🤖 AI Summary
Phenological data inherently possess an ordinal categorical structure, yet conventional continuous-distribution or binary models often neglect this property, leading to biased ecological inference. To address this, we propose an Ordinal Generalized Additive Model (O-GAM) implemented in R using the *mgcv* package— the first to integrate an ordinal logistic link function with smooth terms, thereby explicitly preserving the ordered nature of phenological stages while flexibly modeling nonlinear effects of environmental covariates. The method supports Bayesian confidence interval estimation and systematic residual diagnostics, enhancing interpretability and robustness. Applied to Greenland saxifrage phenology data, O-GAM accurately detects phenological trends, quantifies environmental drivers, and generates ecologically meaningful derived metrics. This approach establishes a new paradigm for phenological modeling that balances statistical rigor with ecological plausibility.
📝 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.