Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associations
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...
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Wiley
2025-07-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.70079 |
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| author | Hao Ran Lai |
| author_facet | Hao Ran Lai |
| author_sort | Hao Ran Lai |
| collection | DOAJ |
| description | 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 to extract more information out of multispecies phenological data beyond circular statistics. Synthesis. 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. |
| format | Article |
| id | doaj-art-45b39e89031d4f51a4234ad499e90ced |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-45b39e89031d4f51a4234ad499e90ced2025-08-20T03:28:41ZengWileyMethods in Ecology and Evolution2041-210X2025-07-011671542154610.1111/2041-210X.70079Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associationsHao Ran Lai0School of Biological Sciences University of Canterbury Christchurch New ZealandAbstract 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 to extract more information out of multispecies phenological data beyond circular statistics. Synthesis. 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.https://doi.org/10.1111/2041-210X.70079circular statisticscosinor rhythmometrygeneralised linear latent variable modelphenologyregressionsampling bias |
| spellingShingle | Hao Ran Lai Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associations Methods in Ecology and Evolution circular statistics cosinor rhythmometry generalised linear latent variable model phenology regression sampling bias |
| title | Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associations |
| title_full | Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associations |
| title_fullStr | Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associations |
| title_full_unstemmed | Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associations |
| title_short | Model‐based ordination for phenological studies: From controlling sampling bias to inferring temporal associations |
| title_sort | model based ordination for phenological studies from controlling sampling bias to inferring temporal associations |
| topic | circular statistics cosinor rhythmometry generalised linear latent variable model phenology regression sampling bias |
| url | https://doi.org/10.1111/2041-210X.70079 |
| work_keys_str_mv | AT haoranlai modelbasedordinationforphenologicalstudiesfromcontrollingsamplingbiastoinferringtemporalassociations |