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Chapter 52
Population Ecology
Population Dynamics

Survivorship Curves
Data in a life table can be represented
graphically by a survival curve.
Curve usually based on a standardized
population of 1000 individuals and the X-
axis scale is logarithmic.

Survivorship curves can be classified into three
general types
Type I, Type II, and Type III
Figure 52.5
I
II
III
50 100
0
1
10
100
1,000
Percentage of maximum life span
Number
of
survivors
(log
scale)

Type I curve
Type I curve typical of animals that produce
few young but care for them well (e.g.
humans, elephants). Death rate low until
late in life where rate increases sharply as a
result of old age (wear and tear,
accumulation of cellular damage, cancer).

Type II curve
Type II curve has fairly steady death rate
throughout life (e.g. rodents).
Death is usually a result of chance processes
over which the organism has little control
(e.g. predation)

Type III curve
Type III curve typical of species that produce
large numbers of young which receive little or no
care (e.g. Oyster).
Survival of young is dependent on luck. Larvae
released into sea have only a small chance of
settling on a suitable substrate. Once settled
however, prospects of survival are much better
and a long life is possible.

Population growth
Occurs when birth rate exceeds death rate
(duh!)
Organisms have enormous potential to
increase their populations if not constrained
by mortality.
Any organism could swamp the planet in a
short time if it reproduced without restraint.

Per Capita Rate of Increase
If immigration and emigration are ignored,
a population’s growth rate (per capita
increase) equals the per capita birth rate
minus the per capita death rate

Equation for population growth is
ΔN/Δt = bN-dN
Where N = population size, b is per capita
birth rate and d is per capita death rate.
ΔN/Δt is change in population N over a
small time period t.

The per capita rate of population increase is
symbolized by r.
r = b-d.
r indicates whether a population is growing
(r >0) or declining (r<0).

Ecologists express instantaneous population
growth using calculus.
Zero population growth occurs when the
birth rate equals the death rate r = 0.
The population growth equation can be
expressed as dN
dt
 rN

Exponential population growth
(EPG)
Describes population growth in an
idealized, unlimited environment.
During EPG the rate of reproduction is at its
maximum.

The equation for exponential population
growth is
dN
dt
 rmaxN

The J-shaped curve of exponential growth
Is characteristic of some populations that are
rebounding
1900 1920 1940 1960 1980
Year
0
2,000
4,000
6,000
8,000
Elephant
population

Logistic Population Growth
Exponential growth cannot be sustained for
long in any population.
A more realistic population model limits
growth by incorporating carrying capacity.
Carrying capacity (K) is the maximum
population size the environment can support

The Logistic Growth Model
In the logistic population growth model the
per capita rate of increase declines as
carrying capacity is approached.
We construct the logistic model by starting
with the exponential model and adding an
expression that reduces the per capita rate of
increase as N increases

The logistic growth equation includes K, the
carrying capacity (number of organisms
environment can support)
As population size (N) increases, the equation ((K-N)/K)
becomes smaller which slows the population’s growth
rate.

Logistic model produces a sigmoid (S-shaped) population
growth curve.

Logistic model predicts different per capita growth
rates for populations at low and high density. At
low density population growth rate driven
primarily by r the rate at which offspring can be
produced. At low density population grows
rapidly.
At high population density population growth is
much slower as density effects exert their effect.

800
600
400
200
0
Time (days)
0 5 10 15
(a) A Paramecium population in the lab.
The growth of Paramecium aurelia in
small cultures (black dots) closely
approximates logistic growth (red curve)
if the experimenter maintains a constant
environment.
1,000
Number
of
Paramecium/ml
The Logistic Model and Real
Populations
The growth of laboratory populations of
paramecia fits an S-shaped curve

Some populations overshoot K before settling down
to a relatively stable density
Figure 52.13b
180
150
0
120
90
60
30
Time (days)
0 160
140
120
80 100
60
40
20
Number
of
Daphnia/50
ml
(b) A Daphnia population in the lab. The growth of a population of Daphnia in a
small laboratory culture (black dots) does not correspond well to the logistic
model (red curve). This population overshoots the carrying capacity of its artificial
environment and then settles down to an approximately stable population size.

Some populations fluctuate greatly around K.
0
80
60
40
20
1975 1980 1985 1990 1995 2000
Time (years)
Number
of
females
(c) A song sparrow population in its natural habitat. The population of
female song sparrows nesting on Mandarte Island, British Columbia, is
periodically reduced by severe winter weather, and population growth is
not well described by the logistic model.

K-selection, or density-dependent selection
Selects for life history traits that are sensitive
to population density
r-selection, or density-independent selection
Selects for life history traits that maximize
reproduction

The concepts of K-selection and r-selection
have been criticized by ecologists as
oversimplifications.
Most organisms exhibit intermediate traits
or can adjust their behavior to different
conditions.

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Population dynamics

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