POPULATION ECOLOGY – CHAPTER 52
Population ecology measures changes in population size and composition and
identifies contributing factors. Most populations either remain relatively stable,
with only minor increases and decreases, or they show dramatic increases followed
by equally dramatic decreases. None can grow indefinitely.
I. Characteristics of Populations
Population –
A. Density and dispersion
Every population has geographical boundaries and a population size. Two
important characteristics are density and the spacing of individuals (dispersion).
Population density Population dispersion –
1. Measuring density
It can be impractical or impossible to count all individuals in a population. There
are a variety of sampling techniques to estimate density and total population size.
a.
b.
c. May use the mark-recapture method (Methods box)
i.
ii.
iii.
2. Patterns of dispersion
a. A population's geographical range –
b. Local densities –
c. Individuals exhibit three general patterns of dispersion: clumped, uniform, and
random.
i. A clumped pattern is when individuals are aggregated in patches.
ii. A uniform pattern is when the spacing of individuals is even.
iii. A random pattern is when individual spacing varies in an unpredictable way.
d. The above applies to local dispersion patterns within populations, but it is
important to remember that populations within a species also show dispersion
patterns.
i.
ii. Biogeography –
B. Demography is the study of factors that affect birth and death rates in a
population
1. Demography a.
b.
c.
2. Life tables and survivorship curves
a.
i.
ii.
b. Survivorship curves plot the numbers in a cohort still alive at each age. There
are three general types of survivorship curves (Figure 52.5):
i. Type I curves –
•
ii. Type II curves •
iii. Type III curves •
c. Many species exhibit curves between the basic survivorship curves. Some have
more complex curves.
i.
ii.
3. Reproductive rate
a.
b.
•
III. Life Histories
Read pages 1141 – 1143 to prepare for the next section on population growth
models.
IV. Population Growth Models
A. An exponential model of population growth describes an idealized population
with access to unlimited resources.
1.
2.
3. Change in population size during time interval = Births during time interval Deaths during time interval
In a mathematical form, this equation becomes:
N/t = B - D
Where
N = population size
t = time
B = absolute number of births during the time interval
D = absolute number of deaths during the time interval
N = change in population size
t = the time interval (lifespan or generation time).
4. Populations differ in size. The model must be adjusted to apply it to any
population. This adjustment is a conversion from absolute birth and death rates to
average numbers of births and deaths per individual during the specified time
interval.
•
•
5. Including average birth and death rates alters the previous equation so that
expected numbers of births and deaths can be predicted for any population size:
N/t= bN - mN
• The change in size of our 1000 individual population would be:
N/t = (0.034)(1000) - (0.016)(1000)
N/t = 34 - 16
N/t = 18
• If another population of the same species contained 1500 individuals, then:
N/t = (0.034)(1500) - (0.016)(1500)
N/t = 51 - 24 
N/t = 27
6. Population ecologists study overall changes in population sizes and use r to
represent the difference between per capita birth rates and per capita death rates:
r = b – m; so N/t = rN
•
• Zero population growth (ZPG) –
•
7. Many population ecologists use a slightly different equation to express population
growth in terms of instantaneous growth rates:
N/t = rN
• This expresses the population growth over very short time intervals.
8.
• The maximum population growth rate is called the intrinsic rate of increase and is
designated by rmax .
9. Exponential population growth is population increase under ideal conditions due
to the intrinsic rate of increase. It is expressed as:
N/t = rmaxN
• The size of the population increases rapidly due to ideal conditions of unlimited
resources.
• Produces a J-shaped growth curve (Figure 52.9)
•
10.
•
•
Exponential growth is characteristic of populations introduced into a new or
unfilled environment or whose numbers have been decimated by a catastrophe and
are rebounding.
B. A logistic model of population growth incorporates carrying capacity
Indefinite increases in population size do not occur.
A population may increase rapidly from a low level under favorable environmental
conditions, but this increase in numbers will eventually approach the level where
resources become limiting, and growth will stop.
1. The carrying capacity (K) –
a. Carrying capacity –
b. Carrying capacities - determined by many factors:
i.
ii.
c. Crowding and resource limitation can affect population growth rate.
i.
ii.
iii.
2. The logistic growth equation
a.
b.
i. The equation for logistic population growth is:
N/t = rmax N [(K - N)/K]
K = carrying capacity; the maximum sustainable population.
K - N= the number of new individuals the environment can accommodate.
(K - N)/K = percentage of K available for population growth.
Multiplying rmax by (K - N)/K reduces the value of r as N increases (Table 52.3)
The actual growth rate of a population of any size is rmax N [(K - N)/K]
ii. The implications of the logistic growth equation at varying population sizes for a
growing population are:
• When N is low:
• When N is large and resources are limiting:
• When N = K:
iii. The logistic model of population growth produces a sigmoid (S-shaped) growth
curve (Figure 52.14).
•
• As N approaches K –
c. The logistic model is density-dependent –
3. How well does the logistic model fit the growth of real populations?
a.
i.
ii.
b.
c. Some assumptions of the logistic model do not hold true for all populations. For
example, the logistic model assumes that:
i. Even at low levels, each individual added may have the same negative effect on
population growth rate; any increase in N reduces (K – N)/N.
•
--- Example:
--- Example:
•
ii. Populations approach carrying capacity smoothly.
•
•
d. Some populations appear to fluctuate about a general carrying capacity.
e.
•
The logistic model fits few, if any, real populations. But, it does incorporate ideas
that apply to many.
3. Population growth models and life histories
a. The logistic population model predicts that there will be different growth rates
for populations with high vs. low densities in relation to carrying capacity.
i.
ii.
b. Different life history adaptations would be favored under high densities and low
densities. For example:
i. Under high population densities –
•
ii. Under low population densities •
c. This leads to the concept of K-selected (equilibrium populations) and r-selected
(opportunistic populations) traits of life history strategies.
i.
•
•
ii.
BE SURE you know the traits you would see in populations having each type of life
history: K- versus r-selected.