Response and sensitivity of urban plants with different seed dispersal modes

To check whether the responses of spontaneous plant richness to driving factors differ among dispersal modes, generalized linear mixed effect models (GLMMs) were used to analyze the driving factors of the richness of species with the four seed dispersal modes: anemochory, autochory, hydrochory and zoochory. We fitted a model with species richness as the response variable, using a Poisson distribution and “glmer” function within R package lme4 (version 1.1-35.1) with patch ID nested within site ID, and site ID nested within city ID as random effect (1|patch ID/site ID/city ID). As fixed effects, we used all natural factors, dispersal limitation, and habitat quality described above, as well as the interaction between these factors and seed dispersal modes (hereafter called ‘full model’). To start, the independent variables were standardized and centered to ensure uniform dimensionality. To avoid collinearity, if the correlation coefficient between independent variables was greater than 0.7, only the variable of the highest correlation with the dependent variables was chosen for further modelling. After model construction, the variance inflation factor (VIF) of the model was checked, and variables with VIF greater than 5 were removed from the model sequentially until all variables had a VIF ≤ 5. Residual diagnostics of models was then checked using the “simulateResiduals” function in R package DHARMa (version 0.4.6). The “Anova” function in the R package car (version 3.1-2) was employed to assess both the main effects and interaction effects in the full model. To assess differences in the response to predictor variables between dispersal modes, we used the “emtrends” function in the R package emmeans (version 1.10.1) to compare trend estimates of for the different dispersal modes. To further explore the driving factors within a dispersal mode, we separately ran the same GLMM analysis described above for each group. The “dredge” function in the R package MuMIn (version 1.47.5) was used on each separate model, which created a suite of models with all possible combinations of the initial variables and sorted them according to the Akaike Information Criterion (AIC). We then conducted model averaging on all models with ΔAIC &lt; 2 using the function “model.avg”. We used the “r.squaredGLMM” function in the R package MuMIn to calculate R2, then manually performed variance decomposition to assess the relative contribution of each driving factor and the random effects. We ran all analyses using the software R 4.3.2. <br>

为探究野生植物物种丰富度对驱动因子的响应是否因种子传播模式（seed dispersal modes）而异，本研究采用广义线性混合效应模型（generalized linear mixed effect models, GLMMs），针对4类种子传播模式——风媒传播（anemochory）、自播传播（autochory）、水媒传播（hydrochory）与动物媒传播（zoochory）的物种丰富度驱动因子展开分析。我们以物种丰富度作为响应变量，基于泊松分布，借助R语言lme4包（版本1.1-35.1）中的glmer函数构建模型，将patch ID嵌套于site ID、site ID嵌套于city ID作为随机效应（1|patch ID/site ID/city ID）。固定效应纳入前文所述的所有自然因子、传播限制因子与生境质量指标，以及上述因子与种子传播模式的交互项，即后文所称的“全模型”。建模前，先对自变量进行标准化与中心化处理，以统一量纲。为避免共线性问题，若自变量间相关系数大于0.7，则仅保留与因变量相关性最高的变量用于后续建模。模型构建完成后，检验模型的方差膨胀因子（variance inflation factor, VIF），依次移除VIF大于5的变量，直至所有变量的VIF均≤5。随后，借助R包DHARMa（版本0.4.6）中的simulateResiduals函数完成模型残差诊断。使用R包car（版本3.1-2）中的Anova函数，评估全模型中的主效应与交互效应。为比较不同传播模式对预测变量的响应差异，我们采用R包emmeans（版本1.10.1）中的emtrends函数，对比不同传播模式的趋势估计值。为进一步探究单一传播模式下的驱动因子，我们针对每类传播模式组分别运行上述广义线性混合效应模型分析。借助R包MuMIn（版本1.47.5）中的dredge函数，对每个独立模型生成所有初始变量组合的候选模型集，并依照赤池信息准则（Akaike Information Criterion, AIC）对模型进行排序。随后，针对ΔAIC＜2的所有模型，使用model.avg函数进行模型平均。通过R包MuMIn中的r.squaredGLMM函数计算R²，再手动完成方差分解，以评估各驱动因子与随机效应的相对贡献度。所有分析均通过R 4.3.2软件完成。

搜集汇总

背景与挑战

背景概述

该数据集探讨了不同种子传播方式的植物丰富度对多种驱动因素的响应差异，使用了GLMMs模型进行分析，并提供了相关的数据文件。