Diversity of ageing across the tree of life
Owen R. Jones
1,2,*
, Alexander Scheuerlein
3,*
, Roberto Salguero-Gómez
3,4
, Carlo Giovanni
Camarda
5
, Ralf Schaible
3
, Brenda B. Casper
6
, Johan P. Dahlgren
1,2
, Johan Ehrlén
7
, María
B. García
8
, Eric S. Menges
9
, Pedro F. Quintana-Ascencio
10
, Hal Caswell
2,3,11,12
, Annette
Baudisch
3
, and James W. Vaupel
1,3,13
1
Max-Planck Odense Center on the Biodemography of Aging, Campusvej 55, 5230 Odense M,
Denmark
2
Department of Biology, University of Southern Denmark, Campusvej 55, 5230 Odense
M, Denmark
3
Max Planck Institute for Demographic Research, Konrad-Zuse-Strasse 1, 18057
Rostock, Germany
4
School of Biological Sciences, Centre for Biodiversity and Conservation
Science, University of Queensland, Brisbane QLD 4072, Australia
5
Institut National d’Etudes
Démographiques, 133 Boulevard Davout, 75980 Paris Cédex 20, France
6
Department of Biology,
University of Pennsylvania, 433 South University Avenue, Philadelphia, Pennsylvania
19104-6018, USA
7
Department of Ecology, Environment and Plant Sciences, Stockholm
University, Lilla Frescativägen 5, 10691 Stockholm, Sweden
8
Pyrenean Institute of Ecology
(CSIC), Avenida Montañana 1005, 50059 Zaragoza, Spain
9
Archbold Biological Station, 123 Main
Drive, Venus, Florida 33960, USA
10
Department of Biology, University of Central Florida, 4110
Libra Drive, Orlando, Florida 32816-2368, USA
11
Woods Hole Oceanographic Institution, Biology
Department MS-34, Woods Hole, Massachusetts 02543 USA
12
Institute for Biodiversity and
Ecosystem Dynamics, University of Amsterdam, PO Box 94248, 1090GE Amsterdam, The
Netherlands
13
Duke Population Research Institute, Duke University, Durham, North Carolina
27705, USA
Abstract
Evolution drives, and is driven by, demography. A genotype moulds its phenotype’s age patterns
of mortality and fertility in an environment; these two patterns in turn determine the genotype’s
©2014 Macmillan Publishers Limited. All rights reserved
Correspondence and requests for materials should be addressed to O.R.J. (jones@biology.sdu.dk).
*
These authors contributed equally to this manuscript.
Online Content Any additional Methods, Extended Data display items and Source Data are available in the online version of the
paper; references unique to these sections appear only in the online paper.
Supplementary Information is available in the online version of the paper.
Author Contributions
This research project was initiated by J.W.V. A.S. wrote the first draft; O.R.J., with help from A.S., R.S.-G., H.C., A.B. and J.W.V.,
wrote subsequent drafts; J.W.V. and O.R.J. completed the final draft. The Figure was produced by O.R.J. with suggestions from
J.W.V., A.S., A.B. and H.C. A.B. suggested the method of standardization and the distinction between shape and pace. C.G.C.
developed methods to smooth mortality and fertility trajectories. H.C. and R.S.-G. contributed to the analysis of stage-classified
species. A.S., R.S.-G., O.R.J. and H.C. each provided data, derived from the literature, for several species. R.S. contributed
unpublished data for hydra; J.E., J.D. and M.B.G. for Borderea; R.S.-G. and B.B.C. for Cryptantha; and E.M. and P.F.Q.-A. for
Hypericum. O.R.J., A.S., R.S.-G. and H.C. screened the species for data quality.
Reprints and permissions information is available at www.nature.com/reprints.
The authors declare no competing financial interests.
Readers are welcome to comment on the online version of the paper.
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Published in final edited form as:
Nature. 2014 January 9; 505(7482): 169–173. doi:10.1038/nature12789.
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fitness in that environment. Hence, to understand the evolution of ageing, age patterns of mortality
and reproduction need to be compared for species across the tree of life. However, few studies
have done so and only for a limited range of taxa. Here we contrast standardized patterns over age
for 11 mammals, 12 other vertebrates, 10 invertebrates, 12 vascular plants and a green alga.
Although it has been predicted that evolution should inevitably lead to increasing mortality and
declining fertility with age after maturity, there is great variation among these species, including
increasing, constant, decreasing, humped and bowed trajectories for both long- and short-lived
species. This diversity challenges theoreticians to develop broader perspectives on the evolution of
ageing and empiricists to study the demography of more species.
To examine demographic age trajectories across the tree of life, we studied life tables
1
(that
is, patterns of mortality and fertility over age) and population projection matrices
2
for
multicellular species from a wide range of taxonomic groups (Fig. 1; see Supplementary
Methods for data sources and further rationale). We strived to find species with reliable data
and from diverse taxa. From the data for each species we estimated smoothed trajectories of
fertility, mortality and survivorship over age. Further research will undoubtedly refine the
curves shown for many of the species in Fig. 1 and reveal variation in different
environments and for different genotypes, but the general patterns are, we believe,
serviceably accurate.
We standardized the demographic trajectories to facilitate comparison. Specifically we
standardized the age axis so that it starts at the mean age of reproductive maturity and ends
at a terminal age when only 5% of adults are still alive. After this terminal age, sample sizes
were usually small and determination of age was often problematic. Fertility and mortality
were mean-standardized by dividing age-specific fertility and mortality by the respective
weighted average levels of fertility and mortality for all adults alive from maturity to the
terminal age (see Methods). We refer to these standardized values as relative fertility and
relative mortality. From the highest level of relative mortality at the terminal age (Fig. 1, top
left) to the lowest level (Fig. 1, bottom right), species are ordered sequentially, row-by-row
and from left-to-right. For the 46 diverse species depicted here, the range of variation in
trajectories of fertility and mortality is unexpected. As an indication of variability across
species, in modern Japanese women (Fig. 1, top left), mortality at the terminal age (102
years) is more than 20 times higher than the average level of adult mortality, whereas for
white mangrove (Avicennia marina; Fig. 1, bottom right) the level of mortality at 123 years
is less than half the average adult value.
Such variability is not predicted by the standard evolutionary theories of ageing
1,3–6
. Such
theories provide explanations solely for age patterns of increasing mortality and decreasing
fertility from maturity; the disposable soma theory
6
does so for species that segregate the
germ line from the soma. Furthermore, for those species that show a lifetime increase in
mortality, the canonical theory cannot account for the different magnitudes of that increase,
although the disposable soma theory points to the crucial importance of trade-offs between
the allocation of limited resources to repair and maintenance versus fertility and other
imperatives.
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Mortality
The most notable pattern is the mortality trajectory for post-industrial humans, exemplified
by Japanese women in 2009. The steep rise in relative mortality for the Japanese women is
extreme compared with the rise for other species and sharper than that for historical
populations such as the Swedish cohort born in 1881 and for hunter-gatherers such as the
Aché of Paraguay whose mortality experience may be typical of humans over most of
human existence
1,7
. The increased steepness of the rise of human mortality has largely
occurred over the past century, indicating that it was behavioural and environmental change
(including advances in health care) and not genetic change that moulded the current
pattern
7–9
. Our close relatives, chimpanzees (Pan troglodytes) and baboons (Papio
cynocephalus) also show a rise in mortality with age but far less than that for hunter-
gatherers.
In several species mortality declines with age (Fig. 1, bottom row) and, in some cases,
notably for the desert tortoise (Gopherus agassizii), the decline persists up to the terminal
age. In other cases, an initial decline is followed by more or less constant mortality (for
example, netleaf oak, Quercus rugosa). For species for which the underlying data are based
on stages, such as dwarf gorse (Ulex minor) or the red-legged frog (Rana aurora), an
asymptote is inevitable at older ages
8,10
. To alert readers to this, the mortality (and fertility
and survival) curves derived from stage-classified models are represented by dashed curves
in Fig. 1 at ages beyond which a cohort will have converged to within 5% of the quasi-
stationary distribution (see Methods).
For most species in Fig. 1 the age pattern of mortality is derived from data on ages rather
than stages. For some of these species, mortality levels off at advanced ages (for example,
for the collared flycatcher, Ficedula albicollis, the great tit, Parus major, the fruitfly,
Drosophila melanogaster) and in others remains constant at all adult ages (for example, for
Hydra magnipapillata). For hydra in the laboratory, this risk is so small that we estimate that
5% of adults would still be alive after 1,400 years under those controlled conditions.
Fertility
The fertility trajectories show considerable variation. For humans the trajectories are bell-
shaped and concentrated at younger adult ages, but other shapes are apparent in Fig. 1. The
patterns for killer whales (Orcinus orca), chimpanzees, chamois (Rupicapra rupicapra) and
spar-rowhawks (Accipiter nisus) are also approximately bell-shaped but spread over more of
the course of life. Other species show trajectories of gradually increasing fertility (for
example, southern fulmars, and the agave, Agave marmorata), asymptotic fertility (for
example, tundra voles, Microtus oeconomus), or constant fertility (for example, hydra). In
addition to humans and killer whales, bdelloid rotifers (Macrotrachela sp.), nematode
worms (Caenorhabditis elegans) and Bali mynah birds (Leucopsar rothschildi) have post-
reproductive life spans, which lends further support to the idea that this phenomenon may be
widespread
3–6,11
.
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Axes of senescence
Although the demographic trajectories in Fig. 1 vary widely, most of the 46 species can be
roughly classified along a continuum of senescence; running from strong deterioration with
age, to negligible deterioration, to negative senescence
12
and improvement with age.
However, there are some deviations, for example, for Soay sheep (Ovis aries) and dwarf
gorse, which show mortality reductions with adult age followed by deterioration. Fertility
patterns show similar diversity.
A fast–slow continuum has been proposed to order species from those with short lives and
intense early reproduction to those with long lives and an extended reproductive period
13–16
.
Figure 1 displays mortality and fertility over the adult lifespan; pre-reproductive mortality
trajectories are also of interest but beyond the scope of this article. If distinguished by the
length of life, then fast and slow life histories are scattered irregularly across Fig. 1.
Lifespans range from 1,400 years for the hydra to just 25 days for nematode worms. Species
with fast life histories, such as water fleas (Daphnia longispina), are followed in Fig. 1 by
species with slow life histories, such as the lion, and those with slow life histories, such as
the chimpanzee, occur adjacent to those with fast life histories, such as the human louse
(Pediculus humanus) and the fruitfly (D. melanogaster). Furthermore, species with very
different life spans can display similar patterns of mortality, fertility and survivor-ship. For
example, the water flea’s trajectories are similar to the fulmar’s, although water fleas reach
advanced old age at 48 days, whereas the fulmars do so at 33 years.
If senescence is measured by how long it takes for death rates to rise from some level to a
higher level, then long-lived species senesce slowly. It is more interesting to define
senescence by the sharpness or abruptness rather than the speed of the increase in mortality.
Baudisch
8
distinguishes the pace of life; that is, whether reproduction is fast and life spans
are short or reproduction is slow and life spans are long, from the shape of mortality and
fertility trajectories (whether mortality rises sharply with age and fertility falls sharply or
whether mortality and fertility levels are more constant). One measure of pace, the measure
that we have used, is the terminal age to which only 5% of adults survive; this measure is in
days or years or some other unit of time. One measure of shape, the measure that we have
used, is the ratio of mortality at the terminal age to the average level of adult mortality; this
time-invariant measure does not change if time is measured in days versus years. More
senescent species, with sharper increases in mortality with age, have higher values of this
measure of shape.
The measure can be used to explore further the unexpected lack of association between the
length of life and the degree of senescence. Among the first 24 graphs, those with the
sharpest senescence, 11 species have relatively long life spans and 13 have relatively short
life spans. Among the final 24 graphs, those with less senescence, 13 species have relatively
long life spans and 11 have relatively short life spans. This weak negative association
between the length of life and the degree of senescence is reflected in a weak Spearman rank
correlation of –0.13, which is not significantly different from zero (P =0.362). The
Spearman correlations are also non-significant when assessed for animals (P =0.414) and for
plants (P =0.07) examined separately. If the 12 plants in Fig. 1 are cross-tabulated as longer
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or shorter lived, and as more or less senescent, then three species fall into each of the four
categories. Hence the data support Baudisch’s
8
conjecture that pace and shape may be two
orthogonal axes of life histories.
A survivorship curve indicates the proportion of individuals that are still alive at a given age.
In Fig. 1, we plot survivorship from reproductive maturity on a logarithmic scale. If
mortality increases with age, the log-survivorship curve is concave. If mortality is
independent of age, log-survivorship is linear (for example, roughly from the hydra to the
red abalone (Haliotis rufesens) in Fig. 1). For species with death rates that decline with age,
the curve is convex (for example, from the red-legged frog to the white mangrove at the
bottom of Fig. 1). The classification of survivorship curves into concave, linear and convex
curves is known among biologists as type I, II and III, respectively
17,18
, but normally the
curves are plotted for lifespans starting at birth rather than at maturity. When the
evolutionary theory of ageing
3–6
was being developed, there was very little empirical
evidence for type III survivorship for adults and little evidence for type II survivorship. The
widespread recognition that traditional theories of ageing predict adult senescence to be a
universal trait led researchers to strive to find evidence for senescence in, for example, the
mute swan (Cygnus olor)
19
. For this species, fertility does decline and mortality does
increase at the oldest ages. However, the overall life course is characterized by fertility that
increases and then slowly declines and by roughly constant mortality: the log-survivorship
curve is nearly straight. It is clear from our analyses that the full spectrum of type I, II and
III survivorship curves are found for adults in nature.
Phylogenetic patterns
Phylogenetic relatedness seems to have some role in the order of species in Fig. 1, as shown
by taxonomic clustering of mortality, fertility and survivorship patterns. All mammals are
clustered in the top part of Fig. 1, whereas birds are somewhat more scattered, from the Bali
mynah in the first row to the great tit in the seventh row. Amphibians and reptiles are found
in the lower half of the panel, with flat mortality shapes and almost no overlap with
mammals. In contrast, invertebrates are scattered across the continuum of senescence, with
bdelloid rotifers and water fleas sharing the mammalian mortality pattern. The plants in our
sample tend to occur lower in our ordering, with the first being Hypericum cumulicola.
Although some angiosperm species seem to senesce
20–22
, many angiosperm species seem
not to
23
, perhaps as an artefact of the use of stage-based data
10
. The only alga in our data
set, oarweed (Laminaria digitata), falls in the last row.
Such clustering within broad taxonomic levels of kingdom (plants, animals), or class
(mammals, birds), suggests that primitive traits related to the bauplan of species may have a
pivotal role in determining patterns of ageing. In fact, the evolutionary conservatism of
mechanistic determinants of ageing has been highlighted by genetic studies
24
and it has been
suggested that asexual reproduction
25
, modularity
26
, lack of germ-line sequestration from
the soma
27,28
, the importance of protected niches
29
, regenerative capacity, and the paucity
of diverse cell types
30
, may facilitate the escape from senescence in some clades. Many of
the species in the lower half of Fig. 1—the reptiles, vascular plants, alga, and coral—
continue to grow after reproductive maturity to sizes much larger than those at maturity. For
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