Modelling phenology using ordered categorical generalized additive models

TL;DR

This paper introduces a flexible method for modeling phenological data as ordered categories using generalized additive models in R, improving inference by respecting the data's natural structure.

Contribution

It presents a novel approach to analyze phenological data with ordered categorical models, addressing limitations of traditional continuous or transition-based methods.

Findings

Applied method to Greenland saxifrage phenology data

Provided comprehensive model checking and visualization tools

Demonstrated improved inference over simpler models

Abstract

One form of data collected in ecology is phenological, describing the timing of life stages. It can be tempting to analyze such data using a continuous distribution or to model individual transitions via probit/logit models. Such simplifications can lead to incorrect inference in various ways, all of which stem from ignoring the natural structure of the data. This paper presents a flexible approach to modelling ordered categorical data using the popular R package `mgcv`. An example analysis of saxifrage phenology in Greenland including useful plots, model checking and derived quantities is included.