Download Population Ecology

Chapter 53
Population Ecology
AP Biology
•
Population ecology is the study of populations in relation to their environment
–
•
It explores how biotic and abiotic factors influence:
•
Density
•
Distribution
•
Age structure
•
Population size
In this chapter, we will examine:
–
Population structure and dynamics
–
Tools and models ecologists use to analyze populations
–
Factors that regulate the abundance of organisms
–
Recent trends in the size and makeup of the human population
Concept 53.1:
Dynamic biological processes influence
population density, dispersion, and
demographics
•
A population is a group of individuals of a single species living in the same general
area
–
–
Members of a population:
•
Rely on the same resources
•
Are influenced by similar environmental factors
•
Are likely to interact and breed with one another
Populations can evolve as natural selection acts on heritable variations among
individuals and changes the frequencies of various traits over time
–
3 fundamental characteristics of populations are:
•
Density
•
Dispersion
•
Demographics
Density and Dispersion
• Populations can be described in terms of its density and
dispersion
– Density is the number of individuals per unit area or
volume
• Ex) The number of E.coli bacteria per milliliter in a test
tube
– Dispersion is the pattern of spacing among individuals
within the boundaries of the population
Density: A Dynamic Perspective
•
In most cases, it is impractical or impossible to count all individuals in a population
–
Instead, ecologists use a variety of sampling techniques to estimate densities
and total population sizes
–
Population size can be estimated by:
•
Extrapolation from small samples
– Ex) Count the number of oak trees in several randomly located 100 x
100 meter plots and then calculate the average density in these
samples
•
This information can then be extrapolated to estimate the
population size in the entire area
•
An index of population size
–
•
Ex) Number of nests, burrows, tracks, calls, or fecal droppings
The mark-recapture method
•
Scientists typically begin the mark-recapture method by capturing a random sample
of individuals in a population
–
They then tag, or “mark,” each individual and release it
•
–
With some species, researchers can identify individuals (ex: distinctive
markings) without physically capturing them
After a few days to weeks, once these marked individuals mix back into the
population, scientists capture and sample a second set of individuals
•
Population size (N) can then be estimated using the following equation:
–
N = mn/x, where:
•
N = estimated population size
•
m = total number of individuals marked and released in the 1st
sampling
•
n = total number of animals recaptured in the 2nd sampling
•
x = number of animals recaptured in the 2nd sampling
•
•
The mark-recapture method assumes that:
–
Marked and unmarked individuals have the same probability of being captured
(sampled)
–
The marked organisms have mixed completely back into the population
–
No individuals are born, die, immigrate, or emigrate during the resampling
interval
Sample problem: Researchers want to determine the estimated size of an
endangered population of dolphins
Fig. 53-2
–
They identify 180 dolphins by photographing theirAPPLICATION
distinctive dorsal fins from a
boat
–
A few weeks later, the researchers
encounter 44 dolphins in this population,
7 of which had been photographed before
–
What is the estimated number of dolphins
in this population?
•
Density is not a static (unchanging) property
–
It changes as individuals are added or removed from a population
• Additions occur through birth and immigration
– Immigration is the influx of new individuals from other areas
• Factors that remove individuals from a population are death and
emigration
– Emigration is the
Fig. 53-3
Births
Deaths
movement of individuals
out of a population
Births and immigration
add individuals to
a population.
Immigration
Deaths and emigration
remove individuals
from a population.
Emigration
Patterns of Dispersion
•
Spacing of individuals within a population of a specific density may vary substantially
–
Patterns of dispersion within a given geographical area are influenced by
•
Environmental factors
–
Ex) Some patches of habitat within the geographic range of a
population are more suitable than others
•
–
Social factors – interactions between members of the population
There are 3 common patterns of dispersion seen in a population’s geographic
range:
•
Clumped
•
Uniform
•
Random
•
In a clumped pattern of dispersion, individuals aggregate in patches
–
A clumped dispersion may be influenced by:
•
Resource availability
–
Ex) Plants and fungi are often clumped where soil conditions and
other environmental factors favor germination and growth
•
Behavior
–
Ex) A wolf pack is more likely than a single wolf to subdue a moose or
Fig. 53-4a
other large prey animals
–
Ex) Sea stars group together in
tide pools so they may breed
successfully
Video: Flapping Geese (Clumped)
(a) Clumped
•
A uniform dispersion is one in which individuals are evenly distributed
–
Animals often exhibit uniform dispersion as a result of antagonistic social
interactions:
•
Ex) Some plants secrete chemicals that inhibit growth and germination of
nearby individuals that could compete for resources
•
Fig. 53-4b
Territoriality: the defense
of a bounded physical space against
encroachment by other individuals
Video: Albatross Courtship (Uniform)
(b) Uniform
•
In a random dispersion, the position of each individual is unpredictable and
independent of other individuals
–
It occurs:
•
In the absence of strong attractions or repulsions among individuals of a
population
•
Where key physical or chemical factors are relatively homogeneous (the
same) across the geographical area
Fig. 53-4c
–
Ex) Many plants that grow from windblown seeds (ex: dandelions)
have random distribution due to
the unpredictability of where their
seeds will land
Video: Prokaryotic Flagella (Salmonella typhimurium)
(c) Random
Demographics
•
Demography is the study of the vital statistics of a population and how they change
over time
–
•
Death rates and birth rates are of particular interest to demographers
A useful way of summarizing the vital statistics of a population is with a life table
–
Table 53-1
A life table is an age-specific summary of the survival patterns of a population
•
•
The best way to construct a life table if to follow the fate of a cohort, a group of
individuals of the same age, from birth until they are all dead
–
The number of individuals that die in each age group is tracked during this time
–
From this, the proportion of the cohort surviving from one age to the next can
be calculated
Table 53-1
The life table of Belding’s
ground squirrels reveals many things about this population
–
Ex) A comparison
of the 5th and 10th
columns reveals
that males have a
higher death rate
than females
Survivorship Curves
A survivorship curve is a graphic way of representing the data in a life table
–
This curve plots the proportion or number of individuals in a cohort still alive at
each age
•
In general, a survivorship curve is constructed with a cohort of a specified
size (ex: 1,000 individuals)
•
To obtain this number for a specific population, we can multiply the
proportion of individuals alive at the start of each year by 1,000
Fig. 53-5
–
This gives the number of
individuals within the
cohort that are still alive at
the start of each year
–
These numbers can then
be plotted against age
for both males and females
1,000
Number of survivors (log scale)
•
100
Females
10
Males
1
0
2
4
6
Age (years)
8
10
The survivorship curve for Belding’s ground squirrels shows a relatively
constant death rate
–
It also indicates a lower overall survival rate for males as compared to
Fig. 53-5
females
1,000
Number of survivors (log scale)
•
100
Females
10
Males
1
0
2
4
6
Age (years)
8
10
Survivorship curves can be classified into three general types:
–
Type I: low death rates (flat) during early and middle life, then an increase
(drops steeply) among older age groups
•
–
Type II: the death rate is constant (straight line) over the organism’s life span
•
–
Many large mammals, including humans, that produce few offspring but
with much parental care exhibit this type of curve
Occurs mainly in rodents, various invertebrates, some lizards, and some
annual plants
Type III: high death rates (sharp drop) for the young, then a slower (flattens)
death rate for survivors
Fig. 53-6
•
This type of curve is usually
associated with organisms that
produce very large numbers of
offspring but provide to to no
parental care
–
Ex) Long-lived plants,
many fishes, and most
marine invertebrates
Number of survivors (log scale)
•
1,000
I
100
II
10
III
1
0
50
Percentage of maximum life span
100
• Many species fall somewhere between these basic types of
survivorship or show more complex patterns
– Ex) Mortality is often high among the youngest members of
bird population (as in a Type III curve), but it becomes fairly
constant among adults (as in a Type II curve)
– Ex) Some invertebrates (crabs) may show a “stair-
stepped” curve, with brief periods of increased mortality
during molts, followed by periods of lower mortality
experienced while their exoskeleton in hard
Reproductive Rates
•
In populations not experiencing immigration or emigration, survivorship is
only one of two key factors that affect population size
–
The other key factor is reproductive rate
Table 53-2
• Demographers therefore
often view populations in
terms of females giving rise
to new females, since only
they can produce offspring
–
A reproductive table, or fertility
schedule, is an age-specific
summary of the reproductive
in a population
rates
•
A reproductive table is constructed by measuring the reproductive output of
a cohort from birth until death
–
The average number of female offspring is calculated by multiplying the
proportion of females at each that are breeding by the average number
Table 53-2
of females in the litters of those
females
•
Reproductive tables can help identify
reproductive patterns of a population
–
Ex) For Belding’s ground
squirrels, reproductive output
rises to a peak at age 4 and
then falls off in older females
Concept Check 53.1
• 1) One species of forest bird is highly territorial, while a second lives
in flocks. Predict each species’ likely pattern of dispersion, and
explain.
• 2) Each female of a particular fish species produces millions of eggs
per year. Draw and label the most likely survivorship curve for this
species, and explain your choice.
• 3) As noted in Figure 53.2 (pp. 1175), an important assumption of the
mark-recapture method is that marked individuals have the same
probability of being recaptured as unmarked individuals. Describe a
situation where this assumption might not be valid, and explain how
the estimate of population size would be affected.
Concept 53.2:
Life history traits are products of
natural selection
•
An organism’s life history consists of the traits that affect its schedule of
reproduction and survival, from birth through death, including:
•
–
The age at which reproduction begins
–
How often the organism reproduces
–
How many offspring are produced during each reproductive cycle
With the exception of humans, organisms do not choose consciously when
to reproduce or how many offspring to have
–
Instead, these traits are evolutionary outcomes reflected in the
development, physiology, and behavior of these organisms
Evolution and Life History Diversity
•
Life histories are very diverse
–
Species that exhibit semelparity, or big-bang reproduction, reproduce once
and die
•
Ex) Spawning salmon produce 1000s of eggs in a single reproductive
opportunity before they die
•
Ex) The agave (“century”) plant grows for years before sending up a single
Fig. 53-7
large flowering stalk that produces
then dies
–
Species that exhibit iteroparity, or repeated
reproduction, produce offspring repeatedly
•
Ex) Many animals produce annually for
many years
seeds, and
•
There appear to be 2 critical factors that contribute to the evolution of semelparity
versus iteroparity
•
–
The survival rate of the offspring is low
–
The likelihood that the adult will survive to reproduce again
Highly variable or unpredictable environments tend to have low offspring survival
rates and thus generally favor big-bang reproduction,
–
Dependable, more stable, environments tend to favor repeated reproduction,
since individuals are more likely to survive to reproductive age
•
In these cases, competition for resources may be intense, meaning that
fewer, but larger and more well-provisioned offspring should have a better
chance of surviving to reproductive age
–
Many organisms also have life histories that are intermediate between the two
extremes of semelparity and iteroparity
•
Ex) Oak trees live a long time but produce relatively large numbers of
offspring
“Trade-offs” and Life Histories
•
Organisms have finite resources, which may lead to trade-offs between survival and
reproduction
–
Selective pressures influence the trade-off between the number and size of
offspring
•
Plants and animals whose young are subject to high mortality rates often
produce large numbers of relatively small offspring
–
Ex) Most weedy plants (ex: dandelions) grow quickly and produce a
large number of seeds to ensure that at
least some seeds will eventually grow
and reproduce
Fig. 53-9
•
In other organisms, extra investment on the
part of the parent greatly increases the
offspring’s chances of survival
–
(a) Dandelion
Ex) Some plants (ex: coconut palm)
produce a more moderate number of
very large seeds that contain enough
endosperm to ensure the success of
most of their offspring
(b) Coconut palm
Concept Check 53.2
•
1) Consider 2 rivers: one is spring fed and has a constant water volume and
temperature year-round; the other drains a desert landscape and floods and
dries out at unpredictable intervals. Which river would you predict is more
likely to support many species of iteroparous animals? Why?
•
2) In the fish called the peacock wrasse (Symphodus tinca), females
disperse some of their eggs widely and lay other eggs in a nest. Only the
latter receive parental care. Explain the trade-offs in reproduction that this
behavior illustrates.
•
3) Mice that cannot find enough food or that experience other forms of stress
will sometimes abandon their young. Explain how this behavior might have
evolved in the context of reproductive trade-offs and life history.
Concept 53.3:
The exponential model describes
population growth in an idealized,
unlimited environment
• Populations of all species, regardless of their life histories, have
the potential to expand greatly when resources are abundant
– Though unlimited growth does not occur for long in nature,
it is useful to study population growth in such an idealized
situation
• These situations help us understand:
– The capacity of species to increase
– The conditions that may facilitate this growth
Per Capita Rate of Increase
•
Consider a population consisting of a few individuals living in an ideal, unlimited
environment:
–
–
Under these conditions, there are no environmental restrictions on the abilities
of individuals to:
•
Harvest energy
•
Grow
•
Reproduce
The population will increase in size with every birth (B) and immigration event
(I)
•
It will decrease in size with every death (D) and emigration event (E)
•
Thus, the change in population size ( P ) during a fixed time interval can
be calculated using the formula:
–
P = (B + I) – (D + E)
•
If immigration and emigration are ignored, a population’s growth rate over time (t)
equals birth rate minus death rate
P/ t = B- D
•
Next, this simple model can be converted to express births or deaths as an average
number per individual (per capita) during the specified time interval
–
The per capita birth rate (b) is the number of offspring produced per unit time
by an average member of the population
•
Ex) If there are 34 births per year in a population of 1000, the annual per
capita birth rate is 34/1,000 or 0.034
–
From the annual per capita birth rate, we can then calculate the expected
number of births per year in a population of any size, using the formula:
B = bN
•
Ex) For a population of 500 with an annual per capita birth rate of 0.034, B
= 0.034 x 500 = 17 births/year
•
The per capita death rate (d) can be calculated in a similar fashion to determine the
expected number of deaths per unit time in a population of any size:
D = dN
–
Ex) If d = 0.016 per year, then we would expect 16 deaths per year in a
population of 1000 (D = 0.016 X 1000 = 16)
•
Using substitution, we can now revise the population growth equation again:
P/ t = B – D = bN – dN
•
Because population ecologists are most interested in the difference between per
capita birth rate and per capita death rate, known as the per capita rate of increase
(r), we can make one more revision to the equation:
–
If r = b – d, then by substitution:
P/ t = B- D = bN – dN = N(b – d) = rN
• The value of per capita rate rate of increase (r)
indicates if a population is changing size
– If r > 0, the population is growing
– If r < 0, the population is declining
– If r = 0, zero population growth (ZPG)
occurs, where the birth rate equals the death
rate
Exponential Growth
• Exponential (geometric) population growth is population increase
under idealized conditions:
–
All members have access to abundant food
–
All members are free to reproduce at their physiological capacity
• Under these conditions, the rate of reproduction is at its maximum
(rmax), called the intrinsic rate of increase
Fig. 53-10
Thus, the equation for
exponential growth is:
• dN/dt = rmax N
2,000
dN
= 1.0N
dt
Population size (N)
–
1,500
dN
= 0.5N
dt
1,000
500
0
0
5
10
Number of generations
15
Exponential Growth
A graph of this equation results in a J-shaped growth curve when population size is
plotted over time
–
Although the maximum rate of increase is constant, the population accumulates
more new individuals per unit of time when it is large as compared to when it is
small
•
•
The curve therefore gets progressively steeper over time, as N increases
The J-shaped curve of exponential growth is characteristic of some populations that
are:
Fig. 53-11
–
Introduced into a new environment
–
Rebounding after their numbers have
been drastically reduced by a
catastrophic event
•
Ex) African elephant population of
Kruger National Park in South
Africa after hunting was prohibited
8,000
Elephant population
•
6,000
4,000
2,000
0
1900
1920
1940
Year
1960
1980
Concept Check 53.3
• 1) Explain why a constant rate of increase (rmax) for a population
produces a growth graph that is J-shaped rather than a straight line.
• 2) Where is exponential growth by a plant population more likely – on
a newly formed volcanic island or in a mature, undisturbed rain
forest? Why?
• 3) In 2006, the US had a population of about 300 million people. If
there were 14 births and 8 deaths per 1,000 people, what was the
country’s net population growth that year (ignoring immigration and
emigration, which are substantial)? Do you think the US is currently
experiencing exponential population growth? Explain.
Concept 53.4:
The logistic model describes how a
population grows more slowly as it
nears its carrying capacity
•
Exponential growth cannot be sustained for long in any population because
resources become limited as population increases
–
A more realistic population model limits growth by incorporating carrying
capacity
•
–
Carrying capacity (K) is the maximum population size the environment
can support
Carrying capacity of a given environment varies with the abundance of limited
resources, including:
•
Energy
•
Shelter
•
Refuge from predators
•
Nutrient availability
•
Water
•
Suitable nesting sites
The Logistic Growth Model
•
We can thus modify our mathematical model to incorporate changes in growth rate
as a population nears carrying capacity
–
In the logistic population growth model, the per capita rate of increase
approaches zero as carrying capacity is reached
•
We construct the logistic model by starting with the exponential model and
adding an expression that reduces per capita rate of increase as N
approaches K
dN
dt
–
rmax N
(K
N)
K
If the maximum sustainable population size (carrying capacity) is K,
then K-N is the number of additional individuals the environment can
support
–
The expression (K-N)/N is therefore the fraction of K that is still
available for population growth
•
When N is small compared to K (small population), the term (K-N)/K is large, close to
1
–
In this case, the per capita rate of increase (Rmax(K-N)/K) is close to the
maximum rate of increase predicted by the exponential growth model
•
Table 53-3
As N increases and resources become
limited, however, then (K-N)/K becomes
a small fraction, which in turn decreases
the per capita rate (Rmax(K-N)/K)
–
When N = K, the population
stops growing
The logistic model of population growth produces a sigmoid (S-shaped)
curve when N is plotted over time
–
New individuals are added to the population most rapidly at
intermediate population sizes, during which time:
• The breeding population is of a substantial size
Fig. 53-12
• There is much available
space and resources in
the environment
–
Then population growth rate
then slows dramatically as
N approaches K
Exponential
growth
2,000
Population size (N)
•
dN
= 1.0N
dt
1,500
K = 1,500
Logistic growth
1,000
dN
= 1.0N
dt
1,500 – N
1,500
500
0
0
5
10
Number of generations
15
The Logistic Model and Real Populations
The growth of laboratory populations of some small animals, such as
beetles, crustaceans, bacteria, and paramecia fits an S-shaped curve
–
These organisms are grown in a constant environment lacking
Fig. 53-13a
predators and competitors
• However, these
conditions rarely
occur in nature
Number of Paramecium/mL
•
1,000
800
600
400
200
0
0
5
10
Time (days)
15
(a) A Paramecium population in the lab
Some of the basic assumptions built into the logistic model clearly do not apply to all
populations
–
There is often a lag time before the negative effects of an increasing population
are realized
•
Some populations will thus actually overshoot K before settling down to a
relatively stable density
– Ex) As food becomes limited in a population, females may call on
their energy reserves
to continue reproducing
for a short time
•
If the population then drops
below carrying capacity,
there will be a delay in
population growth until new
offspring are actually born
Fig. 53-13b
Number of Daphnia/50 mL
•
180
150
120
90
60
30
0
0
20
40
60
80 100 120
Time (days)
(b) A Daphnia population in the lab
140
160
• Still other populations fluctuate greatly and make it difficult to define K
–
Some populations show an Allee effect, in which individuals have a
more difficult time surviving or reproducing if the population size is
too small
• Ex) A single plant may be damaged by excessive wind if it is
standing alone, but it would be protected in a clump of
individuals
–
This is contrary to the logistical model of population growth, which
incorporates the idea that, regardless of population density, each
individual added to a population has the same negative effect on
population growth rate
The Logistic Model and Life Histories
•
Different life history traits are favored by natural selection under the different
per capita growth rates predicted for low and high density populations,
relative to their carrying capacity
–
At low population densities, selection favorsadaptations that promote
rapid reproduction should be favored
• Ex) Production of numerous, small offspring
–
At high population densities, selection favors adaptations that allow
organisms to survive and reproduce with few resources
• Ex) Competitive ability and efficient use of resources should be
favored
The Logistic Model and Life Histories
•
Ecologists have attempted to connect these differences in favored traits at different
population densities with the logistic growth model:
–
K-selection, or density-dependent selection, selects for life history traits that
are sensitive to population density
•
K-selection is said to operate in populations living at a density near
carrying capacity, where competition among individuals is relatively strong
–
r-selection, or density-independent selection, selects for life history traits that
maximize reproduction
•
R-selection is said to maximize the per capita rate of increase (r) and
occurs in environments in which population densities are well below
carrying capacity or where individuals face little competition
–
These names follow from the variables of the logistic equation
Concept Check 53.4
• 1) Explain why a population that fits the logistic growth model
increases more rapidly at intermediate size than at relatively small or
large sizes.
• 2) When a farmer abandons a field, it is quickly colonized by fastgrowing weeds. Are these species more likely to be K-selected or Rselected species? Explain.
• 3) Add rows to Table 53.3 (pp. 1184) for three cases where N > K:N
= 1,600, 1,750, and 2,000. What is the population growth rate in
each case? In which portion of Figure 53.13b (pp. 1185) is the
Daphnia population changing in a way that corresponds to the values
you calculated?
Concept 53.5:
Many factors that regulate
population growth are density
dependent
• There are two general questions about regulation
of population growth:
– What environmental factors stop a population from
growing indefinitely?
– Why do some populations show radical
fluctuations in size over time, while others remain
stable?
Population Change and Population Density
•
To understand why a population stops growing, it is helpful to first examine the
effects of population density on birth and death rates
–
Populations can be affected by population density in one of 2 ways
Fig. 53-15
•
–
In density-independent populations, birth rate and Density-dependent
death
birth
rate rate do not
change with population density
DensityBirth or death rate
per capita
•
dependent
death rate
In density-dependent populations, birth rates fall and death rates rise with
population density
Equilibrium
density
density
As a result of various combinations of density-dependent Population
and density(a) Bothand
birth rate
and death
rate vary.
independent regulation, populations may stop growing
reach
equilibrium
53-15
Density-dependent
birth rate
Densityindependent
death rate
Densitydependent
death rate
Equilibrium
density
Population density
(a) Both birth rate and death rate vary.
Equilibrium
density
Population density
(b) Birth rate varies; death rate is constant.
Birth or death rate
per capita
Birth or death rate
per capita
Density-dependent
birth rate
Densityindependent
birth rate
Density-dependent
death rate
Equilibrium
density
Population density
(c) Death rate varies; birth rate is constant.
(b
Density-Dependent Population Regulation
• Density-dependent birth and death rates are an example of
negative feedback that regulates population growth
– They are affected by many factors, such as:
• Competition for resources
• Territoriality
• Disease, predation
• Toxic wastes
• Intrinsic factors
Competition for Resources
• In crowded populations, increasing population density:
– Intensifies competition for resources
– Thus results in a lower birth rate
• Ex) Reproduction by juvenille Soay sheep on Hirta Island drops
dramatically as population size
increases
Percentage of juveniles producing lambs
Fig. 53-16
100
80
60
40
20
0
200
300
400
500
Population size
600
Territoriality
•
In many vertebrates and some invertebrates, competition for territory may
limit population density
–
Maintaining a territory increases
Fig. 53-17
the likelihood of capturing enough
food to reproduce and provides more
opportunity to locate nesting sites
• Ex) Cheetahs are highly
territorial, using chemical
(a) Cheetah marking its territory
communication to warn other
cheetahs of their boundaries
• Ex) Gannets that cannot obtain
a
nesting site do not reproduce
(b) Gannets
Disease
• Population density can influence the health and
survival of organisms
– In dense populations, pathogens can spread
more rapidly
• Ex) In humans, the air-borne lung disease
tuberculosis strikes a greater percentage of people
living in densely populated cities
Predation
• Predation may be an important cause of density-dependent
mortality in prey species
– As a prey population builds up, predators may feed
preferentially on that species
• Ex) Trout may concentrate on a particular species of insect that
is emerging from its aquatic larval stage for a few days and
then switch to eating another insect species that is more
abundant
Toxic Wastes
• Accumulation of toxic wastes can contribute to densitydependent regulation of population size
– In lab cultures of microorganisms, metabolic by-products
accumulate as populations grow, poisoning the organisms
• Ex) The alcohol content of wine is usually less than
13% because this is the maximum concentration of
ethanol that most wine-producing yeast cells can
tolerate
Intrinsic Factors
• For some populations, intrinsic (physiological)
factors appear to regulate population size
– Ex) High population densities in mice can
induce a stress syndrome in which hormonal
changes delay sexual maturation, cause
reproductive organs to shrink, and depress
the immune system
Population Dynamics
• We will now examine why some populations fluctuate
dramatically while others remain relatively stable
– The study of population dynamics focuses on the
complex interactions between biotic and abiotic factors
that cause variation in population size from:
• Year to year
• Place to place
• Season to season
Stability and Fluctuation
Long-term population studies have challenged the hypothesis that
populations of large mammals are relatively stable over time
–
Weather can affect population size over time
• Harsh weather, particularly cold, wet winters weakens Soay sheep
and decreases food availability, leading to decreased population
size
Fig. 53-18
2,100
• Conversely, when sheep
numbers are low and
weather is mild, food is
available and the
1,900
Number of sheep
•
1,700
1,500
1,300
readily
1,100
900
700
population grows quickly
500
0
1955
1965
1975
1985
Year
1995
2005
• Changes in predation pressure can also drive population
fluctuations
– The moose population on Isle Royale in Lake Superior
fluctuates along with that of its main predator, the wolf
Fig. 53-19
2,500
50
Moose
40
2,000
30
1,500
20
1,000
10
500
0
1955
1965
1975
1985
Year
1995
0
2005
Number of moose
Number of wolves
Wolves
Population Cycles: Scientific Inquiry
While many populations fluctuate at unpredictable intervals, some populations
undergo regular boom-and-bust cycles
–
Three hypotheses have been proposed to explain the hare’s 10-year interval
–
1) These cycles may be caused by food shortage during winter
–
2) These cycles may be due to predator-prey interaction
–
Fig. 53-20
3) The size of the hare populations may vary with
sunspot activity, which also undergoes cyclic
changes
•
•
When sunspot activity is low, slightly less
atmospheric ozone is produced, resulting in
more UV radiation reaching Earth’s surface
In response, plants produce more UVblocking chemicals and thus fewer chemicals
that deter herbivores, increasing to quality of
the hares’ food
160
Snowshoe hare
120
9
Lynx
80
6
40
3
0
0
1850
1875
1900
Year
1925
Number of lynx
(thousands)
•
Ex) Lynx populations follow the 10 year boom-and-bust cycle of hare
populations, their main food source
Number of hares
(thousands)
•
•
Hypothesis 1: The hare’s population cycle follows a cycle of winter food
supply
–
If this hypothesis is correct, then the cycles should stop if the food
supply is increased
–
Additional food was provided experimentally to a hare population in the
Yukon for 20 years
• The whole population increased in size by ~3X but continued to
cycle
• No hares appeared to have died of starvation
–
Thus, food supplies alone do not cause the hare cycles observed, and
this hypothesis is rejected
•
Hypothesis 2 : The hare’s population cycle is driven by pressure from other predators
–
During the same 20-year study of hares in the Yukon field ecologists tracked
individual hares using radio collars to determine why they died
–
•
90% of the hares were killed by predators
•
These data support this second hypothesis
Ecologists also excluded predators from one area with electric fences and
provided the hares with extra food in another area within in the first
•
They found that the hare cycle is largely driven by excessive predation and
that food availability also plays an important role
–
Perhaps better-fed hares are more likely to escape from predators
•
Hypothesis 3: The hare’s population cycle is linked to sunspot cycles
–
There is good correlation between sunspot activity and hare population
size
• Periods of low sunspot activity were followed by peaks in the hare
population
–
The results of all these experiments suggest that:
• Both predation and sunspot activity may regulate the cycling of
hare numbers
• Food availability plays a less important role
Immigration, Emigration, and Metapopulations
•
Immigration and emigration also influence populations, especially when many local
populations are linked
–
•
Metapopulations are groups of populations linked by immigration and
emigration
High levels of immigration combined with higher survival can result in greater stability
in populations
–
Ex) On the Aland Islands of Finland, local populations of butterflies (filled
circles) are found in only a fraction of the
?
suitable habitat patches (open circles) at
Aland
Islands
EUROPE
any given time
Fig. 53-21
•
Individuals can move between local
populations and colonize unoccupied
patches
•
New populations of butterflies thus
regularly reappear as existing populations
become extinct
5 km
Occupied patch
Unoccupied patch
Concept Check 53.5
• 1) Identify 3 density-dependent factors that limit population
size, and explain how each exerts negative feedback.
• 2) Describe 3 attributes of habitat patches that could affect
population density and rates of immigration and emigration.
• 3) If you were studying an endangered species that, like the
snowshoe hare, has a 10-year population cycle, how long
would you need to study the species to determine if its
population size is declining? Explain.
Concept 53.6:
The human population is no longer
growing exponentially but is still
increasing rapidly
No population can grow indefinitely, and humans are no exception
–
–
The human population increased relatively slowly until about 1650 and then
began to grow exponentially
•
In only the two centuries following 1650, the population doubled from 500
million to 1 billion people
•
The population doubled again to 2 billion between 1850 and 1930
•
The population doubled a 3rd time by 1975 to > 4 billion
The global population in now
more than 6.6 billion people and
is increasing by ~75 million each
year (200,000 people/day)
Fig. 53-22
7
6
5
4
•
Population ecologists predict
a population of 7.8-10.8
billion people on Earth by the
year 2050
3
2
The Plague
1
0
8000
B.C.E.
4000 3000 2000 1000
B.C.E. B.C.E. B.C.E. B.C.E.
0
1000
C.E.
2000
C.E.
Human population (billions)
•
•
Though the global population is still growing, the rate of growth began to slow during
the 1960s
–
The annual rate of increase in the global population peaked at 2.2% in 1962
–
By 2005, it declined to 1.15%
–
Current models predict a continued decline in annual growth rate to just over
0.4% by 2050
Fig. 53-23
2.2
The reduction in growth rate over the past
2.0
4 decades shows that the human
population has departed from true
exponential growth
–
This is the result of fundamental
changes in population dynamics due
to diseases and voluntary
population control
1.8
Annual percent increase
•
1.6
1.4
2005
1.2
Projected
data
1.0
0.8
0.6
0.4
0.2
0
1950
1975
2000
Year
2025
2050
Regional Patterns of Population Change
•
To maintain population stability in a specific region, a human population’s birth rate
must equal its death rate
–
•
Zero population growth = High birth rate – High death rate
•
Zero population growth = Low birth rate – Low death rate
Demographic transition is the move
Fig. 53-24
from the first state toward the second
state
–
It is associated with:
•
An increase in the quality
of health care and sanitation
•
Improved access to
education, especially for
women
Birth or death rate per 1,000 people
•
This can occur can in one of two configurations:
50
40
30
20
10
Sweden
Birth rate
Death rate
0
1750
1800
Mexico
Birth rate
Death rate
1850
1900
Year
1950
2000 2050
•
Most of the current global population growth is concentrated in developing countries
–
Because death rates have declined rapidly in countries since 1950, variability in
birth rate is the main factor affecting local population growth
•
In developing countries, though birth rates are declining, they still remain
high compared to those of developed countries
•
In industrialized nations, populations are actually near equilibrium, with
reproductive rates near replacement level
–
In some of these countries, total reproductive rates are even below
replacement, meaning these populations will decline over time at the
current birth rate
•
Ex) Canada, Japan, Germany, UK
Age Structure
•
One important demographic factor in present and future growth trends is a country’s
age structure
–
Age structure is the relative number of individuals at each age in a population
–
Age structure is commonly graphed as “pyramids”
•
Some are bottom-heavy (Afghanistan), with a large majority of young
individuals
•
Some are relatively even, showing little (U.S.) to no (Italy) growth
–
The U.S. is still growing slowly due to:
Fig. 53-25
•
Many “baby boomers” and their offspring still being of
Rapid growth
Slow growth
reproductive age
Afghanistan
United States
Male
•
Female
Immigration from
other countries
10 8
6 4 2 0 2 4 6
Percent of population
Age
85+
80–84
75–79
70–74
65–69
60–64
55–59
50–54
45–49
40–44
35–39
30–34
25–29
20–24
15–19
10–14
5–9
0–4
8 10
8
Male
Female
6 4 2 0 2 4 6
Percent of population
Age
85+
80–84
75–79
70–74
65–69
60–64
55–59
50–54
45–49
40–44
35–39
30–34
25–29
20–24
15–19
10–14
5–9
0–4
8
8
No growth
Italy
Male
Female
6 4 2 0 2 4 6 8
Percent of population
•
Age structure diagrams can predict a population’s growth trends
–
They can also illuminate social conditions and help us plan for the
future
• Ex) Employment and education opportunities will continue to be a
significant problem for Afghanistan, due to their large young
population
• Ex) In Italy and the U.S., a decreasing proportion of younger
working-age people will soon be supporting an increasing
population of retired “boomers”
– This demographic feature has made the future of Social
Security and Medicare a major political issue in the U.S.
Infant Mortality and Life Expectancy
•
Infant mortality and life expectancy at birth vary greatly among developed and
developing countries
Infant mortality is the number of infant deaths per 1,000 live births
–
Life expectancy at birth is the predicted average length of life at birth
Fig. 53-26
infant mortality is low while
life expectancy is high
–
The reverse is true of
less industrialized countries
60
80
50
Life expectancy (years)
In industrialized countries,
Infant mortality (deaths per 1,000 births)
•
–
40
30
20
60
40
20
10
0
0
Indus- Less industrialized
trialized
countries countries
Indus- Less industrialized
trialized
countries countries
Global Carrying Capacity
•
How many humans can the biosphere support?
–
The carrying capacity of Earth for humans is uncertain
•
–
The average estimate is 10–15 billion
Ecologists use different methods to estimate carrying capacity
•
Some use curves like that produced by the logistic equation to predict the
future maximum of the human population
•
Others generalize from existing “maximum” population densities in
overpopulated regions and multiply this number by the area of habitable
land
•
Still others base their estimates on single limiting factors, including the
amount of available farmland or the average yield of crops
Limits on Human Population Size
•
A more comprehensive approach to estimating carrying capacity is to
recognize that humans have multiple constraints:
•
–
Food and water
–
Fuel
–
Building materials
–
Clothing
The ecological footprint concept summarizes the aggregate land and
water area needed to sustain the people of a nation
–
It is one measure of how close we are to the carrying capacity of Earth
Limits on Human Population Size
•
One way to estimate the ecological footprint of the entire human population
is to add up all the ecologically productive land on the planet and divide by
the population
–
This calculation yields ~2 hectares (ha) per person
• Reserving some land for parks and conservation means reducing
this allotment to 10.7 ha/person
• Therefore, anyone who consumes resources that require more than
1.7 ha to produce is said to be using an unsustainable share of
Earth’s resources
–
A typical ecological footprint for a person in the U.S. is ~10 ha
Limits on Human Population Size
•
Ecologists sometimes calculate ecological footprints using other currencies besides
land areas
–
Ex) The amount of photosynthesis that occurs on Earth is finite, since it is
constrained by the amount of land and sea area, as well as by the sun’s
radiation
•
Scientists have studied the extent to which people around the world
consume 7 types of photosynthetic products:
–
Plant foods
–
Wood for building and fuel
–
Paper
–
Fiber
–
Meat
–
Milk
–
Eggs
Limits on Human Population Size
•
Areas with higher population densities (China, India) have higher consumption rates
–
However, areas of much lower population (Europe, U.S.) density have higher
per capita (average/person) consumption, leading to equally high consumption
Fig. 53-27
rates
•
These areas have consumption rates as much as 400X greater the rate at
which photosynthetic products are produced in those areas
Log (g carbon/year)
13.4
9.8
5.8
Not analyzed
• Our carrying capacity could potentially be limited by:
– Food
– Space
– Nonrenewable resources: metals, fossil fuels
– Fresh water
– Buildup of wastes
• Technology has undoubtedly increased Earth’s carrying
capacity for humans
– However, no population can continue to grow
indefinitely
Concept 53.6
• 1) How does a human population’s age structure
affect its growth rate?
• 2) How has the growth of Earth’s human population
changed in recent decades? Give your answer in
terms of growth rate and the number of people added
each year.
• 3) What choices can you make that influence your
own ecological footprint?
You should now be able to:
1.
Define and distinguish between the following sets of terms: density and
dispersion; clumped dispersion, uniform dispersion, and random
dispersion; life table and reproductive table; Type I, Type II, and Type III
survivorship curves; semelparity and iteroparity; r-selected populations and
K-selected populations
2.
Explain how ecologists may estimate the density of a species
3.
Explain how limited resources and trade-offs may affect life histories
4.
Compare the exponential and logistic models of population growth
5.
Explain how density-dependent and density-independent factors may
affect population growth
6.
Explain how biotic and abiotic factors may work together to control a
population’s growth
7.
Describe the problems associated with estimating Earth’s carrying capacity
for the human species
8.
Define the demographic transition