4.2 Demography and Life History Strategies

Demographic Processes

Population Dynamics and Structure

Demography is the study of how populations change in size, structure, and distribution over time. In ecology, it applies to any species, not just humans.

Age structure diagrams (also called population pyramids) show how individuals are distributed across age groups. These diagrams do two things: they reflect past demographic events (like a baby boom or a disease outbreak) and they help predict future population trends. A population with lots of young individuals will likely grow, while one skewed toward older individuals may decline.

Carrying capacity (K) is the maximum population size an environment can sustain indefinitely, given available resources like food, water, space, and shelter. Populations can temporarily overshoot K, but they'll typically crash back down.

Key Demographic Factors

Four processes drive population change:

• Birth rates add individuals to the population
• Death rates remove individuals from the population
• Immigration brings individuals in from other populations
• Emigration sends individuals out to other populations

The balance among all four determines whether a population grows, shrinks, or stays stable. A simple way to think about it: if births + immigration > deaths + emigration, the population grows.

Demographic Transition Model

The demographic transition model describes how birth and death rates shift as societies develop economically. While this model comes from human demography, it illustrates a broader ecological principle: environmental conditions shape reproductive and survival patterns.

The four stages are:

1. Pre-transition: Both birth and death rates are high, so population growth is slow
2. Early transition: Death rates drop (better nutrition, sanitation, medicine), but birth rates stay high, causing rapid population growth
3. Late transition: Birth rates begin to fall as well, slowing growth
4. Post-transition: Both birth and death rates are low, and population size stabilizes

Demographic Rates

Birth and Death Rates

Crude birth rate (CBR) is the number of live births per 1,000 individuals per year:

$CBR = \frac{\text{Number of live births}}{\text{Total population}} \times 1000$

For example, a population of 50,000 with 750 births in a year has a CBR of 15 per 1,000.

Crude death rate (CDR) is the number of deaths per 1,000 individuals per year:

$CDR = \frac{\text{Number of deaths}}{\text{Total population}} \times 1000$

A population of 100,000 with 800 deaths in a year has a CDR of 8 per 1,000.

Infant mortality rate (IMR) measures deaths of infants under one year old per 1,000 live births:

$IMR = \frac{\text{Number of infant deaths}}{\text{Number of live births}} \times 1000$

If there are 50 infant deaths in a year with 10,000 live births, the IMR is 5 per 1,000. IMR is often used as an indicator of overall population health because infant survival is highly sensitive to environmental conditions.

These rates are called "crude" because they don't account for age structure. A population with many elderly individuals will naturally have a higher CDR even if conditions are good.

Fertility and Reproduction Rates

Total fertility rate (TFR) is the average number of children a female would produce over her lifetime if current age-specific fertility rates stayed constant. In human demography, a TFR of about 2.1 is considered replacement level, the rate needed to keep population size stable (the extra 0.1 accounts for individuals who don't survive to reproduce).

Age-specific fertility rates measure births per 1,000 females in a particular age group per year. For instance, women aged 25–29 typically have higher fertility rates than women aged 40–44. These rates matter because they reveal when in life reproduction peaks.

Net reproductive rate ($R_0$) combines fertility and survival data to measure the average number of female offspring a female produces in her lifetime:

• $R_0 > 1$: population is growing
• $R_0 < 1$: population is declining
• $R_0 = 1$: population is stable

The difference between TFR and $R_0$ is that $R_0$ factors in mortality. A species could have a high TFR but still decline if many individuals die before reproducing.

Life Expectancy

Life expectancy at birth is the average number of years a newborn is expected to live given current mortality rates. It varies enormously: in humans, Japan averages about 84 years while the Central African Republic averages around 53 years.

Factors that influence life expectancy include healthcare access, nutrition, predation pressure, disease prevalence, and environmental conditions. In non-human species, life expectancy is closely tied to life history strategy, which brings us to the next section.

Life History Strategies: r vs. K

Every organism faces a fundamental trade-off: put energy into making lots of offspring, or put energy into making fewer but better-cared-for offspring. The r/K selection theory describes two ends of this spectrum.

r-Selected Species Characteristics

r-selected species prioritize rapid reproduction. They tend to:

• Inhabit unstable or unpredictable environments where sudden die-offs are common
• Mature early with short generation times
• Produce many offspring per reproductive event
• Invest little parental care in each individual offspring
• Have short lifespans
• Show boom-and-bust population cycles

Examples: Bacteria can divide every 20 minutes. Fruit flies reach maturity in about 10 days and lay hundreds of eggs. Dandelions release thousands of seeds per plant. The strategy is essentially "produce as many offspring as possible and hope some survive."

K-Selected Species Characteristics

K-selected species prioritize survival and offspring quality. They tend to:

• Live in stable, predictable environments
• Maintain populations near carrying capacity (K)
• Mature late with long generation times
• Produce few offspring per reproductive event
• Invest heavily in parental care
• Have long lifespans

Examples: Elephants have a 22-month gestation and raise calves for years. Whales produce a single calf and nurse it for months. Humans invest decades in each offspring. The strategy is "produce few offspring but give each one a strong chance of surviving."

Evolutionary Trade-offs and Adaptations

The r/K framework is a continuum, not an either/or classification. Many species fall somewhere in the middle. Sea turtles, for instance, lay many eggs (r-selected trait) but are long-lived (K-selected trait).

The core trade-off is between reproduction and survival: energy spent on producing offspring is energy not spent on growth, immune function, or self-maintenance. Environmental pressures shape where a species lands on this continuum:

• High predation or frequent disturbance favors r-selected traits (reproduce fast before you die)
• Stable environments with strong competition favor K-selected traits (invest in competitive offspring)
• Resource availability and climate stability also push species toward one end or the other

This framework matters for conservation. r-selected species can bounce back quickly from population crashes, while K-selected species recover slowly and are more vulnerable to extinction from habitat loss or overharvesting.

Population Growth Models

Exponential Growth Model

The exponential growth model describes what happens when a population has unlimited resources and no constraints. Growth accelerates over time, producing a J-shaped curve.

The equation:

$\frac{dN}{dt} = rN$

• $N$ = population size
• $t$ = time
• $r$ = intrinsic rate of natural increase (birth rate minus death rate under ideal conditions)

The bigger the population gets, the faster it grows, because more individuals are reproducing. This model applies to real situations like bacteria colonizing a fresh culture medium or an invasive species entering a new habitat with no predators. But no population grows exponentially forever; eventually, resources run out.

Logistic Growth Model

The logistic growth model is more realistic because it accounts for carrying capacity. As the population approaches K, growth slows and eventually levels off, producing an S-shaped curve.

The equation:

$\frac{dN}{dt} = rN\left(\frac{K - N}{K}\right)$

• $K$ = carrying capacity

The term $\frac{K - N}{K}$ is what makes this different from exponential growth. When $N$ is small relative to $K$, this fraction is close to 1, and growth is nearly exponential. As $N$ approaches $K$, the fraction approaches 0, and growth stalls. If $N$ ever exceeds $K$, the fraction becomes negative, meaning the population shrinks.

A real-world example: a deer population introduced to a forest grows quickly at first when food is abundant, then levels off as competition for browse intensifies.

Factors Affecting Population Growth

Two categories of factors regulate population size:

Density-dependent factors become stronger as population density increases:

• Competition for food, water, and space
• Increased predation (more prey attracts more predators)
• Faster disease transmission in crowded populations

Density-independent factors affect populations regardless of size:

• Natural disasters (earthquakes, floods, volcanic eruptions)
• Extreme weather events (droughts, harsh winters)
• Habitat destruction

Most real populations are shaped by both types simultaneously. Ecologists use these growth models to predict population trends, guide wildlife management decisions, and assess how environmental changes will affect species over time.