Phylogenetic estimates of species-level phenology improve ecological forecasting

Ignacio Morales-Castilla (), T. J. Davies, Geoffrey Legault, D. M. Buonaiuto, Catherine J. Chamberlain, Ailene K. Ettinger, Mira Garner, Faith A. M. Jones, Deirdre Loughnan, William D. Pearse, Darwin S. Sodhi and E. M. Wolkovich
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Ignacio Morales-Castilla: University of Alcalá
T. J. Davies: University of British Columbia
Geoffrey Legault: University of British Columbia
D. M. Buonaiuto: Harvard University
Catherine J. Chamberlain: Harvard University
Ailene K. Ettinger: Arnold Arboretum of Harvard University
Mira Garner: University of British Columbia
Faith A. M. Jones: University of British Columbia
Deirdre Loughnan: University of British Columbia
William D. Pearse: Imperial College London
Darwin S. Sodhi: University of British Columbia
E. M. Wolkovich: University of British Columbia

Nature Climate Change, 2024, vol. 14, issue 9, 989-995

Abstract: Abstract The ability to adapt to climate change requires accurate ecological forecasting. Current forecasts, however, have failed to capture important variability in biological responses, especially across species. Here we present a new method using Bayesian hierarchical phylogenetic models and show that species-level differences are larger than the average differences between cues. Applying our method to phenological experiments manipulating temperature and day length we show an underlying phylogenetic structure in plant phenological responses to temperature cues, whereas responses to photoperiod appear weaker, more uniform across species and less phylogenetically constrained. We thus illustrate how a focus on certain clades can bias prediction, but that predictions may be improved by integrating information on phylogeny to better estimate species-level responses. Our approach provides an advance in ecological forecasting, with implications for predicting the impacts of climate change and other anthropogenic forces on ecosystems.

Date: 2024
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DOI: 10.1038/s41558-024-02102-2

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