Starve: An R package for spatio‐temporal analysis of research survey data using nearest‐neighbour Gaussian processes. Issue 3 (12th January 2023)

• Record Type: Journal Article
• Authors: Lawler, Ethan; Field, Chris; Mills Flemming, Joanna
• Is Part Of: Methods in ecology and evolution. Volume 14:Issue 3(2023)
• Page Start: 817
• Page End: 830
• Publication Date: 2023-01-12
• Subjects: generalized linear mixed model -- hierarchical model -- nearest‐neighbour Gaussian process -- software -- spatio‐temporal analysis -- species distribution model; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.14053
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Spatio‐temporal datasets that are difficult to analyse are commonly derived from ecological surveys. There are software packages available to analyse these datasets, but many of them require advanced coding skills. There is a growing need for easy‐to‐use packages that researchers can use to analyse common ecological datasets. We develop a particular generalized linear mixed model framework for spatio‐temporal point‐referenced data that is flexible enough to accommodate data from most ecological surveys while being structured enough to facilitate analyses without advanced coding. Our implementation in the starve package uses a computationally efficient version of a nearest‐neighbour Gaussian process enabling analysis of relatively large datasets. A tutorial analysis of a Carolina wren survey presents a recommended workflow for analyses while showcasing the capabilities of the package. Our model and package are tools that can easily be added to researchers' routine to help make sense of data from ecological surveys. We emphasize the ability of our model to create fine‐scale spatio‐temporal predictions which can then be used to visualize and identify important trends in species distributions.