions such as growth and survival (e.g. Reekie &

Bazzaz 2005). Understanding the evolution of time-to-repro-

duction is a key area of life-history research, and both direct

and indirect selection pressures could select for delayed repro-

duction (e.g. Cole 1954; Murphy 1968; Schaffer 1974; Orzack

& Tuljapurkar 1989; Tuljapurkar 1990; Wilbur & Rudolf

2006; Metcalf et al. 2008). Delayed reproduction may be a

result of direct benefits, such as increased fecundity with

greater age or size at reproduction (Metcalf, Rose & Rees

2003), or might result in a cost because of the delay in time-

to-reproduction (Bell 1980; Reznick 1985). Alternatively,

increasing the age at first reproduction might confer indirect

benefits by decreasing variance in reproduction (e.g. Murphy

1968; Schaffer 1974; Orzack & Tuljapurkar 1989; Tuljapurkar

1990; Wilbur & Rudolf 2006; Koons, Metcalf & Tuljapurkar

2008;Metcalf et al. 2008).

This indirect benefit of delayed reproduction, via temporal

variability, might select for delayed reproduction either in spe-

cies with high mean survival or in species with low mean sur-

vival (Koons, Metcalf & Tuljapurkar 2008). If fecundity varies

more over time than survival, then the theory predicts that

species with high survival will evolve delayed reproduction

(Koons, Metcalf & Tuljapurkar 2008). In contrast, if survival

varies more over time than fecundity, then species with low

survival will evolve delayed reproduction (Koons, Metcalf &

Tuljapurkar 2008). We use demographic matrix models to test

for direct and indirect costs or benefits of delayed reproduction

on total fecundity or variation in fecundity.

In addition to exploring timing of reproduction, we use

demographic models to ask whether environmental variation

selects for ‘buffering’ in more important vital rates (e.g. sur-

vival, fecundity). Demographic analyses allow us to determine

which vital rates have a disproportionately large influence on

population growth rate, i.e. vital rates with large sensitivities or

elasticities (de Kroon et al. 1986; de Kroon, van Groenendael

& Ehrlen 2000; but see Salguero-Gomez & Casper 2010). The

sensitivities of vital rates describe the effect of small additive

changes in that vital rate to the projected population growth

rate, whereas the elasticities of vital rates describe the effect

of small proportional changes to the population growth rate

(Caswell 2001), and sensitivities and elasticities can be inter-

preted as the absolute and relative, respectively, importance of

a change in that vital rate to the population growth rate

(de Kroon et al. 1986; de Kroon, van Groenendael & Ehrlen

2000). Vital rates with higher sensitivities and elasticities might

be expected to be under strong selection and therefore exhibit

lower variability than vital rates with smaller effects on popula-

tion growth rate (Pfister 1998;Morris &Doak 2004;Metcalf &

Pavard 2006). We test the hypothesis that there is a negative

correlation across species between the sensitivities and elastici-

ties of vital rates and their coefficient of variation (CV) across

years.

Incorporating phylogeny into an among-species test of buf-

fering is important for statistical and biological reasons. Incor-

porating phylogeny in comparative tests will maintain

acceptable type I error rates (e.g. Ackerly 2000; Martins,

Diniz-Filho & Housworth 2000). Further, if closely related

species have similar geographic distributions, then they might

share environments. For example, many species have highly

variable recruitment in desert environments, so we might

expect these species to buffer variable recruitment with high

survival. If desert species also occur in particular clades, then

each clade, not each species, represents an independent test of

the evolution of this relationship.

To evaluate evolutionary life-history hypotheses, we use a

demographic data base of 204 plant species and their corre-

sponding phylogeny (Davies et al. 2004; Webb & Donoghue

2005; Stevens 2009). We evaluate three life-history hypotheses

in a demographic and phylogenetic framework: (i) do life

histories, including vital rates and their sensitivities and elastic-

ities, exhibit a phylogenetic signal, (ii) does delayed reproduc-

tion bring costs or benefits in terms of mean fecundity and

variation in fecundity, and (iii) do vital rates with large sensitiv-

ities or elasticities vary less over time than vital rates with small

sensitivities or elasticities?

Materials and methods

A literature survey was conducted to examine studies presenting

demographic stage-, size-, or age-based models in matrix form

(Caswell 2001). The data bases employed in Ehrlen & Lehtila

Plant demography, phylogeny and life history 335

� 2010 The Authors. Journal compilation � 2010 British Ecological Society, Journal of Ecology, 98, 334–344

(2002) and Ramula et al. (2008) were combined and substantially

updated for this study. We included suitable studies published up

to 2007, along with some unpublished studies to which we had

access (see Table S1, Supporting information). Studies were

included in our analysis if (i) they presented demographic matrix

models, (ii) they were conducted on terrestrial plants, and (iii)

vital rates could be determined from the publication. Vital rates

could not be determined when the process the original authors

used to calculate the matrix was unclear; e.g., when both clonal

reproduction and retrogression were present, and the authors did

not present a matrix including the matrix element calculations

from the vital rates. We calculated conditional vital rates, which

are mathematically independent of one another, as a way to test

for correlations among vital rates and to avoid mathematical arte-

facts (see details of calculations below). We summarized matrix

models for 204 terrestrial plant species, 185 of which are iterop-

arous and 19 of which are semelparous.

We classified species by life form (e.g. herbaceous, shrub, tree) and

habitat by type (i.e. bog, forest, grassland, heathland,multiple, rocky,

mesic, sanddunes, savanna, scrub, shrubland, wetlands) according to

the information present in the original study. A classification of ‘mul-

tiple’ habitats indicates that the species was found in more than one

habitat type (e.g. was found in both forest and grassland).We present

results where habitat or life formwas significant covariate in themod-

els (see details ofmodel selection below).

DEMOGRAPHIC PARAMETERS

Populationmatrices were analysed using standardmethods (Cochran

& Ellner 1992; Caswell 2001), implemented within a custom-built

MATLAB program (version 7.4; The MathWorks, Inc., Natick,

MA,USA).

Age at first reproduction was calculated for sexual reproduction

(mean age at maturity, Cochran & Ellner’s 1992, eq. 15), including all

matrix stages. Ages refer to ramets or clonal fragments as defined by

the original studies (Ehrlen & Lehtila 2002). To calculate age at first

reproduction, reproductive stages in the matrix models were defined

separately for each population within a species. Reproductive stages

are those in which some of the plants in a population reproduced sex-

ually in any year. Where the incorporation of a seed bank led to an

incorrect 1-year delay in the life cycle, the stage was removed and the

remaining vital rates corrected before analysis was carried out (Silver-

town et al. 1993; Caswell 2001).

We also calculated conditional vital rates, such as fecundity and sur-

vival, and their sensitivities and elasticities.Conditional vital rates (see

methods below), which are not mathematically constrained, are used

to calculate the matrix elements (e.g. Morris & Doak 2004; Lesica &

Crone 2007). Correlations among vital rates therefore reflect the true

life-history relationships rather than spurious relationships that may

occur for correlations among matrix elements (Mesterton-Gibbons

1993; Silvertown et al. 1993; Shea, Rees &Wood 1994; van Tienderen

1995; Morris & Doak 2004). Given a demographic matrix with an

accurate description of the position of each element, it is possible to

calculate the values of the vital rates implicit in each cell of the matrix

(Caswell 2001; Franco& Silvertown 2004).We calculated conditional

vital rates for growth conditioned on survival and also conditioned

on growth to other stages, where relevant. We calculated retrogres-

sion conditioned on survival and not growing and also conditioned

on retrogression to other stages, where relevant. For example, matrix

A (below) is a 3 · 3 matrix with three reproductive stages (1–3). The

matrix form differed among species; this example is based on

the matrix for Aristida bipartita, where individuals reproduce from

classes 1, 2 and 3 into class 1 (following terminology used in Franco&

Silvertown 2004; see Appendix S1, for example MATLAB code

for calculating the conditional vital rates and their elasticities for this

species).

rj = survival of stage class j.

cj = the probability of growth from stage class j, conditional on sur-

vival at j (and growing to other stages classes, if growing more than

one stage class).

qj = the probability of retrogression from stage class j, conditional

on surviving and not growing (and retrogression to other stage clas-

ses, if revertingmore than one stage class).

gi,j = probability that an individual from class j that reverts does so

by i size classes, given that there is reversion (necessary when it is pos-

sible to regress to more than one stage class; corresponds toMorris &

Doak’s 2005 ‘k’).

/j = number of offspring produced in a year by an individual of

stage class j.

A ¼/1 þ r1ð1� c1Þ /2r2ð1� c2Þq2 /3 þ r3q3g13

r1c1 r2ð1� c2Þð1� q2Þ r3q3ð1� g13Þ0 r2c2 r3ð1� q3Þ

0@

1A:

For matrix elements that are not restricted and obviously correlated

to other elements, such as clonal reproduction and fecundity, and

where it was not possible to calculate vital rates, we present thematrix

elements and their sensitivities and elasticities. Because populations

are not necessarily evenly distributed among stage classes, to calculate

total fecundity, we summed fecundity vital rates for all stages,

weighted by the normalized stable stage distribution. The conditional

vital rates can be analysed without the possibility of mathematical ar-

tefacts driving correlations among them.

We also calculated sensitivities and elasticities for the underlying

vital rates (Caswell 2001; Franco & Silvertown 2004, Appendix S1).

Vital rate sensitivities and elasticities for survival (including stasis),

growth, retrogression, fecundity (sexual reproduction) and clonal

(asexual) reproduction were summed over all stages, excluding the

seed bank, to determine whether there is evidence for buffering out-

side the seed bank. We also calculated alternative estimates including

the seed bank, and found no evidence that including or excluding the

seed bank influenced the results, and so we present the set of analyses

without the seed bank.

Many of the studies includedmatrices for more than 1 year and ⁄ ormore than one population. Estimates of age to first reproduction and

sensitivities and elasticities of conditional vital rates were made using

the mean matrix for each species (Franco & Silvertown 2004). Mean

matrices were used to avoid infinite life spans caused by zero mortal-

ity in some annual matrices and to minimize the effect of fluctuations

in vital rates caused by varying sampling effort within a species.

Where the original study used experimental treatments (e.g. exclo-

sures forHaplopappus radiatus, Kaye & Pyke 2003), we used only the

non-experimental (e.g. control) demographic matrices to examine

life-history hypotheses under naturally occurring environmental con-

ditions (e.g. control for Haplopappus radiatus, Kaye & Pyke 2003;

core range forUlex gallii, Stokes et al. 2004). In four cases, there were

multiple matrices for a species of different dimensionality (i.e.Hypo-

chaeris radicata, Lomatium bradshawii, Neobuxbaumia tetetzo, Prim-

ula vulgaris). In these cases, the sensitivities and elasticities of the vital

rates were calculated for the different sizes of matrices separately and

then averaged.

Because temporal variation in survival and fecundity are predicted

to influence life-history evolution, for each population and species we

calculated the CV, the standard deviation divided by the mean, for

336 J. H. Burns et al.


these vital rates (survival = weighted post-seedling survival, with the

first non-seedling stage excluded; fecundity = number of offspring

that enter all stages), to address hypotheses about the role of temporal

variation in life history (e.g. Pfister 1998; Morris & Doak 2004;

Koons, Metcalf & Tuljapurkar 2008). To estimate temporal varia-

tion, CVs were calculated across sampling times for a given popula-

tion, within a single study, and averaged across populations to yield a

single estimate for each species. The CV was calculated with bias

correction for small sample sizes (eqn 1, Sokal &Rohlf 1995).

CV ¼ 1þ 1

4n

� �rl: eqn 1

Because the CV for vital rates, like survival, which are constrained

to be between 0 and 1, can be spuriously correlated with their elastici-

ties or sensitivities, we corrected the CV for survival followingMorris

&Doak (2004) (eqn 2), where

CVcorrected ¼ CV

CVmax; eqn 2

CVmax ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1� pð Þp

� �s; eqn 3

and p is the mean survival (eqn 3). Such correction is not neces-

sary for unconstrained vital rates, such as fecundity (Morris &

Doak 2004). The CV over time for fecundity could be calculated

for 57 species and the CV over time for survival could be calcu-

lated for 64 species; both estimates were available for 56 species.

Species for which the original matrix was already a mean matrix

across years or populations were excluded from calculation of

variance in demographic parameters.

DATA ANALYSIS

To analyse the evolution of life-history traits, we generated a phylog-

eny for the species for which we had demographic data (Fig. S1). We

used the plant phylogeny available via phylomatic (reference tree #

R20050610), which references the angiosperm phylogeny website, the

most recent, and constantly updated, summary of plant phylogenies

available (phylomatic version 2, Webb & Donoghue 2005; Webb,

Ackerly & Kembel 2008; Stevens 2009). This typology was then

assigned branch lengths based on fossil calibration following

Wikstrom, Savolainen & Chase (2001) using the bladj command in

phylocom (version 4.0.1b; Webb, Ackerly &Kembel 2008). The bladj

command fixes the nodes for which there is a fossil calibration, then

sets the other branch lengths by placing them evenly between dated

nodes (Webb, Ackerly & Kembel 2008); branch lengths are thus in

units of approximate millions of years. This phylogeny contained

polytomies (36 of 335 nodes, or 10.7% of nodes), which most proba-

bly represent uncertainty in our estimation of the phylogeny.We used

this phylogeny to generate a variance–covariance matrix for use in

phylogenetic generalized least-squares (PGLS) analysis (below,

Martins &Hansen 1997).

We then conducted (P)GLS analyses to determine relationships

among life-history traits. We compared models with and without

phylogeny (see below) and with and without matrix dimensionality

using the model selection criteria Akaike information criterion (AIC)

(Burnham & Anderson 2002) and present here the optimal model.

We conducted analyses with matrix dimensionality as a covariate in

the model, where the model with matrix dimensionality had a higher

likelihood (see below, cf. Salguero-Gomez & Casper 2010). The

demographic matrices analysed in this study varied in matrix dimen-

sionality (from 2 to 19 dimensions, 6.5±3.1 SD), and matrix dimen-

sionality is known to affect the estimation of elasticities (e.g. Enright,

Franco& Silvertown 1995). Our use of the vital rate elasticities, rather

than the matrix element elasticities, should be more robust to error

induced by matrix dimensionality (Franco & Silvertown 2004) or by

variability within a matrix in the duration of each stage (Wardle

1998). Matrix dimensionality most probably also correlates with

meaningful characteristics of the life history of the organism, assum-

ing that biologists choose larger dimensionalities for longer-lived

species. Therefore, analyses that control for matrix dimensionality

should be interpreted with caution, as they might ‘control’ for some

of this meaningful variation. We speculate that we would have a

lower probability of seeking an effect of phylogeny when controlling

for matrix dimensionality, if closely related species are more likely to

have similar life spans, and thus similar matrix dimensionality. Thus,

we expect that the phylogenetic signal might be underestimated when

matrix dimensionality matters. Note, however, that matrix dimen-

sionality is rarely important in these analyses; the model without

matrix dimensionality most often has a higher likelihood than the

correspondingmodel with matrix dimensionality (Tables S2–S9).

We used PGLS to analyse the relationships among variables, while

including phylogeny as part of the error structure of the model (Mar-

tins & Hansen 1997). The PGLS models are an extension of general-

ized least-square models with phylogeny included in the error

structure of the model as a variance–covariance matrix (Martins &

Hansen 1997). The PGLS has several advantages in this context,

including the ability to include phylogeny in the model, allow for

polytomies and include covariates (i.e. matrix dimensionality) in the

model. Different models of evolution could be used to construct this

variance–covariance matrix (Paradis 2006), and we used the model

selection criteria AIC (Burnham & Anderson 2002) to select the

appropriate model. Preliminary comparisons suggested that Brown-

ian motion models (using corBrownian, Paradis 2006) resulted in

lower model AIC than O-U (corMartins, Martins &Hansen 1997) or

Grafen (corGrafen, Grafen 1989) models (in some cases by DAIC =

�50), therefore the variance–covariance matrices were constructed

assuming a Brownian motion model of evolution. Simulations sug-

gest that the PGLS has expected type I error rates (about 5%) and

increasing power with increasing sample sizes, comparable with the

power of phylogenetic independent contrasts (Martins, Diniz-Filho

& Housworth 2000). All (P)GLS analyses were conducted in R (R

2.8.1; The R Foundation for Statistical Computing 2004–2008) with

the nlme and ape packages and the gls function (Paradis, Claude &

Strimmer 2004; Paradis 2006).

We tested several hypotheses using (P)GLS. To determine whether

vital rates with a larger influence on population growth rate are more

constrained across years or populations, we correlated the CV for sur-

vival and fecundity with their elasticities and sensitivities using

(P)GLS. To examine the hypothesis that there is either a direct benefit

of delayed reproduction (e.g. Metcalf, Rose & Rees 2003) or a cost

(e.g. Reznick 1985), we correlated age at first reproduction with total

fecundity, and to test for an indirect benefit of delayed reproduction,

we correlated the CV of fecundity with the age at first reproduction.

To compare the fits of the (P)GLSmodels, we compared models with

and without phylogeny, and with and without matrix dimensionality,

using likelihood ratio tests against a v2 distributionwith 1 d.f.We also

tested for the effects of potential covariates, life form and habitat, by

model deletion, using likelihood ratio tests against a v2 distributionwith 1 d.f. We dropped non-significant (P > 0.10) covariates from

the model. The (P)GLS analyses were conducted on ln-transformed

variables, with a small constant added where necessary. We report

results of likelihood ratio tests for significance of each effect for the

(P)GLS results.



Results

IS THERE A PHYLOGENETIC SIGNAL ON LIFE-HISTORY

TRAITS?

Most models did not benefit from including phylogeny in the

error structure (Table 1; Tables S6–S9); however, those that

did suggest phylogenetic conservation in sensitivities, but not

in the corresponding vital rates or their elasticities (Table 1).

For analyses among vital rates, the best-fit model was always

either the non-phylogenetic (i.e. TIPS) analysis or there was

not a significantly bettermodel fit with phylogeny thanwithout

(NA). Relationships among elasticities were always best fit

with models not including phylogeny (Table 1). However,

TIPS analyses were rarely preferred formodels of relationships

among sensitivities, and models with phylogeny (PGLS) were

preferred in 7 of 10 suchmodels (Table 1).

The best-fit model for the relationship between age at first

reproduction and total fecundity was the model without phy-

logeny (v2 = 11.38, P < 0.01, Table S6). The best-fit model

for the relationship between age at first reproduction and the

CV of fecundity was the model with phylogeny, although this

model was not a significantly better fit than the model without

phylogeny (v2 = 1.6, P > 0.05, Table S6). Overall, phylog-

eny was only informative in models of relationships with sensi-

tivities, not inmodels with vital rates, elasticities or variation in

vital rates (CV) (Table 1).

ARE THERE COSTS OR BENEFITS OF DELAYED

REPRODUCTION?

To determine whether there was a direct cost or benefit of

delayed reproduction, we correlated the age at first reproduc-

tion with total fecundity (fecundity into both the seed bank

and seedling stages). We compared models with life form or

habitat as covariates for this relationship.Habitat was a signifi-

cant covariate in the model with total fecundity as a function

of age of first reproduction (Table 2); life form was not signifi-

cant and was dropped from the model (P > 0.10). There was

a significant interaction between habitat and age at first repro-

duction (Table S10), although it was not possible to test this

interaction in theGLS (Table 2), and there was a negative rela-

tionship between age at first reproduction and total fecundity

for grasslands, savannas and for species occurring across mul-

tiple habitat types (e.g. forests and grasslands) (Fig. 1). The

slope of the relationship between total fecundity and age at first

reproduction was positive (although not significantly so) for

deserts, forests and shrublands (Fig. 1).

Table 1. Summary of the likelihood-ratio tests comparing models

with and without phylogeny (phylogenetic generalized least-squares

(PGLS) compared with non-phylogenetic GLS (TIPS)). Summarized

are the proportion of tests where including phylogeny improved the

fit of the model after each type of parameter (e.g. Vital rates,

0 ⁄ 10 = 0 of the 10 preferred models included phylogeny). Both

model types (TIPS, PGSL) included matrix dimensionality as a

covariate

Independent Dependent

v2

(likelihood-

ratio test)

Model

supported

Vital rates (0 ⁄ 10)Survival Retrogression )0.2 NA

Survival Growth )9.9* TIPS†

Survival Fecundity )39.2* TIPS

Survival Clonal reproduction 1.6 NA

Retrogression Growth )34.5* TIPS

Retrogression Fecundity )20.1* TIPS

Retrogression Clonal reproduction )2.6 NA

Growth Fecundity )33.0* TIPS

Growth Clonal reproduction 1.3 NA

Fecundity Clonal reproduction 2.9 NA

Elasticities (0 ⁄ 10)Survival Retrogression )17.9* TIPS

Survival Growth )35.0* TIPS

Survival Fecundity )46.7* TIPS

Survival Clonal reproduction )7.6* TIPS

Retrogression Growth )10.9* TIPS


Retrogression Clonal reproduction )12.7* TIPS

Growth Fecundity )27.3* TIPS

Growth Clonal reproduction )5.2* TIPS

Fecundity Clonal reproduction )7.5* TIPS

Sensitivities (7 ⁄ 10)Survival Retrogression )43.1* TIPS

Survival Growth 9.9* PGLS

Survival Fecundity 110.3* PGLS

Survival Clonal reproduction 8.9* PGLS

Retrogression Growth 2.4 NA


Retrogression Clonal reproduction 5.6* PGLS

Growth Fecundity 111.1* PGLS

Growth Clonal reproduction 6.5* PGLS

Fecundity Clonal reproduction 6.6* PGLS

*P < 0.05. †TIPS, analysing the species on the tips of the

branches, independent of the phylogeny so that the supported

model does not contain phylogeny; PGLS, supported model is

the phylogenetic generalized least-squares; NA, no significant dif-

ference between models.

Table 2. Tests for direct or indirect costs or benefits of delayed

reproduction across demographic studies in plant species. Tests

summarize the effects of deletion from the (phylogenetic) generalized

least-squares ((P)GLS)model

Source v2 P-value

Total fecundity*

ln (age at first reproduction) 8.51 <0.05

Habitat 25.73 <0.05

Total fecundity

ln (CV age at first reproduction) 2.06 >0.15

Habitat 24.32 <0.05

CV of fecundity

ln (age at first reproduction) 1.61 >0.20

Life form 8.41 <0.10

CV of fecundity

ln (CV age at first reproduction) 1.73 >0.15

*See Table S10 for ancova analysis with interaction between habi-

tat and age of first reproduction. CV, coefficient of variation.



To determinewhether there was evidence for an indirect cost

or benefit of delayed reproduction, we correlated the variation

in fecundity with age at first reproduction, where a negative

correlation would imply that greater age at first reproduction

buffers against a temporal CV in fecundity. There was no cor-

relation between age at first reproduction and a temporal CV

in fecundity (Table 2). Life formwas not a significant covariate

inmodels predicting the CV in fecundity with age at first repro-

duction (v2 = 8.41, P < 0.10). Results for the model predict-

ing CV in fecundity with age at first reproduction were

consistent when we removed the outlier Tsuga canadensis

(results not shown).

To determine whether high- or low-survival life histories

exhibit delayed reproduction, we correlated the age at first

reproduction with the elasticities of survival and fecundity

(Koons, Metcalf & Tuljapurkar 2008). Age at first reproduc-

tion was positively correlated with the elasticity of survival

(slope = 0.04, P < 0.01). The age at first reproduction was

negatively correlated with the elasticity of fecundity (slope =

)0.02,P < 0.05).

DO VITAL RATES WITH LARGE SENSIT IV IT IES OR

ELASTIC IT IES VARY LESS OVER TIME THAN VITAL

RATES WITH SMALL SENSIT IV IT IES OR ELASTIC IT IES?

There was support for the buffering hypothesis across vital

rates. Fecundity was much more variable across years and

populations than was survival for 52 of 56 species (Figs 2, S2),

while the sensitivities and elasticities for survival were larger

than those for fecundity (Fig. 2; seeCaswell 2001; vanTienderen

2000 for discussion of relative merits of sensitivity and

elasticity as measures of importance for fitness). However,

there was no support for the buffering hypothesis within

either survival or fecundity. The CV for survival was not

significantly correlated with the sensitivity or elasticity of

survival (Fig. 3, Table 3); the CV for fecundity was not

correlated with the sensitivity or elasticity for fecundity

(Fig. 3, Table 3). The effects of habitat and life form were

not significant in most of these models, except for the

relationship between CV of survival and the sensitivity of

survival, for which life form was a significant covariate

(Table 3). There was no significant interaction between

life form and sensitivity of survival (Table 3).

Discussion

VITAL RATES ARE EVOLUTIONARILY LABILE , AND

THEIR SENSIT IV IT IES EXHIB IT GREATER

PHYLOGENETIC SIGNAL

Including phylogeny in the models with vital rates and elastici-

ties did not improve themodel fit, compared withmodels with-

out phylogeny (Table 1; Tables S6–S9). This suggests that

many of these demographic parameters are highly evolution-

arily labile and that phylogeny explains little of the variation in

these parameters. As life histories are notoriously evolution-

arily labile in many taxa (e.g. Reznick & Endler 1982; Baker

et al. 2008; Treier et al. 2009), this is perhaps not surprising.

0 1 2 3 4 5

–10

–50

5 Desert Forest Grassland Savanna Shrubland Multiple

Fig. 1. There was evidence for a cost of delayed reproduction, where

species that had a later age at first reproduction had lower total fecun-

dity, but the slope was negative only in grasslands and savannas and

for species occurring across multiple habitat types (e.g. forests and

grasslands), and was positive in deserts, forests and shrublands

(Table 2). See Table S10 for an ancova demonstrating an interaction

between habitat and age at first reproduction (adjusted R2 = 0.09).

Each point represents a single species; data were ln-transformed.

0.0

0.2

0.4

0.6

0.8

1.0

Fecundity Survival

Res

pons

e (C

V, e

last

icity

)

CV Elasticity(a)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Fecundity Survival

Res

pons

e (C

V, s

ensi

tiviti

es) CV Sensitivity

(b)

Fig. 2. (a) The coefficient of variation across years was greater for

fecundity than for survival, and the elasticity of survival was greater

than the elasticity for fecundity, consistent with the buffering hypoth-

esis. (b) Sensitivities for fecundity and survival have a similar pattern

to the elasticities.Means±SE across species.



Improved resolution of the phylogeny at a finer scale might yet

reveal a phylogenetic signal on vital rates or elasticities (cf.

Datson, Murray & Steiner 2008; Tank & Olmstead 2008), but

it is reasonable to conclude that these effects would be rela-

tively small.

Models including sensitivities did benefit from the inclusion

of phylogeny (Table 1; Tables S6–S9). Thus, there is a phylo-

genetic signal on sensitivities, and including phylogeny in these

cases yields a more accurate answer about correlations among

sensitivities. This result suggests that the absolute importance

of vital rates (sensitivities) might be less evolutionarily labile

than the vital rates or their elasticities. The phylogenetic signal

on sensitivities is consistent with the taxonomic conservation

of transient demographic dynamics in other studies (Stott et al.

2010), and these observations together suggest that aspects of

plant demography (i.e. sensitivities, transient dynamics) may

have a greater phylogenetic signal than vital rates, such as

fecundity or survival. For example, closely related species

might share sensitivities, if absolute changes in survival have

large effects on population growth rate, as in long-lived peren-

nials. Demographers studying life-history evolutionmight ben-

efit from paired comparisons of close relatives, especially for

questions where the sensitivities are relevant, such as in studies

of buffering (e.g. Pfister 1998).

This result begs the question of what mechanisms might

drive the phylogenetic signal in sensitivities to be greater than

in their corresponding vital rates. It is of course possible that

this apparent phylogenetic signal is spurious; however, models

with phylogeny provided a better fit in 7 of 10models with sen-

sitivities, and in no models including vital rates (Table 1), sug-

gesting a strong pattern. It is also possible that demographic

strategies could exhibit a greater phylogenetic signal than vital

rates, if there is a phylogenetic signal on the covariance among

vital rates. That is, closely related species might have similar

demographic strategies, e.g. long-lived perennials might be

more closely related than expected by chance and might share

a similar pattern of sensitivities, with relatively high sensitivi-

ties for survival and low sensitivities for fecundity, even if their

survival and fecundity vital rates vary considerably among spe-

cies. Future comparative studies of demography should keep

inmind the need to incorporate phylogeny.

The apparently lower phylogenetic signal on elasticities than

on sensitivities suggests the effects of relative changes in vital

rates are more labile evolutionarily than the effects of absolute

changes. Because sensitivities measure the effects of vital rate

changes on an absolute scale while elasticities measure the

effects on a mean-standardized scale, this difference is a likely

result of mean-standardization removing part of the phyloge-

netic signal. As an example, onemay imagine two plant genera,

one that produces a very large number of small seeds each with

a small probability of recruitment and the other producing a

smaller number of larger seeds with a higher probability of

0. 0 0. 5 1. 0 1.5 2. 0 2.5 3. 0

–4

–2

0

0. 0 0. 2 0. 4 0. 6 0. 8

–6

–2

2

0.5 1.0 1.5 2.0 2.5 3.0

–4–2

0

0.6 0.7 0.8 0.5 1.0 1.1 1.2

–6–2

2

Fig. 3. There was no correlation between the coefficient of variation (CV) across years and the importance (sensitivities or elasticities) of vital

rates across plant species (Table 3), inconsistent with the buffering hypothesis. The CV was corrected for sampling bias and for maximum CV,

followingMorris &Doak (2004). Each point represents a single species; datawere ln-transformed. The point with the small value of CV fecundity

is Tsuga canadensis (Pinaceae). Analyses were not significant without this species and removal of other potential outliers (top panels) also did not

influence the results.

Table 3. Test for the buffering hypotheses for vital rates across

demographic studies of plant species. Relationships between CV and

sensitivities and elasticities for fecundity and survival resulting from

(phylogenetic) generalized least-squares ((P)GLS) analyses

Source v2 P-value

CV of fecundity*

Sensitivity of fecundity 3.27 <0.10

CV of fecundity*

Elasticity of fecundity 0.51 >0.45

CV of survival†

Sensitivity of survival 1.18 >0.25

Life form 11.75 <0.05

CV of survival*

Elasticity of survival 0.33 >0.55

Visual inspection of the plots suggested that quantile regression

was not necessary (Fig. 3, cf. Fig. S2, Morris & Doak 2004).

Alternative calculations of elasticities and sensitivities that include

seed bank did not change the results. *Life form and habitat were

not significant as covariates (P > 0.05). †See Fig. S3 for graphs

illustrating these relationships. CV, coefficient of variation.



recruitment. Given that the relative importance of recruitment

to the population does not vary systematically between the two

genera, elasticities will not vary because proportional changes

in seed production will have similar effects in both genera, but

sensitivities will vary because absolute increases in seed number

will have very different effects. Given some environmentally

driven selection at the species level, we may thus expect the

phylogenetic signal to be more easily detected for sensitivities

than for elasticities.

THERE IS EV IDENCE FOR A DIRECT COST OF DELAYED

REPRODUCTION

We found evidence for a cost of delayed reproduction, where

species with a later age at first reproduction have lower total

fecundity. This is inconsistent with some within-species stud-

ies that have found evidence for a direct benefit of delayed

reproduction (e.g. Klinkhamer & de Jong 1987; reviewed in

Metcalf, Rose & Rees 2003). In contrast, the age at first

reproduction does not explain much of the variance in total

fecundity (about 9%), and there are multiple strategies that

can result in similar fecundities, suggesting that many repro-

ductive strategies might be equally beneficial (or costly). The

direct cost of delayed reproduction only holds for some habi-

tats, such as grasslands, and there is a positive trend for

some other habitats, such as forests (Fig. 1). This suggests

that the relative costs or benefits of delayed reproduction are

environment-dependent. For example, we would expect high-

and size-independent adult mortality to select for earlier

reproduction and low- and size-dependent adult mortality to

select for delayed reproduction.

An indirect benefit of delayed reproduction, as a result of

environmental uncertainty (Wilbur & Rudolf 2006), could

result in greater delayed reproduction in high- or low-survival

species, depending on whether survival or fecundity is more

variable (Koons, Metcalf & Tuljapurkar 2008). We found that

age at first reproductionwas positively correlatedwith the elas-

ticity of survival and negatively correlated with the elasticity

of fecundity, consistent with the prediction that high-survival

species will evolve delayed reproduction. If high variation in

fecundity is a common mechanism that selects for earlier

reproduction, then we expect to see high variance in fecundity

across sampling times (Koons, Metcalf & Tuljapurkar 2008).

We found evidence consistent with this hypothesis, where the

CV was higher for fecundity than for survival for the majority

of species (Fig. 2). However, we found little evidence that

reproducing at a later age decreases the variance in fecundity,

inconsistent with an indirect benefit of delayed reproduction

via an effect on variation in fecundity (Table 2). We suggest

that future field studies of the costs and benefits of delayed

reproduction might conduct such tests across different habitat

types, as habitat type may influence the relative importance of

direct or indirect benefits of delayed reproduction. The relative

benefit of delayed reproduction via decreases in temporal vari-

ability vs. increases in mean fecundity may be larger in tempo-

rally varying environments (such as deserts) than in more

stable environments (such as forests) (Brown&Venable 1986).

SURVIVAL IS MORE IMPORTANT, AND LESS VARIABLE,

THAN FECUNDITY

Several authors have suggested that environmental variation

should select for ‘buffering’ of life histories, or selection for

lower variability across years in more important vital rates

(e.g. Pfister 1998; Morris & Doak 2004; Metcalf & Pavard

2006). We found evidence consistent with these predictions

across vital rates, where there was a greater sensitivity and elas-

ticity for survival than for fecundity and a lower CV in survival

than for fecundity. This is consistent with selection for less var-

iation in more important life-history traits (Fig. 2, Morris &

Doak 2004;Metcalf & Pavard 2006).

Although the direction of the trends for the correlation

between the CV and the sensitivity for fecundity or survival

were negative in some cases, as predicted by the buffering

hypothesis, these relationships were not statistically significant

across species, within vital rates (Table 3, Fig. 3, Pfister 1998).

We also found no evidence for buffering in alternative analyses

including the seed bank. Thus, we found no evidence for buf-

fering within vital rates in the seed bank or non-seed bank

stages of the life cycle. The seed bank has long been hypothe-

sized to buffer populations against temporal variation, by low-

ering the variance in performance over time (e.g. Brown &

Venable 1986), although our results are not consistent with

adaptive variation in buffering by the seed bank across the spe-

cies in these studies.

The lack of a significant correlation between the CV and

sensitivities and elasticities may be partly the result of the rel-

atively short time series for calculating the CV (2–14 years,

mean = 4.5±3.6 SD). Also, others looked at all vital rates

pooled across taxa (e.g. Pfister 1998); thus their results could

be driven by within-species correlations between variation

and importance across vital rates. Among species, differences

in traits are confounded with differences in the environment;

some species live in more variable environments and others

live in less variable environments. The lack of a negative cor-

relation between sensitivity or elasticity and the CV across

species in our study may be because species that live in more

variable environments, but are not well adapted to them, are

simply not able to buffer environmental fluctuations, which

could add noise to the among-species comparison. However,

we found no evidence for habitat effects on the CV of fecun-

dity or survival, inconsistent with such a confounding effect

of habitat type (Table 2, Fig. S3). We found some evidence

for an effect of life form on CV of survival, where trees had

lower variation in survival over time than grasses (Fig. S3);

although we had insufficient power to test for an interaction

between life form and the sensitivity of survival on CV of

survival, there is some suggestion that shrubs have a more

negative relationship (greater buffering) than herbs, perhaps

consistent with the hypothesis that longer-lived species have

greater buffering (Fig. S3). However, testing this hypothesis

requires greater samples sizes. Alternatively, our lack of evi-

dence for buffering within vital rates could be a meaningful

biological pattern and is consistent with other studies (Jonge-

jans et al. 2010), suggesting that buffering may not occur



within vital rates, but is either more important or more

detectable among vital rates.

As others have pointed out, the observation that survival

has greater sensitivities and elasticities compared with fecun-

dity has serious conservation implications (Morris et al.

2008). This result, like others before it (e.g. Pfister 1998),

comes with the caveat that more important vital rates tend

to be less variable. The best measure of variance in popula-

tion growth rates is the product of natural variance in vital

rates and their sensitivities or elasticities. It is also possible

that we underestimate variance in survival, where some pop-

ulations go extinct and variance in survival cannot be sam-

pled. Nonetheless, this study and others (e.g. Pfister 1998;

Crone 2001) have found survival to be more important to

population growth rates, and thus it may be more important

to measure survival than fecundity as a single surrogate of

population growth rates to make predictions for conserva-

tion, such as predicting species responses to future climate

change (Metcalf & Pavard 2006).

Conclusions

Ours is one of the first studies of comparative plant demogra-

phy in a phylogenetic context (see also Franco & Silvertown

1997). Notably, our analyses reveal little signal of phylogeny

on vital rates, but a significant signal on the absolute impor-

tance (sensitivities) of those vital rates. At the same time, we

support classical hypotheses about timing of reproduction and

patterns of vital rate variation, over a broad range of species. It

is important to recognize that these tests are correlations and

that unmeasured variables might confound these correlations

(Partridge &Harvey 1988). Some of these correlations are con-

sistent with hypotheses about life-history evolution and sug-

gest hypotheses for further study. For example, while vital

rates, such as fecundity and survival, are evolutionarily labile,

their sensitivities might exhibit more phylogenetic signal, and

we encourage future comparative demographic studies testing

these hypotheses.

Acknowledgements

We are grateful to the authors who generously donated unpublished demo-

graphic matrices: C. Gustafsson, M. Hutchings, M. Milden, F. Nicole and B.

Tenhumberg. Thanks to K. S. Moriuchi, S. Price, T. P. Young, the Handling

Editor and two anonymous referees for helpful comments on the manuscript,

and H. Quested and P. Aronsson for Matlab codes. We also thank the ARC-

NZResearch Network for Vegetation Function for funding to the ‘Plant Popu-

lation Syndromes’ working group. J. H. B. thanks Tyson Research Center at

Washington University in St. Louis, the American Association of University

Women, and the Center for Population Biology, University of California,

Davis, for funding during the course of this project. Y. M. B. is funded by an

Australian Research Fellowship from the Australian Research Council

(DP0771387).

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Received 19August 2009; accepted 9 December 2009

Handling Editor: Roberto Salguero-Gomez

Supporting Information

Additional Supporting Information may be found in the online ver-

sion of this article:

Appendix S1. Example MatLab code for calculating the elasticities

and sensitivities of conditional vital rates for survival, growth and ret-

rogression using the Symbolic Toolbox forAristida bipartita.

Table S1. Species in this study and the references from which demo-

graphic matrices were extracted.

Table S2. Life-history traits associated with parity and time to repro-

duction.

Table S3.Correlations among the vital rates for 204 terrestrial plants,

with phylogeny in themodel.

Table S4. Correlations among the vital rate elasticities for 204 terres-

trial plants, with phylogeny in themodel.

Table S5.Correlations among the vital rate sensitivities for 204 terres-

trial plants, with phylogeny in themodel.

Table S6. GLS details for the relationships among reproduction (it-

eroparity, age at first reproduction) and life-history characteristics.

Table S7. Generalized least-squares details for the relationships

among vital rates.


among elasticities.


among sensitivities.



Table S10. ancova results for weighted total fecundity as a function of

habitat and age of first reproduction.

Figure S1. Phylogeny used in this study to compare the demography

of 204 terrestrial plant species.

Figure S2. Coefficients of variation (CV) for fecundity (into the first

non-seed and non-dormant stage) and for survival (excluding the seed

bank).

Figure S3. Though there was not a significant interaction between life

form and sensitivity of survival on CV of survival, the patterns do

suggest that different life formsmight have different CV of survival.

As a service to our authors and readers, this journal provides support-

ing information supplied by the authors. Such materials may be re-

organized for online delivery, but are not copy-edited or typeset.

Technical support issues arising from supporting information (other

thanmissing files) should be addressed to the authors.



Empirical tests of life-history evolution theory using phylogenetic analysis of plant demography

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