Aging Cell (2005) 4, pp000–000
Doi: 10.1111/j.1474-9726.2005.00162.x
REVIEW
Blackwell Publishing, Ltd.
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Correlations between physiology and lifespan – two
widely ignored problems with comparative studies
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Introduction
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Comparative differences between species provide a powerful source of information that may inform our understanding of the aging process. However, two problems
regularly attend such analyses. The co-variation of traits
with body mass is frequently ignored, along with the lack
of independence of the data due to a shared phylogenetic
history. These problems undermine the use of simple correlations between various factors and maximum lifespan
potential (MLSP) across different species as evidence that
the factors in question have causal effects on aging. Both
of these problems have been widely addressed by
comparative biologists working in fields other than aging
research, and statistical solutions to these issues are available. Using these statistical approaches, of making analyses
of residual traits with the effects of body mass removed,
and deriving phylogenetically independent contrasts, will
allow analyses of the relationships between physiology
and maximum lifespan potential to proceed unhindered
by these difficulties, potentially leading to many useful
insights into the aging process.
Key words
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Summary
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Aberdeen Centre for Energy Regulation and Obesity (ACERO),
School of Biological Sciences, University of Aberdeen, Aberdeen,
Scotland, UK, and ACERO, Division of Energy Balance and Obesity,
Rowett Research Institute, Bucksburn Road, Aberdeen, Scotland, UK
restricted to single classes of organism. The shortest lived
mammals (class Mammalia), for example shrews, with MLSPs of
around 12 –15 months (Brambell, 1935; Hutterer, 1976), have
lives that are two orders of magnitude shorter than the current
estimates of maximum longevity for the longest lived mammalian species (whales living up to 200 years; George et al., 1999).
These differences in MLSP have evolved within the different
ecological contexts in which the different species live, but must
also have an underlying physiological, biochemical and molecular
basis. Studies examining the differences in the physiology of
species that vary in their MLSP may therefore provide insights
into the mechanisms that underlie the physiological basis
of aging in specific creatures – such as humans (Austad, 1996,
1997; Barja, 2004). This comparative approach for understanding the physiological basis of the aging phenomenon has been
used since at least the early 1900s (e.g. Rubner, 1908).
In this paper I will highlight two problems with this approach
that, despite several previous papers highlighting them (Promislow, 1991, 1993, 1994; Speakman et al., 2002), have been, and
continue to be, widely ignored by the community of scientists
working on aging. Although not the only difficulties with the
comparative method, they are among the more serious issues.
The problems are generic and they attend utilization of the
comparative method in all fields of study, and are not restricted
to using such data to study aging phenomena. Fortunately, in
several other fields these problems have been well appreciated
(e.g. studies of life histories of animals; Harvey & Keymer, 1991)
and statistical methods have been developed to overcome them
(Felsenstein, 1985; Garland et al., 1993). I suggest these alternative methods can be profitably utilized in the study of comparative biology of aging. Unfortunately, however, the apparent
lack of familiarity with these issues to date has led to the
publication of many fundamentally flawed and potentially
misleading papers in the field of aging research.
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John R. Speakman
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The maximum lifespan potential (MLSP) has been used widely
as a conceptual expression of the physiologically attainable
lifespan by a given species. The problems with this concept
have been fully explored by Carey (2003). Nevertheless, MLSP
remains a frequently used trait in comparative biology, where
it is generally equivalent to the lifespan of the oldest observed
specimen of any particular species. Species differ enormously
in their MLSPs, even when the comparative database is
Correspondence
Dr John R. Speakman, Aberdeen Centre for Energy Regulation and Obesity
(ACERO), School of Biological Sciences, University of Aberdeen, Aberdeen,
Scotland, UK. Tel.: +44 (0)1224 272879, Fax: +44 (0)1224 272396;
e-mail: [email protected].
Accepted for publication 29 April 2005
Problem one: co-variation of lifespan and
physiological traits with body mass
Body mass is a pervasive trait that influences all levels of organismal biology. Apart from a few phenomena such as circadian
cycles, almost every aspect of organismal biology differs as a
function of body mass (e.g. Peters, 1983; Calder, 1984; SchmidtNielsen, 1984). This ‘allometry’ of relationships to body mass
differences between species has formed one of the few bases
for the development of fundamental biological laws that unify
our understanding of function across life in general (West et al.,
1997; West & Brown, 2004). Not surprisingly, MLSP is also a
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trait that is correlated with body mass of different species
(for example in mammals – Fig. 1). The direct consequence of
this pervasive role of body mass in organismal biology is that
MLSP will be correlated with anything that is also related to body
mass – which is just about everything. Figure 2 shows some
examples of such relationships between biological traits and
MLSP that have recently been published, and some additional
relationships between MLSP that are newly generated here to
illustrate the problem. Mammals that live longer have lower urinary excretion rates of DNA excision repair products (8-oxoGua
and 8-Oxodg) (Foksinski et al., 2004), lower oxidative damage to
mitochondria (8-Oxodg in mitochondrial DNA) (Barja, 2002a,b),
lower levels of fatty acid desaturation in heart phospholipids
and lower levels of DHEA in heart phospholipids (Pamplona
et al., 1999). However, longer lived mammals also have lower
activities of citrate synthase, combined with higher levels of
both lactate dehydrogenase and pyruvate kinase (Emmet &
Hochachka, 1981), lower mass-specific basal metabolic rates
(Calder, 1984), and perhaps most revealingly concerning the
spurious nature of such analyses, they also have longer legs and
larger diameter eyeballs.
Although we could construct some plausible mechanistic linkage for these latter two relationships – for example animals with
bigger eyes can see dangers coming at greater distances and
are therefore able to run away sooner, and longer legs enable
them to run away faster, both potentially reducing their risks
of mortality, most researchers would agree that there is no
causal relationship between the size of an animal’s eyeballs or
its legs and its MLSP or its rate of aging. It is obvious that these
relationships arise because MLSP is linked to body size and
bigger animals have both bigger eyes and longer legs. The other
relationships in Fig. 2(F–J) arise by generally similar mechanisms
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Fig. 1 The relationship between Loge MLSP (maximum reported longevity)
and Loge body mass for 249 species of mammal.
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– animals that are bigger have lower metabolic rates and hence
the enzymatic machinery attached to metabolism (citrate
synthase, lactate dehydrogenase and pyruvate kinase) differs in
consistent ways with body size and this leads to spurious associations between these traits and MLSP. Such relationships are
not strong evidence for a causal link between these traits and the
aging process. Because they are generated in exactly the same
way, this criticism also extends to the other traits (Fig. 2A – E)
where it is generally inferred that the associations do reflect
some underlying causality.
Clearly, the differences in lifespan between large and small
species must arise via some mechanism rooted in physiology
(Barja, 2004). The problem is that simply correlating MLSP with
aspects of physiology does not allow us to separate important
traits where differences in the physiology cause differences in
MLSP from trivial traits where the correlation arises only because
both are related to body mass. If we interpret the relationship
between variation of a given trait and MLSP as an important
indicator of the process of aging, this reflects more our biases
concerning the physiological basis of the aging process rather
than anything inherent in the statistics.
To overcome these problems, an approach is needed that
statistically allows us to separate the alternative reasons that
might generate an interspecific association between MLSP and
a given trait of interest. Such methods are in widespread use
in other fields of enquiry (e.g. Pearl, 2000; Shipley, 2000) and
include several sophisticated approaches such as ‘path analysis’.
Among the simplest methods to overcome this problem is to
seek associations between residual variation in the trait and
MLSP, once the effects of body mass have been statistically
controlled (Promislow, 1991). This method is illustrated for a
hypothetical trait in Fig. 3. As is generally the case, both this
hypothetical trait and MLSP are related to body mass. The technique involves fitting an allometric relationship between the
trait and body mass and between MLSP and body mass, for the
same species. Several alternative regression models are available
to fit allometric relationships. The most commonly utilised is
model I regression or least squares. Least-squares regression
makes the assumption that the error variation in the x-trait is
zero. This is clearly unrealistic as no variable can be measured
free of error. It is the most commonly used regression model
in allometry, however, because it is widely acknowledged that
although body mass cannot be measured free of error, the error
variance is generally considerably lower than any other trait
of interest. Consequently, although measurements generally
violate the assumptions of least-squares regression, the most
common alternative regression model [model II or reduced major
axis (RMA) regression] makes the even less realistic assumption
that the error variance in both traits is equal.
To remove the confounding effect of body mass, one calculates the residuals to this fitted regression. These residuals are
in effect the vertical distances that each data point lies from
the line of best fit. The nature of the least-squares regression
fitting procedure is that the residuals must sum to zero and be
independent of the x variable. This is another advantage of
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Correlations between physiology and lifespan, J. R. Speakman 3
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Fig. 2 Relationships between physiological and other traits and Loge MLSP. (A) Urinary 8-oxo-7,8-dihydroguanine, (B) urinary 8-oxo-7,8-dihydro-2′deoxyguanosine, (C) urinary 5-hydroxymethyl uracil, (D) mitochondrial oxidative damage (Mt 8OhdG), (E) lipid double bond index, (F) DHEA levels, (G) basal
metabolic rate/gram body tissue, (H) citrate synthase, (I) lactate dehydrogenase, (J) pyruvate kinase, (K) limb length and (L) eyeball diameter. All traits are Log
transformed. (A) – (C) from Foksinski et al. (2004); (D) from Barja et al. (2004); (E) and (F) from Pamplona et al. (1999); (G) based on data in Speakman (2005);
(H) –(J) from Emmett & Hochachka (1981); (K) and (L) based on data compiled from direct measurements of specimens in the University of Aberdeen zoological
museum and animal house.
© Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 2005
4 Correlations between physiology and lifespan, J. R. Speakman
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using least-squares regression to fit the line because the residuals
to other fitted regression lines, like RMA, are not independent
of the x variable (body mass in these cases). For any trait, therefore, there is a combination of positive and negative residual
values. If a species has a high positive residual for MLSP then
this value says that for its body mass the species lives a long
time. Its actual maximum lifespan may be quite short relative
to that of a whale or an elephant, but relative to other animals
of the same size it lives a long time. For example, take a small
insectivorous bat like the pipistrelle (Pipistrellus pipistrellus). This
animal weighs 6 g and has an MLSP of 16.5 years (Hurka, 1986).
In absolute terms its life is short. However, for its size it has a
very long life. The pipistrelle data point lies well above the relationship of MLSP to body size and it therefore has a high positive
residual. Similarly, a large negative MLSP residual value says
that, independent of body mass, that species lives a relatively
short life. By definition these residuals are not related to body
mass. To see if the trait in question is related to MLSP, independent of any effects of body mass, one examines the
relationship between the residuals. If the hypothesis is that
high rates of mitochondrial oxidative damage lead to shortened
lifespan one would anticipate that species with high residual
rates of mitochondrial oxidative damage would have low residual lifespans. If this analysis is significant then one might
infer that the relationship between the two occurs not because
they are both related to mass, but because they are really related.
However, if there is no relationship in the residuals, one might
conclude that the association when body mass was not
accounted for arose simply because both traits were correlated
with body mass.
As a practical example of this approach, consider the relationship of basal metabolic rate (BMR) to MLSP. Many previous
studies have addressed the question of whether metabolic rates
of animals are related to their lifespans, dating back to the
seminal work of Rubner (1908). As illustrated in Fig. 2, MLSP is
negatively related to BMR. Animals with low rates of metabolism (per gram body tissue) live longer. The association in Fig. 2
is based on an analysis of 249 species for which both BMR and
MLSP are currently available (Speakman, 2000, 2005). One
hypothesis is that this association arises because low metabolic
rate involves low rates of oxidative phosphorylation, correspondingly low rates of electron transport and hence lowered
free-radical production. But it may equally arise because both
BMR and lifespan are related to body mass. If we calculate the
residual BMR and plot it against the residual MLSP (Fig. 4) then
it is clear that mammalian species that have high metabolic rates
for their body masses do not live shorter lives for their body
masses, allowing us to reject the causal nature of the original
correlation.
In Fig. 5, I have re-plotted several recently published relationships between MLSP and various traits related to oxidative
damage using this residuals approach. From these plots several
interesting things emerge. In the original plots between lifespan
and the absolute values of these traits, all the relationships were
highly significant (Fig. 2). However, when the shared effects of
Fig. 3 The analysis of residuals. In (A) the relationship between MLSP and
body mass is plotted for a sample of 29 species. In (B) the relationship of a
hypothetical trait related to aging is also plotted against body mass for the
same 29 species. Because the relationship between body mass and lifespan
is positive and the relationship to the hypothetical trait is negative the
relationship between lifespan and the trait is also negative (C). The residual
MLSP is the vertical distance of each datum to the line of best fit in (A) and
the residual trait is the same in (B). To test if the trait is associated to MLSP
independent of the effects of body mass one examines the association of
the residual MLSP to the residual trait (C). In this case the relationship revealed
in the residuals is positive (D), exactly the opposite of the trend in the raw
data (C), exposing how misleading the plots of raw data can be because of
co-variation of traits with body mass.
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body mass are removed most of these significant relationships
disappear. Nevertheless, some relationships remain significant.
For example, the negative association between urinary levels of
the DNA excision repair product 8-oxo-7,8-dihydroguanine and
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MLSP remains significant (P = 0.026, r = 78.5%: Fig. 5A), and
the negative association between levels of DHEA and MLSP also
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Fig. 4 Analysis of residuals of MLSP plotted against residual basal metabolic
rate (BMR). Although MLSP is strongly negatively related to BMR (Fig. 2H)
there is no significant association in the residuals indicating that this
relationship arises because of the relationships of both MLSP and BMR to
body mass.
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remains significant (P = 0.038, r = 83.5%, Fig. 5F), after the
shared effects of body mass are removed. I suggest that using
this approach may give us a much better indication of the traits
that are significantly related to the aging process. However, it
is important to remember that even after removing the effects
of body mass, the relationship between residual variation in a
trait and residual variation in MLSP is still only a correlation. This
may give us some insight into processes that are linked to aging,
but it cannot be used to infer causality. This is because the
correlation may arise as a result of causality in the opposite
direction, or as a result of other shared covariations apart from
body mass. So, simply because levels of residual variation in
DHEA are lower in individuals that have greater residual MLSP
does not mean these low levels cause the extended life. The
low levels of DHEA may themselves be a consequence rather
than a cause of MLSP, or low levels of DHEA may be a consequence of a separate process that itself drives, or is caused
by, the MLSP.
There is one additional caveat. Many of the comparative
studies published thus far are based on limited data sets. Typical
numbers of species included in such studies are less than 20.
These small samples are prone to bias in residual analyses by
occasional outlying values. Naturally outlying values may be of
important biological significance: an example is the pipistrelle
bat mentioned above. However, where the factor driving the
value to be an outlier is not biological, then in a small sample
such data may cause problems – either generating spurious
Fig. 5 The relationships in Fig 2(A–F) re-plotted using residuals of MLSP and the traits in question to body mass. In most cases the highly significant relationships
disappear, although some remain, possibly indicating their significance for the aging process.
© Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 2005
6 Correlations between physiology and lifespan, J. R. Speakman
other non-significant relationships (Fig. 5B,C) up to the P < 0.05
significance criterion, and the significant association to residual
levels of DHEA (Fig. 5F) is unaffected because humans were not
included in the sample generating those data.
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Problem two: species do not represent
statistically independent data
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The second problem with using comparisons of different species
that has been widely ignored by biogerontologists (but see
Kapahi et al., 1999; Speakman et al., 2002) is the problem that
extant species are the products of the process of evolution. As
such, all mammals, for example, share part of their evolutionary
history with some common ancestor, which renders them nonindependent data. Because independence of the individual data
included in any analysis is a prerequisite of most statistical procedures, for example, the least-squares regression used in the
preceding analyses, using the raw data may be a problem. The
nature of this problem can perhaps be best appreciated by a
practical example. It is well established that the order Chiroptera
have much longer lifespans than expected for their body sizes
(high residual lifespans) (Austad & Fischer, 1991; Wilkinson &
South, 2002; Brunet-Rossinni & Austad, 2004). The Marsupalia
show the converse pattern of low lifespans for their body
masses (Austad & Fischer, 1991). Consider then a comparative
study that includes three bat species, three marsupials and three
rodents. A plot of their respective lifespans as a function of body
mass is shown in Fig. 6(A). Now consider a hypothetical trait we
believe is related to aging also plotted against body mass for
the same species (Fig. 6B). If we plot the raw data we have a
significant relationship of this trait to MLSP (Fig. 6C), and if we
calculate the residuals we also have a significant relationship
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associations of residual traits or negatively interfering with the
detection of significant associations. The most important example
of this is the inclusion of data of MLSP for humans. The main
problem with the MLSP for humans is that MLSP is generally
quoted as 120 years. This estimate is presumably a rounding of
the longest authenticated human longevity record (122 years
164 days in 2004 – Jeanne Calment) to the nearest decade. But,
estimates of MLSP are very dependent on sample size contributing to the sample (Carey, 2003), and this human longevity
record emerges from hundreds of millions of accurate birth and
death records. No other species comes even remotely close to
matching this sample size. For example, records for longevity
of different dog strains that are derived from pet insurance
schemes currently include fewer than 10 000 estimated lifespan
records for most dog breeds (e.g. Speakman et al ., 2003)
and these are among the better lifespan data available for mammals. Hence, MLSP for humans is an outlier in most analyses,
not because humans are exceptionally long lived, but mostly
because they are exceptionally well documented and have an
enormous sample size. This might cause some additional complications if the human datum reflects the largest body weight
included in the analysis, because this would bias upwards the
regression linking MLSP to body size and hence compromise the
estimated residual calculations. Identifying outliers in data is a
difficult problem for which there are many available and sophisticated techniques. However, in this case one has a good a priori
demographic reason to exclude the human datum before
analyses commence (as suggested by Promislow, 1994). In the
example in Fig. 5(A), omitting the human datum reduces the
significance of the relationship of residual MLSP to residual
8-oxo-7,8-dihydroguanine such that it is no longer significant
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(F = 3.85, P = 0.145, r × 100 = 56.2%), but does not bring the
Fig. 6 (A) Hypothetical relationship of loge MLSP
in three bat (open circles), three rodent (open
squares) and three marsupial (open triangles)
species to Loge body mass. (B) Hypothetical
relationships of a trait believed to be related to
aging in the same species plotted in (A) also plotted
against body mass; (C) raw values of the trait in
(B) plotted against MLSP; (D) residual variation in
the trait in (B) plotted against residual MLSP.
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(Fig. 6D). However, it is also clear that the data in Fig. 6(D) are
clustered by their order of origin. A phylogenetic tree showing
the interrelationships of these species is shown in Fig. 7. A single
mutation at point A in the tree that conferred low levels of the
trait in question would be shared by all the animals downstream
of that point in the phylogeny. This includes all the marsupials.
Equally, a mutation at point B would affect all the bats.
Although we have sampled three species of bats and three
species of marsupials we are really pseudo-replicating the analysis
because in reality only two mutational events occurred in the
evolutionary history. The ‘true’ sample size in this analysis is not
nine but closer to three, which reflects the three different genotypes: carrying mutation A, carrying mutation B but not A, and
carrying neither A nor B. Another way to think about this is that
one would not include the data for each individual animal within
a species as independent data in such an analysis, as two
individuals of the same species share their physiology because
of a shared phylogenetic history – not because each has been
the product of a unique special act of creation. Data for different
species cannot be included for the same reason.
This problem in comparative biology has been appreciated
for at least 20 years (Felsenstein, 1985) and a whole series of
sophisticated statistical approaches have been developed that
aim to transform raw interspecific data into what are generally
termed phylogenetically independent contrasts (Pagel & Harvey,
1988; Harvey & Keymer, 1991; Purvis & Garland, 1993;
Diaz-Uriarte & Garland, 1998; Garland et al ., 1999, 1993;
Freckleton et al., 2002). These contrasts are constructed using
known phylogenies for the interrelationships between the
different species and this whole area has been facilitated by the
development of molecular methods for diagnosing phylogenetic
interrelationships of different organisms. Sensitivity analyses for
errors in the phylogeny have been widely performed and it has
been shown that the methods are generally robust to such errors
(Freckleton et al., 2002; Purvis & Garland, 1993; Diaz-Uriarte &
Garland, 1998). The net result of performing a phylogenetically
independent contrasts analysis is that the significance of relationships tends to decline because the lack of independence in
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Fig. 7 Hypothetical phylogenetic tree relating the species plotted in Fig. 6.
Mutations at A and B affect all the species downstream of that point.
Sampling several species downstream of the mutation points does not give
an independent sample with respect to the mutations because the samples
are not independent.
© Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 2005
Fig. 8 Phylogenetically independent contrasts analysis. In (A) the tip data are
shown for the relationship of residual MLSP plotted against residual DHEA
(as in Fig. 5F), but with the individual species shown and the division into
rodent/lagomorphs (closed symbols) and artio/perissodactyls (open symbols).
The phylogeny of the eight animals in this sample reconstructed from
molecular data is shown in (B) with the seven nodes numbered. The
phylogenetically independent contrasts analysis for the same data in (A) using
the phylogeny from (B) is shown in (C). The contrasts at each node are
numbered. The resultant relationship was not significant (F = 2.6, P = 0.152).
the data is corrected for. To illustrate the use of this method I have
taken the one relationship in Fig. 5 that remained significant
after the residuals analysis excluding data for humans (Fig. 5F
the negative association of residual MLSP with residual levels
of DHEA). This plot is redrawn in Fig. 8(A) with the individual
species shown. The data comprising this analysis conveniently
divide into the rodents/lagomorphs (closed symbols) and the
artiodactyl/perissodactyls (open symbols). Visual examination of
Fig. 8(A) suggests the data are not clumped in the manner of
8 Correlations between physiology and lifespan, J. R. Speakman
Acknowledgments
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I am grateful to Wayne van Voorhies and Don Thomas for useful
discussions of these issues and to Ted Garland for the copy of
PDAP. Colin Selman and two anonymous referees made extremely
helpful comments on previous drafts of the manuscript.
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Conclusions
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those in Fig. 6(D), indicating that lack of independence from
phylogeny may not be too serious a problem, although obviously the datum for the rabbit exerts a large leverage on this
regression and its inclusion might be questioned. The phylogeny
of the species involved in this analysis reconstructed from molecular sequence data for the mammals in general (Graur, 1993;
Graur et al., 1997; Robinson-Rechavi & Graur, 2001) is shown
in Fig. 8(B). Sophisticated data analysis packages utilize the data
that are encapsulated in this phylogeny along with the original
data to generate the phylogenetically independent contrasts.
The equivalent phylogenetically independent contrasts plot is
shown in Fig. 8(C) to illustrate how these compare. This plot was
derived using the Felsenstein (1985) method for deriving the
phylogenetically independent contrasts (PICs) using the PDAP
program (Garland et al., 1993). Note that each point in the contrasts plot represents the contrast at each node in the phylogeny. As there must be n − 1 nodes in the phylogeny relative
to n original data, the sample size is one lower for the PIC plot
than the original data. Individual contrasts are indicated by
numbers next to the individual points and refer to the nodes
in Fig. 8(B). The relationship in Fig. 8(C) was not significant
(F = 1.6, P = 0.162). The original trend is, however, preserved
and the failure to reach the 0.05 criterion may reflect a power
issue at the low sample size. Alternatively, much of the significance was due to the contrast at node 5, between rabbit and
the rodents, which may occur because of the strong leverage
that the rabbit point had in the original plot (Fig. 8A). Removing
this point would completely remove any correlation. Using phylogenetically independent contrasts in studies of interspecific
data for traits related to MLSP is highly desirable, particularly
when relationships using the raw data are only marginally
significant.
U
N
C
O
Comparative biology provides a rich source of variation in animal lifespans that are the substrate of many comparative studies
aiming to elucidate features of the aging process. However,
attractive as it may seem, comparative biology has lots of pitfalls
into which many previous studies in biogerontology have fallen.
This is despite the numerous excellent previous papers that have
sought to alert readers to the salient problems (e.g. Promislow,
1991, 1993, 1994). The present paper has aimed to highlight
just two of the most common mistakes that still litter the
biogerontological literature. The first is the problem of not
accounting for the shared variability in given traits and MLSP
by differences between species in body mass. The second is not
accounting for the phylogenetic dependence of comparative
data. Fortunately, these problems have been long recognized
in other fields that use the comparative method extensively
(such as evolutionary biology), and statistical methods are available to overcome these problems. I suggest that greater insights
into the process of aging will emerge from the comparative
method if these well-established approaches from other fields
are used rather than ignored.
© Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 2005
Correlations between physiology and lifespan, J. R. Speakman 9
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© Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 2005
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