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Integral projection models. Continuous variable determines Survival Growth Reproduction. Easterling, Ellner and Dixon, 2000. Size-specific elasticity: applying a new structured population model. Ecology 81:694-708. The state of the population. Integral Projection Model. - PowerPoint PPT Presentation
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Integral projection models
Continuous variable determines
SurvivalGrowth Reproduction
Easterling, Ellner and Dixon, 2000. Size-specific elasticity: applying a new structured populationmodel. Ecology 81:694-708.
The state of the population
0 1 2 3 4 5 6
0.0
2.5
size
fre
qu
en
cy
Stable distributions for n=50 and n=100
0 1 2 3 4 5 6
0.4
1.0
size
Re
pro
du
ctiv
e v
alu
eRelative reproductive value for n=50 and n=100
Integral Projection Model
dxtxf(x,y)yxpty ),(]),([)1,( nn
=Number of size y individuals at time t+1
Probability size x individuals Will survive and become size y individuals
Number of size x individuals
at time t
Babies of size y made by size x individuals
Integrate over all possible sizes
Integral Projection Model
dxtxyxkty ),()],([)1,( nn
=Number of size y individuals at time t+1
Number of size x individuals
at time t
The kernel(a non-negative surface representingAll possible transitions from size x to size y)
Integrate over all possible sizes
survival and growth functions
),()(),( yxgxsyxp
s(x) is the probability that size x individual survives
g(x,y) is the probability that size x individuals who survive grow to size y
survival s(x) is the probability that size x individual
survives
logistic regressioncheck for nonlinearity
bxaxsxs ))(1/)(log(
growth function g(x,y) is the probability
that size x individuals who survive grow to size y
meanregressioncheck for nonlinearity
variance
growth function
22 )(2/))((
)(2
1),( xxye
xyxg
-4 -2 0 2 4 6 80
0.05
0.1
0.15
0.2
0.25
0.3
0.35
size y, at time t+1
prob
abili
ty d
ensi
ty
Comparison to Matrix Projection Model
Populations are structured Discrete time model Population divided into
discrete stages Parameters are estimated
for each cell of the matrix: many parameters needed
Parameters estimated by counts of transitions
• Populations are structured
• Discrete time model• Population characterized
by a continuous distribution
• Parameters are estimated statistically for relationships: few parameters are needed
• Parameters estimated by regression analysis
Matrix Projection Model Integral Projection
Model
Comparison to Matrix Projection Model
Recruitment usually to a single stage
Construction from observed counts
Asymptotic growth rate and structure
• Recruitment usually to more than one stage
• Construction from combining •survival, growth and
fertility functions into one integral kernel
• Asymptotic growth rate and structure
Matrix Projection Model Integral Projection
Model
Comparison to Matrix Projection Model
Analysis by matrix methods
• Analysis by numerical integration of the kernel
• In practice: make a big matrix with small category ranges
• Analysis then by matrix methods
Matrix Projection Model Integral Projection
Model
Steps
read in the data statistically fit the model components combine the components to compute
the kernel construct the "big matrix“ analyze the matrix draw the surfaces