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rGAI: An R package for fitting the generalised abundance index to seasonal count data
  • Emily Dennis,
  • Calliste Fagard-Jenkin,
  • Byron Morgan
Emily Dennis
University of Kent

Corresponding Author:[email protected]

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Calliste Fagard-Jenkin
University of St Andrews
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Byron Morgan
University of Kent
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1. The Generalised Abundance Index (GAI) provides a useful tool for estimating relative population sizes and trends of seasonal invertebrates from species’ count data, and offers potential for inferring which external factors may influence phenology and demography through parametric descriptions of seasonal variation. 2. We provide an R package that extends previous software with the ability to include covariates when fitting parametric GAI models, where seasonal variation is described by either a mixture of Normal distributions or a stopover model which provides estimates of lifespan. The package also generalises the model to allow any number of broods/generations in the target population within a defined season. The option to perform bootstrapping, either parametrically or non-parametrically, is also provided. 3. The new package allows models to be far more flexible when describing seasonal variation, which may be dependent on site-specific environmental factors or consist of many broods/generations which may overlap, as demonstrated by two case studies. 4. Our open-source software, available at \href{https://github.com/calliste-fagard-jenkin/GAI}{https://github.com/calliste-fagard-jenkin/rGAI}, makes this extension widely and freely available, allowing the complexity of GAI models used by ecologists and applied statisticians to increase accordingly.
03 Nov 2021Submission Checks Completed
03 Nov 2021Assigned to Editor
04 Nov 2021Reviewer(s) Assigned
24 Dec 2021Review(s) Completed, Editorial Evaluation Pending
31 Dec 2021Editorial Decision: Revise Minor
28 Jun 20221st Revision Received
28 Jun 2022Submission Checks Completed
28 Jun 2022Assigned to Editor
28 Jun 2022Review(s) Completed, Editorial Evaluation Pending
12 Jul 2022Editorial Decision: Accept
Aug 2022Published in Ecology and Evolution volume 12 issue 8. 10.1002/ece3.9200