rGAI: An R package for fitting the generalized abundance index to seasonal count data

Area of Science:

• Ecology
• Population Dynamics
• Statistical Modeling

Background:

• The generalized abundance index (GAI) is a valuable tool for estimating relative population sizes and trends of seasonal invertebrates using count data.
• GAI models offer potential for inferring external factors influencing phenology and demography through parametric descriptions of seasonal variation.

Purpose of the Study:

• To introduce a new R package that extends existing software for fitting parametric GAI models.
• To enhance GAI models by incorporating covariates, allowing for flexible descriptions of seasonal variation.
• To generalize GAI models to accommodate multiple broods/generations and provide bootstrapping options.

Main Methods:

• The R package implements parametric GAI models using either a mixture of Normal distributions or a stopover model to describe seasonal variation.
• The models are generalized to handle any number of broods/generations within a season.
• The package includes options for parametric and nonparametric bootstrapping.

Main Results:

• The new package allows for more flexible modeling of seasonal variation, accommodating site-specific environmental factors.
• The extended GAI models can effectively describe complex seasonal patterns, including overlapping broods/generations, as demonstrated in case studies.
• The open-source software is freely available, facilitating wider adoption and increased complexity in GAI model applications.

Conclusions:

• The developed R package significantly enhances the flexibility and applicability of the generalized abundance index for ecological studies.
• The software enables more accurate estimation of population sizes and trends by incorporating environmental covariates and multiple generations.
• This tool empowers ecologists and statisticians to utilize more sophisticated GAI models for understanding seasonal invertebrate dynamics.