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11/17/2009
1
11/8/09
Population Biology
• Essential to understand human populations
• Essential to understand endangered species
• Essential to understand pests and parasites
• Essential to understand other economically important species
11/8/09
Defining the individual
• Unitary
– Individuals are discrete
– Less plasticity
• Modular
– Individuals reproduce by modules
– More plasticity
– Ramets and genets
– Biomass
• Growth
Determinate
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Populations
• Number of individuals of one species
– All individuals (N)
– Sample (n)
– Within a nature boundary
• Islands
• Well defined habitats (e.g., pond)
– Within an arbitrary boundary
• State, region, county 11/8/09
Populations
N is very rarely ever known• except for the very rare!• or very obvious in well-defined boundaries •too rare… extinct?
Snowdonia hawkweed from the beautiful nation of Wales. N=1
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Population Size?
• Population Index
– Not all organisms are counted
– Some standard is chosen then changes indicate population changes (indices of abundance)
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Population Size?
• Population Index
– Not all organisms are counted
– Some standard is chosen then changes indicate population changes (indices of abundance)
– Common in wildlife management
– Example
• deer brought into check stations indicate the population when the number of deer out there are never known
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Population Indices
• Population Indices
– Require minimal work (therefore cheap)
– Provide minimal information
• Difficult to predict the consequences of management decisions
• Adaptive management
• Difficult to predict the consequences of environmental changes
– Climate change
– Introduced species (parasites, disease [West Nile], new food,
11/8/09
Population Estimation
• Population estimation attempts to figure out how many individuals there are in an area
– N can refer to all individuals of a species but more often the total number of individuals in an area
– A sample of a population is n and can be used to estimate N
– Formally, n = (N hat)
– N is hardly ever known
– A good estimate is when
N̂
NN̂
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Population Estimates
• How do we get ?
• Plants
– Sample in a small area to get density then extrapolate
– Density = number of individuals/area
• Animals
– Density, but more difficult… (ex: cougar in Oregon)
– Capture-recapture
N̂
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Capture-Recapture
• Animals are captured, marked, released, and then resampled
• Labor intensive (not cheap)
• Lots of assumptions
– Animals don’t avoid trapping.…. (e.g. bears)
– Many more but… (sample representative?)
– Problems with biases….. (e.g. bird nets)
• Provide lots of information
– Survivorship, sex ratios, recruitment, health, DNA samples, etc
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Capture – Recapture
• Many animals are PIT tagged
– Passive Integrated Transponder
– used in livestock and pets as well as wildlife
– Endangered species
– Individual recognized by hand-held scanners
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Capture – Recapture
• Many animals are PIT tagged
– Passive Integrated Transponder
– used in livestock and pets as well as wildlife
• Mammals get ear tagged
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Capture – Recapture
• Many animals are PIT tagged
– Passive Integrated Transponder
– used in livestock and pets as well as wildlife
• Mammals get ear tagged
• Birds get banded, big birds get neck collars
11/8/09
Capture – Recapture
• Many animals are PIT tagged
– Passive Integrated Transponder
– used in livestock and pets as well as wildlife
• Mammals get ear tagged
• Birds get banded, big birds get neck collars
• Animals with complex patterns can be photographed
– Whales, jaguars, some salamanders
11/8/09
Capture – Recapture
• Many animals are PIT tagged
– Passive Integrated Transponder
– used in livestock and pets as well as wildlife
• Mammals get ear tagged
• Birds get banded, big birds get neck collars
• Animals with complex patterns can be photographed
– Whales, jaguars, some salamanders
• Insects and hummingbirds – white out!
11/8/09
Capture – recapture
• 2 samples
)2()1(
)(1)((
1
)1)((̂2
secsecsec
recapturedrecaptured
recapturednnn
recaptured
nnN ondondinitialondinitial
11/8/09
Capture – recapture
• 2 samples
)2()1(
)(1)((
1
)1)((̂2
secsecsec
recapturedrecaptured
recapturednnn
recaptured
nnN ondondinitialondinitial
Population size very useful…. But for management purposes, we also need density AND dispersion of population in environment…
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Density and Dispersion
Random Clumped Uniform
(Measure of evenness)
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DISTANCE FROM NEAREST NEIGHBOR DISTRIBUTION
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DISTANCE FROM NEAREST NEIGHBOR DISTRIBUTION
Mean
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DISTANCE FROM NEAREST NEIGHBOR DISTRIBUTION
Variance
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Density and Dispersion
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Spatial Scale: Extent
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Spatial Scale: Resolution
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Geographic Range
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Life table analysis
• Understanding the Nnow of a particular species
• So we can predict the Nfuture
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Life table analysis: patterns of birth, death and growth
• Survival (survivorship curve)
• Fecundity schedule (birth rate)
• Mortality (proportion dying at each life stages)
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Life table analysis: patterns of birth, death and growth
• For species with annual life cycle…
• Semelparity vs. iteroparity
• Easy for species with distinct life stages
• Generation only overlap between breeding adults and offspring (distinct generations)
• Cohort: group of individuals born within the same short interval of time
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Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Various stages of the life cycle that have been distinguishedCan be divided by age-classes…
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Raw data: number of individuals observed at each stagea0 for the first stage, a1 for the second, etc
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
ax good only for that population, lx can be compared, since it’s a proportion
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Proportion of original cohort dying at each stage
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Fraction dying during each stage: probability of an ind. dying.“intensity of mortality”
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Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Used for survivorship curvesCaptures the biologically meaningful observations instead of just arithmetic
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(Parenthesis: the use of log)
• Ex.: If pop. goes down 1000 to 500 in a single time interval
• Then, goes down from 100 to 50
• One is down 500 ind. (- 500)
• The other is only 50 (-50)
• BOTH are identical biologically: they lost half their population (mortality rate is same)
• Both provide a slope of -0.301 in log scale!
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Raw data: number of eggs deposited during each stage
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Bit more useful: number of eggs produced by surviving individual in each stage
11/8/09
Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Basic reproductive rateMean number of offspring produced by original ind. by the end of the cohort 11/8/09
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Cohort Life Table: Red Deer of the Isle of Rhum
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Cohort Life Table: Red Deer of the Isle of Rhum
All individuals known, all calves counted form 1957 until 1966
All cause of death known
Monitored all events
Problems: we have overlapping generations, very few are sessile…
so not easy… Too hard for long lived animals?
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Static Life Table (or time-specific)
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Static Life Table (or time-specific)
Try to find a solution…
Based on reconstructed age-structure of the population (looking at those who died)
Also criticized…. Provide negative mortality rates…Same data as a single cohort followed.
Not quite perfect for species with overlapping generations...
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R0 (basic reproductive rate)
calculated for overlapping generation species is almost the same as species with discrete or distinct generations
What happens with varying densities from year to year?
What causes changes in density?
Can we include that in life table?
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Iteroparity and the age-dependent reproduction
Static fecundity schedules for multiple breeding seasonsAge- or stage-related patterns...
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Reproductive rates (R0, λ, r)
• Basic reproductive rate
For species with distinct generationsSums of all proportion of ind. surviving with average fecundity per survivor
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Reproductive rates (R0, λ, r)
• Basic reproductive rate
Becomes average number of offspring produced by an individual in its lifetimeUnit of time IS a generation in distinct generations
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Reproductive rates (R0, λ, r)
• Basic reproductive rate
• Fundamental net reproductive rate
– If prefer the symbol: λ
– If λ > 1 the population increases if < 1 the population decreases
– Does not separate between survival and reproductionPop size at one interval of time
Initial pop size11/8/09
Reproductive rates (R0, λ, r)
• Note that R0 = λ T
• And ln(R0)= T ln(λ)
• And ln(λ) = ln(R0)/T
• ln(λ) = r
• So r=ln(R0)/T
• r is the intrinsic rate of natural increase
(increase in pop size per unit time)
• If r > 0 the population grows, if < 0 then population declines and if r = 0 then?
Generation time
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Simple Cohort Life TableInterval (days) Number
surviving (ax)Proportion of original
surviving (lx)Proportion of orginal cohert dying during interval (dx)
Mortality rate per day (qx)
Log10 lx Cohort fecundity Fx
Average fecundity per survivor (mx)
Proportion of original fecundity
(lxmx)
0-63 996 1.000 0.329 0.006 0.000
63-124 668 0.671 0.374 0.013 -0.173
124-184 295 0.296 0.105 0.007 -0.528
184-215 190 0.191 0.014 0.003 -0.720
215-264 176 0.177 0.004 0.002 -0.753
264-278 172 0.173 0.005 0.002 -0.763
278-292 167 0.168 0.008 0.004 -0.776
292-306 159 0.160 0.005 0.002 -0.797 53 0.333 0.053
306-320 154 0.155 0.007 0.003 -0.811 485 3.149 0.487
320-334 147 0.148 0.042 0.025 -0.831 803 5.461 0.806
334-348 105 0.105 0.083 0.106 -0.977 973 9.264 0.977
348-362 22 0.022 0.022 1.000 -1.656 95 4.309 0.095
362- 0 0.000
Basic reproductive rateMean number of offspring produced by original ind. by the end of the cohort
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Iteroparity and the age-dependent reproduction
Static fecundity schedules for multiple breeding seasonsAge- or stage-related patterns...
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Matrices
• A matrix is a rectangular arrangement of data in the form m x n where m is the number of rows and n is the number of columns
• Capital letters are used for a matrix: A
• An element or entry is any datum of the matrix and lowercase is typically used
• The position of the element is denoted with subscripts ij where i is the row and j is the column.
– Example: a12,3 would be found in the 12th row, 3 third column
• A vector is a m x 1 or 1 x n matrix
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Leslie or Population ProjectionMatrix
In this exampleThere are three age classesFx is the fecundity of cohort xSx is the survival of cohort x to cohort x+1
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Population vector
In this example:Three age classesNx is the number in each age class
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Matrix multiplication
x nt=1 =
= nt=1
=
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Population Change
The resultant vector is the new population broken down by age structure
The new population is the sum of each of the age groups. In this case, Nt+1 = 112
Remember that λ is and the original population was or 100
0
1
N
Nt
so λ is 112/100 or 1.12
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Population Change (r)
The resultant vector is the new population broken down by age structure
The new population is the sum of each of the age groups. In this case, Nt+1 = 112
Remember that λ is and the original population was or 100
0
1
N
Nt
so λ is 112/100 or 1.12
λ over 1, pop is increasingln(λ) = r, so here r = 0.11r over 0, pop is increasingr is the intrinsic rate of natural increase
(increase in pop size per unit time)
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And? What do we do with this beast?
• A useful tool for seeing what happens to populations in the future
• Can incorporate stochasticity
– Gives a range instead a single value
• Can estimate the parameters that drive the results using a sensitivity analysis
– Important to figure out what to protect for endangered species or
– What to target for pest species
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Leslie Projection Matrices
• Used to see what would happen under different scenarios
• Can be expanded (gets ugly fast)
– Include spatial structure
– React to disease
– Competitors
– Etc
– Carrying capacity
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Carrying Capacity (K)
• Ideal populations size
– Everything needed is provided
– Populations increase exponentially
• Real populations
– Population has some limit set by the environment
– This upper limit is carrying capacity
– Highest density “allowed” by the environment
– Real populations may show exponential growth up to, over, or near the carrying capacity
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Carrying Capacity
• In most cases, exceedingly difficult to measure
– May be easy on a small scale where a limiting factor is easily monitored
• Rock structure for sessile organisms (density-dependent mortality vs density-dependent birth)
• Nesting sites for albatross
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Carrying Capacity
• In most cases, exceedingly difficult to measure
– May be easy on a small scale where a limiting factor is easily monitored
• Rock structure for sessile organisms
• Nesting sites for albatross
– Limiting factor can be biotic and abiotic
– Typically a mix of factors
– Ecology, being ecology, makes for complexities
• Species that limit some species are themselves limited by other species that are limited by other species that are limited by other species that are limited by other species that are limited by other species and so on
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Carrying Capacity
‘S’-shaped population increase, or sigmoidal curve
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Carrying capacity (K)
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Carrying capacity (K)
Much more likely to happen in real life
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Density Independent vs. Density Dependent Population Growth
dN by dt: speed at which a population increases in size N as time t progresses
r is the intrinsic rate of natural increase (increase in pop size per unit time)N pop sized density
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Density Independent vs. Density Dependent Growth
dN by dt: speed at which a population increases in size N as time t progresses
In absence of competition, or density independent.When density has no effect on pop, or pop growth.Which is unrealistic…..
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r and K
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r and K
Competition factor
When N rise to K, net increase fall to 0
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r and K selection
“r-selected “species
maximize“K-selected” species maximize
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r and K selection
When environment stable, K selectionhighly competitive, high survivorship, low reproductive outputFragile to rare disturbances (Tropics)When environment NOT stable, r selectionpoorly competitive, low survivorship, high reproductive outputEasily adapted to frequent disturbances (northern latitudes)
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r and K selection
Robert MacArthurE. O. Wilson
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R and K selection Characteristics
r-strategist K-strategist
Climate variable constant or predictable
Population size variable, recolonization at equilibrium
Competition variable, lax keen
Lifespan short, <1yr long, slower development
Size small larger
Reproduction much energy toward, rapid, large number of progeny
delayed reproduction, few offspring
Leads to high productivity efficiency
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Age Structure and Populations Age structured populations
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N t= N 0 ert
N t double= 2N0 2N0= N 0e
rtdouble
2= ertdoubleln 2 = rtdouble
tdouble=ln 2
r
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Species r (individuals/ individual*day)
Doubling time
T phage 300 3.3 minutes
E. coli 58.7 17 minutes
Rattus norvegicus 0.0148 46.8 days
Nothofagus fuscus 0.000075 25.3 years
Human Population Growth
The Human Population
• Current growth rate is ~1.3% per year
• ~2.1% per year in 1965-1970
• the absolute number of new people per year (~90 million) is at an all time high
• one billion in 1804
• Two billion in 1927
• Five billion in 1987
• Now – 6.57
• 9 billion in 2050 (revised down because of AIDS then revised back up because of new antivirals)
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Population Regulation
• Central charge of ecology
• Still largely unknown for most organisms
• Abiotic factors: weather (Andrewartha and Birch 1954)
• Biotic factors
• Interspecific and intraspecific interactions
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Bottom-up or top-down?
• Bottom up
– Nutrients
– Water
– Nesting sites
– Trophic levels below
• Top down
– Predators
– Parasites11/8/09
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