DOI: 10.57760/sciencedb.08378
title: Plant hormones drive the arms race between seed germination of oak and hoarding behaviour of rodents
title: Plant hormones drive the arms race between seed germination of oak and hoarding behaviour of rodents

description: Statistical analyses were performed using SPSS (version 22.0; SPSS Inc., Chicago, IL, USA) and R version 4.0.3 (R Core Team, 2020). The Chi-square test or ANOVA was used to test the differences in the germination rate, root length, and seedling establishment of partially eaten seeds and intact seeds. One-way analysis of variance (ANOVA) was used to identify significant differences in the degree of feeding, state of partial feeding, and mean germination time of different treatments. Linear regression was used to compare the relationship between the germination time of partially eaten and intact seeds under field conditions. Repeated-measures ANOVA using a general linear model was used to compare the differences in water uptake and weight gain of seeds between different treatments, and a paired t-test was used to compare differences in hormone content changes before and after seed germination and seeds were partially damaged. The Chi-square test was also used to test differences in germination proportion and germination time between different treatments and the visiting frequency and time of the four treatment odours by rodents. A generalised linear mixed model (GLMMs) with a binomial error distribution and logit link function (function glmer, package “lme4”) (Bates et al. 2015) was used to analyse the effect of fixed factors (seed types with different treatments) on seed fates (including the probability of seeds being eaten or hoarded), and with enclosure or patch and station as the random effects. Significant differences in seed fates between different treatment groups were evaluated by least-squares means (the pairs function in the package ‘emmeans’). One-way ANOVA was used to test the differences in the compound contents of VOCs among the four treatment groups. For models with non-normal residuals, the response variables were log-transformed to obtain a better approximation of the normal residuals
description: Statistical analyses were performed using SPSS (version 22.0; SPSS Inc., Chicago, IL, USA) and R version 4.0.3 (R Core Team, 2020). The Chi-square test or ANOVA was used to test the differences in the germination rate, root length, and seedling establishment of partially eaten seeds and intact seeds. One-way analysis of variance (ANOVA) was used to identify significant differences in the degree of feeding, state of partial feeding, and mean germination time of different treatments. Linear regression was used to compare the relationship between the germination time of partially eaten and intact seeds under field conditions. Repeated-measures ANOVA using a general linear model was used to compare the differences in water uptake and weight gain of seeds between different treatments, and a paired t-test was used to compare differences in hormone content changes before and after seed germination and seeds were partially damaged. The Chi-square test was also used to test differences in germination proportion and germination time between different treatments and the visiting frequency and time of the four treatment odours by rodents. A generalised linear mixed model (GLMMs) with a binomial error distribution and logit link function (function glmer, package “lme4”) (Bates et al. 2015) was used to analyse the effect of fixed factors (seed types with different treatments) on seed fates (including the probability of seeds being eaten or hoarded), and with enclosure or patch and station as the random effects. Significant differences in seed fates between different treatment groups were evaluated by least-squares means (the pairs function in the package ‘emmeans’). One-way ANOVA was used to test the differences in the compound contents of VOCs among the four treatment groups. For models with non-normal residuals, the response variables were log-transformed to obtain a better approximation of the normal residuals