Model-based ordination for phenological studies: from controlling sampling bias to inferring temporal associations

2025 · doi:10.32942/x25h0b
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Abstract

Willig et al. (Methods in Ecology and Evolution, 15, 868--885, 2024) cautioned that unequal sampling effort and pseudoreplication can bias the characterisation of species phenology using circular statistics. Borrowing concepts from rarefaction, they proposed bootstrapping to control for time-varying marginal totals that arise from unequal sampling effort over time. This study extends their cautionary notes to regressions of phenological time series, where bootstrapping can be replaced by various built-in functionalities of generalised linear mixed-effect models. I further take this opportunity to borrow a key innovation in model-based ordination and joint species distribution modelling --- generalised linear latent variable models (GLLVM) --- to illustrate its ability in extracting more information out of multispecies phenological data beyond circular statistics. With sampling-bias adjustment, GLLVMs, or regressions in general, are robust predictive and inferential tools that enrich our phenological understandings in conjunction with circular statistics for hypothesis testing.
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Abstract

Willig et al. (Methods in Ecology and Evolution, 15, 868--885, 2024) cautioned that unequal sampling effort and pseudoreplication can bias the characterisation of species phenology using circular statistics. Borrowing concepts from rarefaction, they proposed bootstrapping to control for time-varying marginal totals that arise from unequal sampling effort over time. This study extends their cautionary notes to regressions of phenological time series, where bootstrapping can be replaced by various built-in functionalities of generalised linear mixed-effect models. I further take this opportunity to borrow a key innovation in model-based ordination and joint species distribution modelling --- generalised linear latent variable models (GLLVM) --- to illustrate its ability in extracting more information out of multispecies phenological data beyond circular statistics. With sampling-bias adjustment, GLLVMs, or regressions in general, are robust predictive and inferential tools that enrich our phenological understandings in conjunction with circular statistics for hypothesis testing. DOI https://doi.org/10.32942/X25H0B Subjects Biostatistics, Ecology and Evolutionary Biology, Integrative Biology, Longitudinal Data Analysis and Time Series, Multivariate Analysis, Statistical Methodology

Keywords

generalised linear latent variable model, time series, circular statistics, cosinor rhythmometry, regression, species associations, sampling effort, pseudoreplication, GLMM Dates Published: 2025-03-17 18:37 Last Updated: 2025-08-01 12:12 Older Versions License CC-BY Attribution-NonCommercial 4.0 International Additional Metadata Conflict of interest statement: None Language: English

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