Analysis of insect population data with structured population models

Principal Investigators:

Perry de Valpine

Agricultural pest control relies on understanding complex communities of herbivores and their natural enemies. Field experiments of agricultural communities have produced voluminous multi-species, spatiotemporal population data. However, conventional data analysis uses model frameworks that lack biological structure and thus are only indirectly related to the hypothesized processes of birth, death, predation and movement. This project will use population models to analyze extensive existing data from three agricultural insect communities and one... more

Participants and Meetings

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ActivityDatesFurther Information
Postdoctoral Fellow1st September 2000—31st August 2001Participant List  

Participant Contact Information

Perry de Valpinepdevalpine@berkeley.eduUnknown

Products: Publications, Reports, Datasets, Presentations, Visualizations

TypeProducts of NCEAS Research
Presentations de Valpine, Perry. 2000. Fitting fisheries models with process noise and observation error using nonlinear, non-Gaussian state-space models. Invited Talk from Mote International Fisheries Symposium.
Presentations de Valpine, Perry. 2000. Fitting population models incorporating process noise and observation error. University of Texas, Austin. Austin, TX.
Presentations de Valpine, Perry. 2001. Analysis of experimental population data with population models. Entomological Society of America Annual Meeting.
Presentations de Valpine, Perry. 2002. Analysis of experimental population data with population models: Better inferences from complex data. Ecological Society of America Annual Meeting.
Journal Article de Valpine, Perry. 2002. Review of methods for fitting time-series models with process and observation error and likelihood calculations for nonlinear, non-Gaussian state-space models. Bulletin of Marine Science. Vol: 70. Pages 455-471.
Journal Article de Valpine, Perry. 2003. Better inferences from population-dynamics experiments using Monte Carlo state-space likelihood methods. Ecology. Vol: 84. Pages 3064-3077.
Journal Article de Valpine, Perry. 2004. Monte Carlo state-space likelihoods by weighted posterior kernel density estimation. Journal of the American Statistical Association. Vol: 99(466). Pages 523-536.
Journal Article de Valpine, Perry; Hilborn, Ray. 2005. State-space likelihoods for nonlinear fisheries time-series . Canadian Journal of Fisheries and Aquatic Sciences. Vol: 62. Pages 1937-1952.
"Analysis of insect population data with structured population models" is project ID: 3080