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>

为探究自生植物丰富度对驱动因子的响应是否因种子扩散模式而异，本研究采用广义线性混合效应模型（generalized linear mixed effect models, GLMMs），针对风媒传播（anemochory）、自播传播（autochory）、水媒传播（hydrochory）与动物媒传播（zoochory）四类种子扩散模式对应的物种丰富度，分析其驱动因子。
我们以物种丰富度作为响应变量，基于泊松分布（Poisson distribution），使用R软件lme4包（版本1.1-35.1）中的“glmer”函数构建模型，将斑块ID（patch ID）嵌套于位点ID（site ID）、位点ID嵌套于城市ID（city ID）的分组作为随机效应（1|patch ID/site ID/city ID）。固定效应纳入前述提及的所有自然因子、扩散限制与生境质量，以及这些因子与种子扩散模式的交互项，即后文所述的“全模型”。
分析伊始，我们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（collinearity）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，我们对模型的方差膨胀因子（variance inflation factor, VIF）进行检验，依次剔除VIF大于5的变量，直至所有变量的VIF均≤5。随后使用R包DHARMa（版本0.4.6）中的“simulateResiduals”函数对模型开展残差诊断。我们借助R包car（版本3.1-2）中的“Anova”函数，对全模型的主效应与交互效应进行评估。
为比较不同扩散模式对预测变量的响应差异，我们使用R包emmeans（版本1.10.1）中的“emtrends”函数，对不同扩散模式的趋势估计值进行对比。为进一步探究单一扩散模式下的驱动因子，我们针对每类扩散模式分别运行前述相同的GLMM分析。
针对每个独立模型，我们使用R包MuMIn（版本1.47.5）中的“dredge”函数，生成包含初始变量所有可能组合的模型集，并依据赤池信息准则（Akaike Information Criterion, AIC）对模型进行排序。随后，我们针对所有ΔAIC＜2的模型，使用“model.avg”函数开展模型平均。借助MuMIn包中的“r.squaredGLMM”函数计算R²，再通过手动方差分解（variance decomposition）评估各驱动因子与随机效应的相对贡献。
所有分析均通过R 4.3.2软件完成。