Hydrothermal-time-to-event models for seed germination

Abstract

Time-to-event methods have been proposed in the agricultural sciences, as one of the most suitable options for the analysis of seed germination data. In contrast to traditional linear/nonlinear regression, time-to-event methods can easily account for all statistical peculiarities inherited in germination assays, such as censoring, and they can produce unbiased estimates of model parameters and their standard errors. So far, these methods have only been used in combination with empirical models of germination, which are lacking biological underpinnings. We bridge the gap between statistical requirements and biological understanding by developing a general method that formulates biologically-oriented hydro time (HT), thermal time (TT) and hydrothermal time (HTT) models into a time-to-event framework. HT, TT, and HTT models are widely used for describing seed germination and emergence of plants as affected by the environmental temperature and/or water potential. Owing to their simplicity and the direct biological interpretation of model parameters, these models have become one of the most common tools for both predicting germination as well as understanding the physiology of germination responses to environmental factors. However, these models are usually fitted by using nonlinear regression and, therefore, they fall short of statistical rigor when inference about model parameters is of interest. In this study, we develop HT-to-event, TT-to-event and HTT-to-event models and provide a readily available implementation relying on the package "drc" in the R statistical environment. Examples of usage are also provided and we highlight the possible advantages of this procedure, such as efficiency and flexibility.

Temperature and storage time strongly affect the germination success of perennial Euphorbia species in Mediterranean regions

“…For each Petri dish, the observed counts were used to parameterize a log-logistic germination model, by using a time-to-event modeling platform (Onofri, Benincasa, Mesgaran, & Ritz, 2018;Ritz, Pipper, & Streibig, 2013). The fitted model was used to derive the final percentage of germinated seeds (FPG) and the time to 50% germination (T 50 ), which were submitted to ANOVA.…”

Effects of Sub-Optimal Temperatures on Seed Germination of Three Warm-Season Turfgrasses with Perspectives of Cultivation in Transition Zone

“…In order to evaluate the effect of temperature on germination rates, the time-course of germination was determined for each of the 336 treatment combinations (4 temperature levels × 7 cvs × 6 replicates × 2 runs). Model fitting was accomplished by using a time-to-event model, considering a Weibull distribution of germination times [35,36]. The cumulative probability function was:…”

Basic Features of the Analysis of Germination Data with Generalized Linear Mixed Models

“…On the other hand, survival analysis is suitable to infer statistical significance of differences between treatments applied to un-replicated binomial samples [13,14,16,29]. This, however, does not mean that a single replication of treatments is usually adequate: Testing replicate batches of seeds is often necessary to estimate background variability in the population and, thus, pointing out other sources of variability that could affect the response of the sample [8].…”

“…Hence, as a further implementation, GzLMMs might be used to perform hydrotime, thermaltime, and hydrothermaltime modeling of germination time-courses, since GzLMMs do not have the theoretical problems that affect LMMs and GzLMs [26]. It would also be interesting to compare such an approach with hydrothermal-time-to-event models for seed germination [29].…”