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Fitting host-parasitoid models with CV² > 1 using hierarchical generalized linear models

Fitting host-parasitoid models with CV² > 1 using hierarchical generalized linear models

Perry, Joe N., Noh, Maeng Seok, Lee, Youngjo, Alston, Robert D., Norowi, H. Mohd., Powell, Wilf and Rennolls, Keith (2000) Fitting host-parasitoid models with CV² > 1 using hierarchical generalized linear models. Proceedings of The Royal Society B: Biological Sciences, 267 (1457). pp. 2043-2048. ISSN 1471-2954 (doi:10.1098/rspb.2000.1247)

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Abstract

The powerful general Pacala-Hassell host-parasitoid model for a patchy environment, which allows host density–dependent heterogeneity (HDD) to be distinguished from between-patch, host density–independent heterogeneity (HDI), is reformulated within the class of the generalized linear model (GLM) family. This improves accessibility through the provision of general software within well–known statistical systems, and allows a rich variety of models to be formulated. Covariates such as age class, host density and abiotic factors may be included easily. For the case where there is no HDI, the formulation is a simple GLM. When there is HDI in addition to HDD, the formulation is a hierarchical generalized linear model. Two forms of HDI model are considered, both with between-patch variability: one has binomial variation within patches and one has extra-binomial, overdispersed variation within patches. Examples are given demonstrating parameter estimation with standard errors, and hypothesis testing. For one example given, the extra-binomial component of the HDI heterogeneity in parasitism is itself shown to be strongly density dependent.

Item Type: Article
Uncontrolled Keywords: host–parasitoid interactions, patch dynamics, Pacala–Hassell model, CV², heterogeneity, density dependence
Subjects: Q Science > QA Mathematics > QA76 Computer software
Q Science > QH Natural history > QH301 Biology
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Statistics & Operational Research Group
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Last Modified: 14 Oct 2016 09:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/514

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