Integral projection models - PowerPoint PPT Presentation

integral projection models n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Integral projection models PowerPoint Presentation
Download Presentation
Integral projection models

play fullscreen
1 / 12
Integral projection models
261 Views
Download Presentation
clovis
Download Presentation

Integral projection models

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. 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.

  2. The state of the population

  3. Integral Projection Model Integrate over all possible sizes Number of size x individuals at time t Number of size y individuals at time t+1 = Babies of size y made by size x individuals Probability size x individuals Will survive and become size y individuals

  4. Integral Projection Model Integrate over all possible sizes Number of size x individuals at time t Number of size y individuals at time t+1 = The kernel (a non-negative surface representing All possible transitions from size x to size y)

  5. survival and growth functions 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

  6. survival s(x) is the probability that size x individual survives logistic regression check for nonlinearity

  7. growth function g(x,y) is the probability that size x individuals who survive grow to size y mean regression check for nonlinearity variance

  8. growth function

  9. Comparison to Matrix Projection Model Matrix Projection ModelIntegral 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

  10. Comparison to Matrix Projection Model Matrix Projection ModelIntegral 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

  11. Comparison to Matrix Projection Model Matrix Projection ModelIntegral 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

  12. 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