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我查看了在线论坛和各种论文，对我对 RDA 分析结果的解释感到有些困惑。

我在该条件下运行了带有遗传簇的完整模型，并使用 anova.cca() 函数使用方差分析 (PERMANOVA) 的排列提出了一个具有全局测试的重要模型。

signif.full.c <- anova.cca(gno.rda.c)
signif.full.c
#Permutation test for rda under reduced model
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + lat + Depth + Condition(Clusters), data = gno.clusters, scale = T)
#Df Variance      F Pr(>F)  
#Model      4    221.0 1.0546  0.007 **
#  Residual 100   5239.3

然后我查看 RDA 轴，它们都不重要：

signif.axis.c <- anova.cca(gno.rda.c, by="axis")
signif.axis.c
#Permutation test for rda under reduced model
#Forward tests for axes
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + lat + Depth + Condition(Clusters), data = gno.clusters, scale = T)
#Df Variance      F Pr(>F)
#RDA1       1     58.0 1.1078  0.123
#RDA2       1     56.3 1.0740  0.307
#RDA3       1     55.3 1.0549  0.302
#RDA4       1     51.4 0.9816  0.686
#Residual 100   5239.3

但是，看看“边距”排列，它着眼于术语的重要性，我得到了经度和深度的显着结果：

signif.margin.c <- anova.cca(gno.rda.c, by="margin")
signif.margin.c
#Permutation test for rda under reduced model
#Marginal effects of terms
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + lat + Depth + Condition(Clusters), data = gno.clusters, scale = T)
#Df Variance      F Pr(>F)  
#long       1     56.2 1.0717  0.027 *
#  lat        1     53.9 1.0285  0.214  
#Depth      2    112.8 1.0762  0.007 **
#  Residual 100   5239.3

我从模型中删除了纬度，模型和术语一样重要，但 RDA 轴也不重要：

#Permutation test for rda under reduced model
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + Depth + Condition(Clusters), data = gno.clusters, scale = T)
#Df Variance     F Pr(>F)  
#Model      3    167.1 1.063  0.005 **
#  Residual 101   5293.2  

#Permutation test for rda under reduced model
#Marginal effects of terms
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + Depth + Condition(Clusters), data = gno.clusters, scale = T)
#Df Variance      F Pr(>F)  
#long       1     55.9 1.0657  0.039 *
#  Depth      2    112.4 1.0719  0.015 *
#  Residual 101   5293.2  

#Permutation test for rda under reduced model
#Forward tests for axes
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + Depth + Condition(Clusters), data = gno.clusters, scale = T)
#Df Variance      F Pr(>F)
#RDA1       1     57.1 1.0900  0.165
#RDA2       1     56.0 1.0681  0.178
#RDA3       1     54.0 1.0308  0.245
#Residual 101   5293.2

这是否意味着我可以忽略模型重要性和术语重要性，因为 RDA 轴不重要？

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