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General Ecology · Population Ecology

Population Demography and Life Tables

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On this page 7 sections
  1. In 30 seconds
  2. Why this matters
  3. The college version
  4. Eli explains
  5. Worked example
  6. Key takeaway
  7. Study tools

In 30 seconds

is the quantitative study of a population's vital statistics — births, deaths, ages, and sexes. Its central tools are life tables, which track survivorship (how many survive to each age) and fecundity (how many offspring are produced at each age). Survivorship data are summarized as curves: Type I (most die old), Type II (constant mortality), and Type III (heavy early mortality). Together with generation time and , life tables let ecologists project growth and identify which life stages matter most for conservation.

Why this matters

Life tables and survivorship data are among conservation's most practical tools. The points to the stage where intervention helps most: protecting eggs and juveniles for a Type III species, versus protecting breeding adults for a Type I species. Reproductive value shows which age classes, if lost, remove the most future reproduction — central to managing harvested or endangered populations. Static life tables underpin assessments of whether a population is stable, growing, or collapsing. Actual decisions are governed by permits, wildlife regulations, and, where relevant, Indigenous land and data sovereignty; this content is conceptual and educational, not operational field guidance.

The college version

1. Age structure and sex ratio

Demography begins with composition. is the distribution of individuals across age classes (pre-reproductive, reproductive, post-reproductive), often drawn as an age pyramid; a broad base of young signals growth, a narrow one decline. is the proportion of males to females; because females typically bear young, the number of reproductive females often limits growth. A is a group born in the same period, followed through life — the basis of a .

2. Life tables and survivorship

A is a schedule of age-specific survival and reproduction. A cohort life table follows one cohort from birth until all die — accurate but slow. A samples all ages at one moment and reconstructs survival from the age distribution, assuming a stable population — fast but assumption-dependent. is the proportion of the cohort alive at age x; is the average offspring per individual of age x, and the lists m(x) across ages. Survivorship curves plot the logarithm of survivors against age:

  • Type I survivorship — most survive to old age (humans, elephants).
  • Type II survivorship — constant mortality across ages (many birds, small mammals).
  • Type III survivorship — heavy early mortality, high later survival (fish, invertebrates, trees).

3. Generation time, reproductive value, projection

Generation time (T) is the average age at which an individual reproduces — the pace of population turnover. Reproductive value is the expected future contribution of an individual of a given age to the population; low at birth (many die first), highest around first reproduction. Population projection uses life-table data to forecast future size and age structure, typically by multiplying each age class's survivorship and fecundity forward (matrix models, conceptually). Interpretation limits matter: tables describe averages under assumed-constant conditions, and projections fail when survival or fecundity changes with density, environment, or chance. Conservation applications follow directly — targeting the vulnerable life stage, or the age class with the highest reproductive value, is far more effective than untargeted effort.

How it works

  1. Record age structure and sex ratio to gauge composition.
  2. Build a life table — cohort (follow one group) or static (one cross-section).
  3. Compute survivorship l(x) for each age.
  4. Compute the fecundity schedule m(x).
  5. Plot a survivorship curve and classify it as Type I, II, or III.
  6. Calculate R₀ and generation time T.
  7. Use reproductive value to find the stages contributing most to the future.
  8. Project future size and age structure, stating the assumptions.

Common confusions

Do not confuseWithDifference
Cohort life tableStatic life tableFollows one group vs. one cross-section
SurvivorshipFecundityChance of being alive vs. offspring per survivor
Type IType III survivorshipMost die old vs. most die young
Age structureSex ratioAges vs. males-to-females
Generation timeLifespanAge of reproduction vs. how long one lives
R₀rLifetime offspring per individual vs. per capita growth rate

Memory aids

"I Live Long, III Die Young" — Type I species reach old age; Type III lose most young early. For tables, "l for live, m for make babies," and R₀ is the sum of "live × make."

Quick review

Topic Recap

  • Demography quantifies births, deaths, age structure, and sex ratio.
  • Life tables record survivorship l(x) and fecundity m(x) across ages.
  • Cohort tables follow one group; static tables use one cross-section.
  • Survivorship curves come in Type I, II, and III shapes tied to strategy.
  • R₀ = Σ l(x) m(x) signals growth (above 1) or decline (below 1).
  • Generation time sets turnover speed; reproductive value flags key stages.
  • Projections are averages with assumptions; conservation targets vulnerable stages.

Knowledge Check

  1. What does a survivorship curve plot, and what are its three classic shapes?
  2. A species lays thousands of tiny eggs with no care. Which type is it, and why?
  3. What is the difference between a cohort and a static life table?
  4. If R₀ is 0.8, is the population growing or declining?
  5. Why is reproductive value highest around first reproduction rather than at birth?

Answers and Rationales

  1. It plots the logarithm of survivors against age. Type I shows most reaching old age; Type II constant mortality; Type III heavy early mortality.
  2. Type III — many small, uncared-for offspring means most die young, with few surviving to reproduce.
  3. A cohort table follows one same-aged group from birth to death; a static table samples all ages at once and reconstructs survival, assuming stability.
  4. Declining — R₀ is average lifetime offspring per individual; 0.8 means each replaces itself with fewer than one offspring.
  5. At birth many die before reproducing, so expected future contribution is low; surviving to reproductive age raises it by passing the risky early stage.
Eli, the EliExplains learning guide

Eli explains

The same idea, in plain words

Explain it like I’m 10

A life table is like a class yearbook that follows one group forward, recording who is still around at each age and how many children each person has. Survivorship is the "still here" column; fecundity is the "children" column.

The comparison stops being exact because a life table describes averaged outcomes for a cohort, not any individual's fate, and it treats births and deaths as predictable averages rather than chance events. These tables matter because they reveal whether a population is growing or shrinking, which age classes are vulnerable, and where conservation effort — protecting juveniles or breeding adults — pays off most.

Simple Example

An elephant life table shows most calves surviving to adulthood, slow reproduction over many years, and mortality concentrated in old age — a Type I survivorship curve.

Worked example

Building and using a cohort life table:

  1. Define a cohort of N₀ newborns.
  2. Track survivorship l(x) — the proportion alive at each age x (l(0) = 1, falling toward 0).
  3. Record the fecundity schedule m(x) — offspring per surviving individual at age x.
  4. Compute the net reproductive rate:

R0 = ∑l(x) m(x)

where l(x) is survivorship and m(x) is fecundity at age x. R₀ > 1 means growth; R₀ < 1 means decline.

  1. Estimate generation time:

T = ∑x l(x) m(x)∑l(x) m(x)

where x is age, weighting each age by its contribution to reproduction.

These are deterministic averages — no density dependence, environmental variability, or individual variation — so R₀ and T are model outputs, not observations. Static life tables additionally assume stable age distribution and constant rates, which real populations often violate.

Key takeaways

  • High yield: Demography = the vital statistics of a population.
  • High yield: Cohort tables follow one group; static tables use one cross-section and assume stability.
  • High yield: Survivorship curves are Type I (late mortality), Type II (constant), Type III (early mortality).
  • High yield: Type III species make many small, uncared-for offspring; Type I make few, cared-for young.
  • High yield: R₀ = Σ l(x) m(x); above 1 grows, below 1 declines.
  • High yield: Generation time T is average age of reproduction; short T allows faster growth.
  • High yield: Reproductive value peaks around first reproduction — the stage conservation often targets.
  • Age-structure pyramids hint at future growth before births are counted.
  • Life-table projections assume constant conditions; they are models, not guarantees.

Keep learning

Ready to build on this? Continue to the next lesson.

Study tools & related lessonsYou’ll learn to · Key vocabulary · Related

You’ll learn to

  • Define demography and explain the roles of age structure and sex ratio.
  • Distinguish cohort and static life tables and what each provides.
  • Explain survivorship curves and the three classic types with examples.
  • Describe fecundity schedules, generation time, and reproductive value in projecting population growth.

Key vocabulary

Demography
Study of births, deaths, ages, sexes
Age structure
Distribution across age classes
Sex ratio
Proportion of males to females
Cohort
Group born together, followed through life
Life table
Schedule of age-specific survival/reproduction
Static life table
Rebuilt from one cross-section of ages
Cohort life table
Follows one cohort to death
Survivorship l(x)
Proportion alive at age x
Fecundity m(x)
Offspring per individual at age x
Fecundity schedule
Fecundity across ages
Survivorship curve
Plot of log survivors vs. age
Type I survivorship
Most die old
Type II survivorship
Constant mortality
Type III survivorship
Heavy early mortality
Generation time (T)
Average age of reproduction
Reproductive value
Expected future contribution by age
Population projection
Forecasting future size/age structure
Interpretation limits
Tables assume constant averages
Conservation applications
Targeting vulnerable stages

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