1. Chapter 53
Population Ecology

2. Figure 53.1
Population ecology: the study of populations in relation to their environment, including environmental influences on density and distribution, age structure, and population size
Figure 53.1 What causes a sheep population to fluctuate in size? 2

3. Described by their boundaries and size
Population: a group of individuals of a single species living in the same general area
Described by their boundaries and size
Density: the number of individuals per unit area or volume
Dispersion: the pattern of spacing among individuals within the boundaries of the population
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4. Density: A Dynamic Perspective
Usually it is impractical/impossible to census
Sampling techniques  used to estimate densities and total population sizes
Population size can be estimated by either:
extrapolation from small samples, an index of population size (e.g., number of nests),
or the mark-recapture method
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5. Mark-recapture method
Capture, tag, and release a random sample of individuals (s) in a population
Allow time to mix back into the population
Scientists capture a second sample of individuals (n), and note how many of them are marked (x)
Population size (N) is estimated by… sn x
N 
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6. Deaths and emigration remove individuals from a population.
Figure 53.3
Births
Deaths
Deaths and emigration remove individuals from a population.
Births and immigration add individuals to a population.
Figure 53.3 Population dynamics.
Immigration
Emigration 6

7. Video: Flapping Geese (Clumped)
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8. Dispersion is affected by environmental and social factors
Figure 53.4
(a) Clumped
Dispersion is affected by environmental and social factors
(b) Uniform
Figure 53.4 Patterns of dispersion within a population’s geographic range.
(c) Random 8

9. Figure 53.4a
Clumped
Figure 53.4 Patterns of dispersion within a population’s geographic range. 9

10. Video: Albatross Courtship (Uniform)
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11. Uniform – may result from territoriality
Figure 53.4b
Uniform – may result from territoriality
Figure 53.4 Patterns of dispersion within a population’s geographic range. 11

12. Video: Prokaryotic Flagella (Salmonella typhimurium) (Random)
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13. Random: no strong attractions or repulsions
Figure 53.4c
Random: no strong attractions or repulsions
Figure 53.4 Patterns of dispersion within a population’s geographic range. 13

14. Demographics
Demography: 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
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15. Table 53.1
Life table: an age-specific summary of the survival pattern of a population
Often follows a cohort, a group of individuals of the same age
Table 53.1 Life Table for Belding’s Ground Squirrels (Spermophilus beldingi) at Tioga Pass, in the Sierra Nevada of California 15

16. Survivorship curve: represents the data in a life table
1,000
100
Number of survivors (log scale)
Females 10
Males
Figure 53.5 Survivorship curves for male and female Belding’s ground squirrels. 1 2 4 6 8 10
Age (years) 16

17. Survivorship curves can be classified into three general types
Type I: low death rates during early and middle life and an increase in death rates among older age groups
Type II: a constant death rate over the organism’s life span
Type III: high death rates for the young and a lower death rate for survivors
Many species are intermediate to these curves
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18. Number of survivors (log scale)
Figure 53.6
1,000 I
100 II
Number of survivors (log scale) 10
Figure 53.6 Idealized survivorship curves: Types I, II, and III.
III 1 50
100
Percentage of maximum life span 18

19. Table 53.2
Reproductive Tables
Demographers study reproductive rates of females in sexually reproducing species.
Table 53.2 Reproductive Table for Belding’s Ground Squirrels at Tioga Pass 19

20. Concept 53.2: The exponential model describes population growth in an idealized, unlimited environment
Idealized situations help us understand the capacity of species to increase and the conditions that may facilitate this growth
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21. Per Capita Rate of Increase
Change in
population
Size
Births
Immigrants
entering
Deaths
Emigrants
leaving   
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22. Population growth rate:
N: change in population size,
t: time interval,
B: number of births,
D: is the number of deaths
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23. b: annual per capita birth rate,
m (for mortality): annual per capita death rate
N: population size
Births per year, B  bN
Death per year, D  mN
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24. The population growth equation can be revised
= N (b – m)
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25. r  b  m The per capita rate of increase (r) is given by N  rN t
Zero population growth (ZPG): birth rate equals the death rate (r  0) N t  rN
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26. Summary N/ t = B – D N/ t = bN – mN N/ t = N (b – m) N/ t = rN
N = population size t = time B = # births D = # deaths b = annual per capita birth rate m = annual per capita death/mortality rate r = per capita rate of increase = b - m

27. Exponential Growth
Exponential population growth: population increase under idealized conditions dN dt 
rmaxN
Results in a J-shaped curve
Cannot be sustained for long in any population
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28. 2,000 dN dt = 1.0N 1,500 dN dt = 0.5N Population size (N) 1,000 500 5
Figure 53.7
2,000
dN dt
= 1.0N
1,500
dN dt
= 0.5N
Population size (N)
1,000
500
Figure 53.7 Population growth predicted by the exponential model. 5 10 15
Number of generations 28

29. The J-shaped curve of exponential growth characterizes some rebounding populations
For example, the elephant population in Kruger National Park, South Africa, grew exponentially after hunting was banned
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30. Figure 53.8
8,000
6,000
Elephant population
4,000
2,000
Figure 53.8 Exponential growth in the African elephant population of Kruger National Park, South Africa.
1900
1910
1920
1930
1940
1950
1960
1970
Year 30

31. Concept 53.3: The logistic model describes how a population grows more slowly as it nears its carrying capacity
A more realistic population model limits growth by incorporating carrying capacity
Carrying capacity (K): the maximum population size the environment can support
Varies with the abundance of limiting resources
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32. The Logistic Growth Model
The per capita rate of increase (r) declines as carrying capacity is reached
The logistic model starts with the exponential model and adds an expression that reduces per capita rate of increase as N approaches K dN dt 
(K  N) K
rmax N
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33. Below carrying capacity:
K – N/K = 40 – 2/40 = 0.95, little effect on rN
Above carrying capacity:
K – N/K = 40 – 39/40 = 0.025, drastically reduces rN

34. Table 53.3
Table 53.3 Logistic Growth of a Hypothetical Population (K = 1,500) 34

35. ( ) The logistic model produces a sigmoid (S-shaped) curve
Figure 53.9
The logistic model produces a sigmoid (S-shaped) curve
Exponential growth
2,000 dN dt
= 1.0N
1,500
K = 1,500
Logistic growth dN dt (
1,500 – N
1,500 )
Population size (N)
= 1.0N
1,000
Figure 53.9 Population growth predicted by the logistic model.
Population growth begins slowing here.
500 5 10 15
Number of generations 35

36. The Logistic Model and Real Populations
The growth of laboratory populations of paramecia fits an S-shaped curve
These organisms are grown in a constant environment lacking predators and competitors
Some populations overshoot K before settling down to a relatively stable density
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37. Number of Paramecium/mL
Figure 53.10
1,000
180
150
800
Number of Daphnia/50 mL
120
Number of Paramecium/mL
600 90
400 60
200 30 5 10 15 20 40 60 80
100
120
140
160
Figure How well do these populations fit the logistic growth model?
Time (days)
Time (days)
(a) A Paramecium population in the lab
(b) A Daphnia population in the lab 37

38. Some populations fluctuate greatly and make it difficult to define K
Allee effect: individuals have a more difficult time surviving or reproducing if the population size is too small
The logistic model fits few real populations but is useful for estimating possible growth
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39. Concept 53.4: Life history traits are products of natural selection
An organism’s life history comprises the traits that affect its schedule of reproduction and survival
The age at which reproduction begins
How often the organism reproduces
How many offspring are produced during each reproductive cycle
 evolutionary outcomes reflected in the development, physiology, and behavior of an organism
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40. Evolution and Life History Diversity
Semelparity, or big-bang reproduction: reproduce once and die. Favored in variable/unpredictable environments.
Iteroparity, or repeated reproduction: produce offspring repeatedly. Favored in stable/predictable environments.
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41. “Trade-offs” and Life Histories
Organisms have finite resources, which may lead to trade-offs between survival and reproduction
For example, there is a trade-off between survival and paternal care in European kestrels
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42. Parents surviving the following winter (%)
Figure 53.13
RESULTS
100
Male
Female 80 60
Parents surviving the following winter (%) 40
Figure Inquiry: How does caring for offspring affect parental survival in kestrels? 20
Reduced brood size
Normal brood size
Enlarged brood size 42

43. These are two extremes on a spectrum
K-selection, or density-dependent selection, selects for life history traits that are sensitive to population density. More common in populations living close to K; puts a greater investment in the offspring.
r-selection, or density-independent selection, selects for life history traits that maximize reproduction. More common in colonizing species.
These are two extremes on a spectrum
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44. (a) Dandelion: r strategist
Figure 53.14
(a) Dandelion: r strategist
Figure Variation in the size of seed crops in plants.
(b) Brazil nut tree (right) and seeds in pod (above): K strategist 44

45. 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?
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46. Population Change and Population Density
In density-independent populations, birth rate and death rate do not change with population density
In density-dependent populations, birth rates fall and death rates rise with population density
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47. Birth or death rate per capita
Figure 53.15
When population density is low, b > m. As a result, the population grows until the density reaches Q.
When population density is high, m > b, and the population shrinks until the density reaches Q.
Equilibrium density (Q)
Birth or death rate per capita
Density-independent death rate (m)
Figure Determining equilibrium for population density.
Density-dependent birth rate (b)
Population density 47

48. Mechanisms of Density-Dependent Population Regulation
Density-dependent birth and death rates are an example of negative feedback that regulates population growth
Affected by many factors such as:
competition for resources,
territoriality,
disease,
predation,
toxic wastes,
and intrinsic factors
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49. % of young sheep producing lambs
Figure 53.16
100 80 60
% of young sheep producing lambs 40 20
Figure Decreased reproduction at high population densities.
200
300
400
500
600
Population size 49

50. Population Dynamics
The study of population dynamics focuses on the complex interactions between biotic and abiotic factors that cause variation in population size
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51. Stability and Fluctuation
Long-term population studies have challenged the hypothesis that populations of large mammals are relatively stable over time
Both weather and predator population can affect population size over time
For example, the moose population on Isle Royale collapsed during a harsh winter, and when wolf numbers peaked
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52. Figure 53.18 50 40 30 20 10
2,500
2,000
1,500
1,000
500
Wolves
Moose
Number of wolves
Number of moose
Figure Fluctuations in moose and wolf populations on Isle Royale, 1959–2008.
1955
1965
1975
1985
1995
2005
Year 52

53. Population Cycles: Scientific Inquiry
Some populations undergo regular boom-and-bust cycles
Lynx populations follow the 10-year boom-and-bust cycle of hare populations
Three hypotheses have been proposed to explain the hare’s 10-year interval
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54. Number of hares (thousands) Number of lynx (thousands)
Figure 53.19
Snowshoe hare
160
120 80 40 9 6 3
Number of hares (thousands)
Lynx
Number of lynx (thousands)
Figure Population cycles in the snowshoe hare and lynx.
1850
1875
1900
1925
Year 54

55. These data do not support the first hypothesis
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, and the whole population increased in size but continued to cycle
These data do not support the first hypothesis
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56. These data support the second hypothesis
Hypothesis 2: The hare’s population cycle is driven by pressure from other predators
In a study conducted by field ecologists, 90% of the hares were killed by predators
These data support the second hypothesis
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57. Hypothesis 3: The hare’s population cycle is linked to sunspot cycles
Sunspot activity affects light quality, which in turn affects the quality of the hares’ food
There is good correlation between sunspot activity and hare population size
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58. The results of all these experiments suggest that both predation and sunspot activity regulate hare numbers and that food availability plays a less important role
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59. © 2011 Pearson Education, Inc.

60. Immigration, Emigration, and Metapopulations
Figure 53.20
Immigration, Emigration, and Metapopulations
EXPERIMENT
Dictyostelium amoebas
Topsoil
Bacteria
200 m
Figure Inquiry: How does food availability affect emigration and foraging in a cellular slime mold?
Dictyostelium discoideum slug
A group of Dictyostelium amoebas can emigrate and forage better than individual amoebas 60

61. Metapopulations: groups of populations linked by immigration and emigration
High levels of immigration combined with higher survival can result in greater stability in populations
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62. ˚ Aland Islands EUROPE Occupied patch Unoccupied patch 5 km
Figure 53.21
Aland
Islands ˚
EUROPE
Figure The Glanville fritillary: a metapopulation.
Occupied patch
Unoccupied patch
5 km 62

63. 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
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64. Human population (billions)
Figure 53.22 7 6 5 4 3 2 1
Human population (billions)
The Plague
Figure Human population growth (data as of 2009).
8000
BCE
4000
BCE
3000
BCE
2000
BCE
1000
BCE
1000 CE
2000 CE 64

65. The rate of growth began to slow during the 1960s
Figure 53.23
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
2009
Annual percent increase
The rate of growth began to slow during the 1960s
Projected data
Figure Annual percent increase in the global human population (data as of 2009).
1950
1975
2000
2025
2050
Year 65

66. Regional Patterns of Population Change
To maintain population stability, a regional human population can exist in one of two configurations
Zero population growth = High birth rate – High death rate
Zero population growth = Low birth rate – Low death rate
The demographic transition is the move from the first state to the second state
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67. The demographic transition is associated with increase in the quality of health care and improved access to education, especially for women
Most of the current global population growth is concentrated in developing countries where the demographic transition is not complete.
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68. 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
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69. Rapid growth Afghanistan Slow growth United States No growth Italy
Figure 53.24
Rapid growth
Afghanistan
Slow growth
United States
No growth
Italy
Male
Female
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
Male
Female
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
Male
Female
Figure Age-structure pyramids for the human population of three countries (data as of 2009). 10 8 6 4 2 2 4 6 8 10 8 6 4 2 2 4 6 8 8 6 4 2 2 4 6 8
Percent of population
Percent of population
Percent of population 69

70. Infant Mortality and Life Expectancy
Infant mortality and life expectancy at birth vary greatly among developed and developing countries but do not capture the wide range of the human condition
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71. Infant mortality (deaths per 1,000 births)
Figure 53.25 60 50 40 30 20 10 80 60 40 20
Infant mortality (deaths per 1,000 births)
Life expectancy (years)
Figure Infant mortality and life expectancy at birth in industrialized and less industrialized countries (data as of 2008).
Indus- trialized countries
Less indus- trialized countries
Indus- trialized countries
Less indus- trialized countries 71

72. Global Carrying Capacity
How many humans can the biosphere support?
Population ecologists predict a global population of 7.810.8 billion people in 2050
The carrying capacity of Earth for humans is uncertain: The average estimate is 10–15 billion
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73. Limits on Human Population Size
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
Countries vary greatly in footprint size and available ecological capacity
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74. Gigajoules > 300 150–300 50–150 10–50 < 10 Figure 53.26
Figure Annual per capita energy use around the world.
> 300
150–300
50–150
10–50
< 10 74

75. Our carrying capacity could potentially be limited by food, space, nonrenewable resources, or buildup of wastes
Unlike other organisms, we can regulate our population growth through social changes
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76. Patterns of dispersion
Figure 53.UN01
Patterns of dispersion
Figure 53.UN01 Summary figure, Concept 53.1
Clumped
Uniform
Random 76

77. dN dt Population size (N) = rmax N Number of generations
Figure 53.UN02 dN dt
= rmax N
Population size (N)
Figure 53.UN02 Summary figure, Concept 53.2
Number of generations 77

78. ( ) K = carrying capacity Population size (N) dN K – N dt K = rmax N
Figure 53.UN03
K = carrying capacity
Population size (N) dN dt
K – N K ( )
= rmax N
Figure 53.UN03 Summary figure, Concept 53.3
Number of generations 78

79. Figure 53.UN04
Figure 53.UN04 Appendix A: answer to Figure legend question 79

80. Figure 53.UN05
Figure 53.UN05 Appendix A: answer to Concept Check 53.1, question 1 80

81. Figure 53.UN06
Figure 53.UN06 Appendix A: answer to Test Your Understanding, question 11 81