Comparing statistical methods for detecting climatic drivers of mast seeding

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Understanding the drivers of mast seeding is important for predicting reproductive dynamics in perennial plants. Here, we evaluate the performance of four statistical methods for identifying weather-associated drivers of annual seed production, i.e, weather cues: climate sensitivity profile, P-spline regression, sliding window analysis, and peak signal detection. Using long-term seed production data from 50 European beech (Fagus sylvatica) populations and temperature records, we assessed each method’s ability to detect a benchmark window around the summer solstice. All methods successfully identified biologically meaningful windows, but their performance varied with data quality, signal strength, and sample size. Sliding window and climate sensitivity profile methods showed the best balance of accuracy and robustness, while peak signal detection had lower consistency. Cue identification was more reliable with at least 20 years of data, and predictive accuracy was highest when models were based on seed trap data. A simulation study showed method-specific sensitivity to signal strength, with the sliding window performing best. This simulation further validated the methods by testing their ability to detect a predefined cue window under varying signal strengths. Our findings provide a means to improve masting forecasts through a practical guide for selecting appropriate cue identification methods under varying data constraints. https://doi.org/10.32942/X2C05X Biology, Ecology and Evolutionary Biology, Forest Sciences, Life Sciences, Plant Sciences phenology, seed production, Weather, climate change Published: 2025-06-16 23:05 Last Updated: 2025-09-16 05:42 CC BY Attribution 4.0 International Data and Code Availability Statement: Data are archived on the OSF Repository at the following link: \url{https://osf.io/u23vy/}. The case study code is accessible on Github \url{https://github.com/ValentinJourne/weatheRcues/tree/main/Application_MASTREE}. A R vignette tutorial is available at \url{https://valentinjourne.github.io/weatheRcues/articles/weatheRcues.html}. Language: English