Transcript Overview: Counting Sheep

Overview: Counting Sheep
• Soay sheep were introduced to
Hirta Island in 1932
• Opportunity to study changes
in population on isolated island
with abundant food and no
predators
• Population ecology: study of
populations in relation to
environment; influences on
density, distribution, age
structure, population size
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Population Ecology
Chapter 53
Population density, dispersion, and
demographics
• Population: group of individuals of one species,
living in an area
• Density: number of individuals per unit area or vol.
– Immigration and emigration
– Impractical to count all individuals
– Sampling techniques, as mark-recapture method
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Figure 53.3
Births
Births and immigration
add individuals to
a population.
Immigration
Deaths
Deaths and emigration
remove individuals
from a population.
Emigration
Patterns of Dispersion
• Dispersion: pattern of spacing among individuals
• Environmental and social factors influence spacing
– Clumped: resource availability, behavior
– Uniform: social interactions such as territoriality, defense of
a space against other individuals
– Random: absence of strong attractions or repulsions
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Figure 53.4
(a) Clumped
(b) Uniform
(c) Random
Demographics
• Demography: study of vital statistics of a population
• Death rates and birth rates
• Life table: age-specific summary of survival pattern;
follows fate of a cohort (individual)
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Example of a Life Table
Figure 53.5
Survivorship Curve for Belding’s ground squirrels
Number of survivors (log scale)
1,000
100
Females
10
Males
1
0
2
4
6
Age (years)
8
10
Survivorship Curves
• Type I – low death rates early, middle; death rates
increase among old age
– Large mammals,
– Produce few offspring but give good care
• Type III – high death rate for young; few survive early
dieoff
– Large numbers of offspring, little or no care of young
– Plants, fish, marine invertebrates
• Type II – constant death rate over lifespan
– Rodents, invertebrates, some lizards
• Many species don’t fall into any of these
categories
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Figure 53.6
Survivorship Curves are of three types
Number of survivors (log scale)
1,000
I
100
II
10
III
1
0
50
Percentage of maximum life span
100
Reproductive Rates
• For species with sexual
reproduction, demographers
often concentrate on females
• Reproductive table or fertility
schedule = age-specific
summary of reproductive rates
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Exponential model of
population growth
• Useful to study population growth in an idealized
situation
• Per capita rate of increase
• Often, migration is ignored
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Expected number of births/deaths per year
B  bN
D  mN
b = annual per capita birth rate
m = per capita death rate
N = population size
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r = per capita rate of increase
rbm
• Zero population growth (ZPG) when r  0
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Exponential Population Growth
• Population increase under ideal conditions
• Rate of increase at maximum – rmax
• Exponential population growth is
dN 
rmaxN
dt
• Exponential growth results in J-shaped curve
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Figure 53.7
Population growth predicted
by the exponential model.
2,000
dN
= 1.0N
dt
Population size (N)
1,500
dN
= 0.5N
dt
1,000
500
0
5
10
Number of generations
15
Figure 53.8
Exponential growth in the African elephant population
Elephant population
8,000
6,000
4,000
2,000
0
1900
1910
1920
1930
1940
Year
1950
1960
1970
Logistic model
• Describes how a population grows more slowly as it
reaches its Carrying Capacity
• Exponential growth cannot be sustained
• More realistic model incorporates carrying capacity (K);
produces sigmoid (S-shaped) curve
• Carrying capacity varies with abundance of resources
• Per capita rate of increase declines as carrying capacity
reached (as N approaches K)
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Table 53.3
Figure 53.9
Exponential
growth
dN
= 1.0N
dt
Population size (N)
2,000
1,500
K = 1,500
Population growth predicted by
the logistic model.
Logistic growth
1,500 – N
dN
= 1.0N
1,500
dt
(
1,000
Population growth
begins slowing here.
500
0
0
5
10
Number of generations
15
)
Figure 53.10
1,000
Number of Daphnia/50 mL
Number of Paramecium/mL
In a constant environment lacking competitors and predators,
how well do these populations fit the logistic growth model?
800
600
400
200
0
0
5
10
Time (days)
(a) A Paramecium population in
the lab
15
180
150
120
90
60
30
0
0
20
40
60 80 100 120 140 160
Time (days)
(b) A Daphnia population in the lab
Logistic Model and Real Populations
• Some populations overshoot K
• Some populations fluctuate greatly; difficult to define K
• Logistic model assumes instant adjustment to growth
• Logistic model can be used to estimate possible growth
• Conservation biologists use model to estimate critical
size below which populations may become extinct
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Life history traits are products
of natural selection
• Life history comprises traits that affect schedule of
reproduction and survival
– Age at which reproduction begins
– How often organism reproduces
– How many offspring are produced during each
reproductive cycle
• Life history traits – evolutionary outcomes reflected in
development, physiology, behavior
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Evolution and Life History Diversity
• Species that exhibit semelparity, or big-bang
reproduction, reproduce once and die
– Highly variable environments favor semelparity
– Century plant or Agave
• Other species show iteroparity (repeated reproduction)
– Dependable environments favor iteroparity
– Some Lizards
• Organisms have finite resources – may lead to trade-offs
– Trade-off between survival & paternal care in kestrels
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How does caring for offspring affect parental
survival in kestrels?
RESULTS
100
Parents surviving the following
winter (%)
Figure 53.13
Male
Female
80
60
40
20
0
Reduced
brood size
Normal
brood size
Enlarged
brood size
f
Trade offs between reproduction and survival
• Some plants produce large number of small seeds,
ensuring that some will grow & eventually reproduce
• Other plants produce moderate number of large
seeds that provide large store of energy that will help
seedlings become established
– r-selection (density-independent) – selects for life history
traits that maximize reproduction
– K-selection (density-dependent) – selects for life history
traits sensitive to population density
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Figure 53.14
(a) Dandelion
(b) Brazil nut tree (right)
and seeds in pod
(above)
Density-dependent factors
• Density-independent = birth and death rates
do not change with population density
• Density- dependent = birth and death rates do
change with population density
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Density-dependent factors
• Density-dependent birth and
death rates
• Negative feedback between
population density and
birth/death rates
• Affected by competition for
resources, territoriality, disease,
predation, toxic wastes
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Figure 53.15
Birth or death rate
per capita
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)
Density-independent
death rate (m)
Density-dependent
birth rate (b)
Population density
Determining equilibrium for
population density.
Figure 53.16
Decreased reproduction at high
population densities.
% of young sheep producing lambs
100
80
60
40
20
0
200
300
400
Population size
500
600
Population Dynamics
• Focuses on complex interactions between biotic and
abiotic factors that cause variation in population size
• Long-term studies have challenged hypothesis that
populations of large mammals are relatively stable
• Weather and predators can affect population size
– Moose population on Isle Royale
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Figure 53.18
50
2,500
Moose
40
2,000
30
1,500
20
1,000
10
500
0
1955
Number of moose
Number of wolves
Wolves
0
1965
1975
1985
Year
1995
2005
Fluctuations in moose and wolf populations on Isle Royale, 1959–2008.
Population Cycles
• Some populations undergo regular boom-andbust
– Lynx populations follow 10-year boom-and-bust
cycle of hare populations
– Three hypotheses have been proposed
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Figure 53.19
Snowshoe hare
120
9
Lynx
80
6
40
3
0
0
1850
1875
1900
Year
1925
Number of lynx
(thousands)
Number of hares
(thousands)
160
Hypothesis 1
• Hare’s cycle follows cycle of winter food supply
• If hypothesis correct, then cycles should stop if food
supply is increased
• Additional food was provided experimentally; whole
population increased, but continued to cycle
• Rejected!
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Hypothesis 2
• Hare’s cycle driven by pressure from other predators
• In study by field ecologists, 90% of hares were killed
by predators
• Second hypothesis supported!
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Hypothesis 3
• Hare’s cycle linked to sunspot cycles
• Sunspot activity affects light quality, which in turn
affects quality of hares’ food
• Good correlation between sunspot activity &
population size
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Figure 53.20
How does food availability affect emigration and
foraging in a cellular slime mold?`
Immigration / Emigration – Dictyostelium amoebas can
emigrate and forage better than individual amoebas
EXPERIMENT
200 m
Dictyostelium
amoebas
Dictyostelium discoideum slug
Dictyostelium movement
Topsoil
Bacteria
Figure 53.21
Metapopulations – groups of populations linked by
immigration and emigration
˚
Aland
Islands
EUROPE
Glanville
fritillary
5 km
Occupied patch
Unoccupied patch
Human population
• Human population increased relatively slowly until
about 1650, then began to grow exponentially
• Global population now ~7 billion people
• Though global population still growing, rate of
growth began to slow during 1960s
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Figure 53.22
6
5
4
3
2
The Plague
1
0
8000
BCE
4000
BCE
3000
BCE
2000
BCE
1000
BCE
0
1000
CE
2000
CE
Human population (billions)
7
Figure 53.23
2.2
Annual percent increase in the global
human population (as of 2009).
2.0
Annual percent increase
1.8
1.6
1.4
2009
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 stability, regional human population can
exist in one of two configurations
ZPG = High birth rate – High death rate
or
ZPG = Low birth rate – Low death rate
• Demographic transition = move from first state to
second state
– Associated with increase in quality of health care and
improved access to education
– Most of current population growth concentrated in
developing countries
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Age Structure
• Important demographic factor in present and
future growth trends
• Relative number of individuals at each age
• Diagrams can predict growth trends & help
future planning
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Figure 53.24
Age-structure pyramids for the human population of three countries (2009).
Rapid growth
Afghanistan
Male
10 8
Female
6 4 2 0 2 4 6
Percent of population
Slow growth
United States
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
No growth
Italy
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
Male
Female
6 4 2 0 2 4 6
Percent of population
8
Figure 53.25
Infant Mortality and Life Expectancy
80
50
Life expectancy (years)
Infant mortality (deaths per 1,000 births)
60
40
30
20
60
40
20
10
0
0
Industrialized
countries
Less industrialized
countries
Industrialized
countries
Less industrialized
countries
Global Carrying Capacity
• Predicted population of 7.810.8 billion in 2050
• Carrying capacity of Earth for humans is uncertain
• Average estimate is 10–15 billion
• Ecological footprint – aggregate land and water
area needed to sustain people
• Countries vary greatly in footprint size and
available ecological capacity
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Figure 53.26
Average per capita energy use
Gigajoules
> 300
150–300
50–150
10–50
< 10