Influence of different predictors on the probability of <i>Spiroplasma</i> infection [Pr(Spiro+)] in generalized linear models (GLMM).

Results from generalized linear mixed models (GLMM), where the random effect was watershed (WS) with random intercept (RI), and the fixed effects were season (SN) and/or nuclear genetic background (GB). The table includes the model name, the fixed effect(s), the degrees of freedom (Df), the Akaike information criterion (AIC) with the best model in bold, the Bayesian information criterion (BIC) with the best model in bold, the log likelihood (logLik), the deviance (Dev) of the model, the analysis of variance (ANOVA) used to test for the model’s improvement, the ANOVA Chi square value (ChiSq), the Chi Square degrees of freedom (ChiDf), and the ANOVA p-value. Adding SN was the only fixed effect that significantly improved the model. In contrast, GB did not significantly improve the GLMM. Additionally, changing the order introducing fixed effects or adding random slope also did not improve the GLMM (S3 Table). For all p-values, the level of significance was marked *** if < 0.0001, ** if < 0.001, * if < 0.05, and not marked if > 0.05.本数据集包含广义线性混合模型（Generalized Linear Mixed Models, GLMM）的分析结果，其中随机效应为流域（WS），采用随机截距（RI），固定效应为季节（SN）和/或核遗传背景（GB）。该表格涵盖以下内容：模型名称、固定效应项、自由度（Df）、赤池信息准则（Akaike Information Criterion, AIC）（最优模型以加粗字体标注）、贝叶斯信息准则（Bayesian Information Criterion, BIC）（最优模型以加粗字体标注）、对数似然值（Log Likelihood, logLik）、模型偏差值（Deviance, Dev）、用于检验模型拟合优度提升效果的方差分析（Analysis of Variance, ANOVA）结果、方差分析卡方值（Chi-Square, ChiSq）、卡方自由度（Chi-Square Degrees of Freedom, ChiDf）以及方差分析p值。仅将季节（SN）作为固定效应纳入模型，可显著提升该GLMM的拟合效果；相较而言，核遗传背景（GB）未能显著优化模型性能。此外，调整固定效应的引入顺序或添加随机斜率，均未对GLMM模型的拟合效果产生显著提升（详见补充表S3）。所有p值的显著性标记规则如下：若p<0.0001，则标记为***；若p<0.001，则标记为**；若p<0.05，则标记为*；若p>0.05，则不添加显著性标记。