The Relationship between Body Size and Lifespan

All species

Let’s try graphing now. First, we will graph all the species; we will graph the adult weights versus their maximum lifespan, and color the datapoints by their Phylum:

# Create the basic plot
p.all <- anage %>% 
  ggplot(
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`, color=Phylum, text=str_glue("Common Name: {`Common name`}<br>Data Quality: {`Data quality`}<br>Sample size: {`Sample size`}"))
  ) +
  geom_point(size=0.5) +
  scale_x_log10() +
  scale_y_log10() +
  labs(
    title='AnAge Species Adult Weight vs Lifespan',
    y='Adult Weight - log(g)',
    x='Maximum Longevity - log(yrs) '
  ) + 
  theme_pubclean() + 
  labs_pubr()
# Output the interactive plot
ggplotly(p.all)

(Note that we scaled the axes using a log-scale; this is done because we want to highlight orders-of-magnitude changes over small-scale change - in other words, we don’t care so much about the difference between 1-2 grams and 100-200 grams as much as a change between 1-10 grams and 100-1000 grams.)

You can see that the graph already has a clear upwards trend! However, there’s a bit of an issue that’s striking in the color scheme; where are our non-chordate species? The first guess I have is that it relates to size measurements - that’s relatively easy to check:

anage %>% 
  filter(!Phylum=="Chordata") %>% 
  select(Kingdom, Phylum, Genus, Species, `Common name`, `Maximum longevity (yrs)`, contains("weight"))

## # A tibble: 16 x 9
##    Kingdom Phylum Genus Species `Common name` `Maximum longev… `Birth weight (…
##    <chr>   <chr>  <chr> <chr>   <chr>                    <dbl>            <dbl>
##  1 Animal… Arthr… Dros… melano… Fruit fly                 0.3                NA
##  2 Animal… Arthr… Apis  mellif… Honey bee                 8                  NA
##  3 Animal… Arthr… Lasi… niger   Black garden…            28                  NA
##  4 Animal… Arthr… Bicy… anynana Squinting bu…             0.5                NA
##  5 Animal… Arthr… Homa… americ… American lob…           100                  NA
##  6 Animal… Cnida… Turr… nutric… Immortal jel…            NA                  NA
##  7 Animal… Echin… Stro… franci… Red sea urch…           200                  NA
##  8 Animal… Echin… Stro… purpur… Purple sea u…            50                  NA
##  9 Animal… Mollu… Arct… island… Ocean quahog…           507                  NA
## 10 Animal… Nemat… Caen… elegans Roundworm                 0.16               NA
## 11 Animal… Porif… Cina… antarc… Epibenthic s…          1550                  NA
## 12 Animal… Porif… Scol… joubini Hexactinelli…         15000                  NA
## 13 Plantae Pinop… Pinus longae… Great Basin …          4713                  NA
## 14 Fungi   Ascom… Sacc… cerevi… Baker's yeast             0.04               NA
## 15 Fungi   Ascom… Schi… pombe   Fission yeast            NA                  NA
## 16 Fungi   Ascom… Podo… anseri… Filamentous …            NA                  NA
## # … with 2 more variables: `Weaning weight (g)` <lgl>, `Adult weight (g)` <dbl>

None of the non-chordates in the AnAge database have any weight information - go figure! However, from the lifespan, we can see that some of these live a ridiculously long time:

# Filter anage based on the weird characteristics:
anage %>% 
  arrange(desc(`Maximum longevity (yrs)`)) %>% 
  head %>% 
  select(Kingdom, Phylum, Genus, Species, `Common name`, `Maximum longevity (yrs)`, `Adult weight (g)`, `Data quality`)

## # A tibble: 6 x 8
##   Kingdom Phylum Genus Species `Common name` `Maximum longev… `Adult weight (…
##   <chr>   <chr>  <chr> <chr>   <chr>                    <dbl>            <dbl>
## 1 Animal… Porif… Scol… joubini Hexactinelli…            15000              NA 
## 2 Plantae Pinop… Pinus longae… Great Basin …             4713              NA 
## 3 Animal… Porif… Cina… antarc… Epibenthic s…             1550              NA 
## 4 Animal… Mollu… Arct… island… Ocean quahog…              507              NA 
## 5 Animal… Chord… Bala… mystic… Bowhead whale              211       100000000.
## 6 Animal… Chord… Seba… aleuti… Rougheye roc…              205             495 
## # … with 1 more variable: `Data quality` <ord>

Remember how I said that some animals live for millenia? Behold the humble sponge; specifically, Scolymastra joubini, which apparently lives for 15,000 years! Its worth noting the column “Data.quality” here; there’s some skepticism in the literature as to whether or not this is estimate is real, since its so incredible. Runners-up include the Great Basin bristlecone pine, the Ocean quahog clam, the Greanland Shark, and my favorite, the Bowhead Whale!

Chordates

Moving on, let us graph the chordates, and color by class. Also, while we’re at it, let’s quantify the relationship between size and lifespan using a linear regression:

anage.chordata <- anage %>% 
  filter(
    Phylum=="Chordata",
    !is.na(`Adult weight (g)`),
    !is.na(`Maximum longevity (yrs)`)
      )


# Basic Graph
p.chordata <- anage.chordata %>% 
  ggplot(
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`, color=Class, text=str_glue("Common Name: {`Common name`}<br>Data Quality: {`Data quality`}<br>Sample size: {`Sample size`}"))
  ) +
  geom_point(size=0.5) +
  scale_x_log10() +
  scale_y_log10() +
  geom_smooth(
    method='lm', 
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`), 
    inherit.aes = FALSE,
    col="black",
    lty="dashed"
    )+
  labs(
    title='Chordates Adult Weight vs Lifespan',
    y='Adult Weight - log(g)',
    x='Maximum Longevity - log(yrs) '
  ) +
  theme_pubclean() + 
  labs_pubr()

# Output the interactive plot
ggplotly(p.chordata)

## `geom_smooth()` using formula 'y ~ x'

Overall, there seems to be a positive trend; we can already see the outliers that are extremely long-lived for their size by hovering over the points to read the common names. As you can see, the longest lived chordates by far are the Rougheye Rockfish, at 205 years; and the Bowhead Whale, at 211 years. A little further down the y axis, we see many more fish, humans (!), and some more whales. Now we can look further at the data by clicking on the classes on the legend to hide those points.

Hide everything so that we can only see Actinopterygii, which is the class of ray-finned fish. This group accounts for almost all of the living fish species, and makes up half of all living vertebrates. As you can see, for any given weight of fish, you could probably find many short lived fish and a couple of really long-lived fish. In contrast, if you look only at Amphibians, you can see that based on our data they pretty much cluster tightly with the exception of the Olm, which lives a very long time for its size.

Looking at Aves, or birds, we can see that there’s actually a fairly clear positive relationship between size and longevity, with most of the density in the plot at the small-and-short-lived end of the tail, fanning out as you get larger and larger. Were I to venture a guess, this fan-shaped pattern is suggestive of the two sides of Peto’s Paradox: some of these birds seem to live longer as per the pattern, but some of them seem to be shorter-lived than one would expect. One thing that caught my attention was how emus and cassowaries, which are sister species, are all shorter-lived than the ostrich, which is at the root of the Ratite clade. While this alone means nothing, its small things of note like this that could lead to the start of some really interesting work!

Mammals

Now, while I may be biased, I want to look at the most interesting clade: mammals!

# Select all mammals with values for size and lifespan
anage.mammalia <- anage.chordata %>% 
  filter(
    Class == 'Mammalia')

# Graph!
p.mammalia <- anage.mammalia %>% 
  ggplot(
    aes(x=`Adult weight (g)`, y=`Maximum longevity (yrs)`, color=Order, text=str_glue("Common Name: {`Common name`}<br>Data Quality: {`Data quality`}<br>Sample size: {`Sample size`}"))
  ) +
  geom_point(size=rel(2)) +
  scale_x_log10() +
  scale_y_log10() +
  geom_smooth(
    method='lm', 
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`), 
    inherit.aes = FALSE,
    col="black",
    lty="dashed"
    )+
  labs(
    #title='A Positive Correlation Between Size and Lifespan Across All Mammals',
    x='Adult Weight - log(g)',
    y='Maximum\nLongevity\nlog(years) ',
    caption = "Human Ageing Genomic Resources:\nAnAge Build 14, 2017"
  ) +
  theme_pubclean() + 
  labs_pubr()+
  theme(
    legend.position = "bottom",
    text = element_text(size=24),
    axis.title.y = element_text(angle=0, vjust = 0.5)
  )

#p.mammalia
ggplotly(p.mammalia)

## `geom_smooth()` using formula 'y ~ x'

Here we see the beautiful correlation between mammalian weight and lifespan! Take a moment to notice some of the subtle things this graph suggest. Rodents are actually shorter-lived as a clade than one would expect based on their size, while bats and primates are longer-lived than expected. A commonplace theory behind this is that the ability to escape terrestial predators allows bats and primates to avoid predators that would otherwise hunt them alongside existing terrestial prey, like rodents; since they are now less likely to die due to extrinsic predatorial dangers, genetic changes that allow for extended lifespan can take root and either be selected for actively or evolve neutrally.

Bats

anage.chiroptera <- anage.mammalia %>% 
  filter(Order=='Chiroptera')

# Once more, with vigor!
p.bats <- anage.chiroptera %>% 
  ggplot(
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`, color=Family, text=str_glue("Common Name: {`Common name`}<br>Data Quality: {`Data quality`}<br>Sample size: {`Sample size`}"))
  ) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10() +
  scale_alpha_discrete(range=c(0.5,1)) + 
  geom_smooth(
    method='lm', 
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`, color=Family), 
    inherit.aes = F,
    se=F
    )+ 
  geom_smooth(
    method='lm', 
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`, text="Chiroptera"), 
    inherit.aes = FALSE,
    col="blue",
    lty="dashed"
    )+
  geom_smooth(
      data = anage %>% filter(Class == 'Mammalia',!is.na(`Adult weight (g)`), !is.na(`Maximum longevity (yrs)`)), 
      method='lm', 
      aes(`Adult weight (g)`, `Maximum longevity (yrs)`, text="Mammalia"), 
      inherit.aes = FALSE, 
      col="black", lty="dashed", alpha=0.5
      ) +
  labs(
    title='A Positive Correlation Between Size and Lifespan Across All Mammals',
    y='Adult Weight - log(g)',
    x='Maximum Longevity - log(yrs) '
  ) +
  coord_cartesian(xlim=c(3e0,2e3), ylim=c(3e0,1e2))+
  theme_pubclean() + 
  labs_pubr()

## Warning: Using alpha for a discrete variable is not advised.

## Warning: Ignoring unknown aesthetics: text

## Warning: Ignoring unknown aesthetics: text

ggplotly(p.bats)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

anage.paenungulata <- anage.mammalia %>% 
  filter(Order %in% c('Proboscidea','Sirenia', 'Hyracoidea'))

p.paen <- anage.paenungulata %>% 
  ggplot(
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`, color=Family, text=str_glue("Common Name: {`Common name`}<br>Data Quality: {`Data quality`}<br>Sample size: {`Sample size`}"))
  ) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10() + 
  geom_smooth(
    method='lm', 
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`), 
    inherit.aes = FALSE,
    col="blue",
    lty="dashed"
    )+
  geom_smooth(
    data = anage.mammalia, 
    method='lm', 
    aes(`Adult weight (g)`, `Maximum longevity (yrs)`), 
    inherit.aes = FALSE, 
    col="black", lty="dashed", alpha=0.5
    ) +
  coord_cartesian(xlim=c(1e2,1e7), ylim=c(1e1,2e2))+
  labs(
    title='Paenungulata Adult Weight vs Lifespan',
    y='Adult Weight - log(g)',
    x='Maximum Longevity - log(yrs) '
  ) +
  theme_pubclean() + 
  labs_pubr()


ggplotly(p.paen)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'