Bulbous perennials precisely detect the length of winter and adjust flowering dates

Abstract

Summary In order to identify the most relevant environmental parameters that regulate flowering time of bulbous perennials, first flowering dates of 329 taxa over 33 yr are correlated with monthly and daily mean values of 16 environmental parameters (such as insolation, precipitation, temperature, soil water content, etc.) spanning at least 1 yr back from flowering. A machine learning algorithm is deployed to identify the best explanatory parameters because the problem is strongly prone to overfitting for traditional methods: if the number of parameters is the same or greater than the number of observations, then a linear model can perfectly fit the dependent variable (observations). Surprisingly, the best proxy of flowering date fluctuations is the daily snow depth anomaly, which cannot be a signal itself, however it should be related to some integrated temperature signal. Moreover, daily snow depth anomaly as proxy performs much better than mean soil temperature preceding the flowering, the best monthly explanatory parameter. Our findings support the existence of complicated temperature sensing mechanisms operating on different timescales, which is a prerequisite to precisely observe the length and severity of the winter season and translate for example, ‘lack of snow’ information to meaningful internal signals related to phenophases.

Winter belowground: Changing winters and the perennating organs of herbaceous plants

“…In mountains, there can be great spatial variability in plant responses because of topographic heterogeneity (Inouye, 2008; Klimešová et al., 2013; Scherrer & Körner, 2010), increasing the importance not only of macroclimatic conditions, but also microclimatic conditions, for herb growth (Winkler et al., 2016). Flowering time is also linked to snow cover variation, and the daily difference in snow depth as compared to the mean value for the same calendar day can be the greatest predictor for bulbous perennials (Jánosi et al., 2020).…”

Testing machine learning algorithms on a binary classification phenological model

“…The differences in the three key parameters (meteorological factors, length of the time window, N , and the number of sample sizes, S ) involved in the data‐preparation stage of the binary classification model between different species might provide an important reference for exploring the driving factors affecting plant phenology in natural conditions. Meteorological factors as parameters can help us to infer the most relevant climatic factors regulating plant phenology in different climatic zones (Janosi et al, 2020). A single meteorological factor or a combination of meteorological factors was selected as an input feature of the model, and the results showed that temperature was the most effective meteorological factor (Figure 7).…”

Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data

“…Plant phenology was already investigated in Hungary, in studies focusing on leaf phenology, flowering time, or other aspects of the phenological cycle [9,29,[55][56][57][58][59][60][61]. In a study looking at the flowering of six mixed herbaceous and woody species between 1951 and 2000, the date of flowering advanced by 1.9-4.4 days/decade in four out of the six species [60].…”