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我正在尝试绘制 PCoA，但该图对我来说没有多大意义。

以前，我使用 10 种沿海植物物种在 5 种不同处理（改变水和盐度）下的重要性数据运行 PERMANOVA (adonis)，并且为此我使用了 Bray-Curtis 矩阵。使用相同的数据，我正在尝试绘制 PCoA。我的对象太大，所以我只选择了其中的一部分作为示例。这是代码：

library(vegan)
library(ape)

dput(meus_dados)

structure(list(Treatment = c("T1", "T1", "T1", "T1", "T1", "T2", 
"T2", "T2", "T2", "T2", "T3", "T3", "T3", "T3", "T3", "T4", "T4", 
"T4", "T4", "T4", "T5", "T5", "T5", "T5", "T5"), Sal = c(20L, 
20L, 20L, 20L, 20L, 3L, 3L, 3L, 3L, 3L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), Agua = c(6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 2L, 2L, 2L, 2L, 2L, 15L, 
15L, 15L, 15L, 15L, 6L, 6L, 6L, 6L, 6L), Sp1 = c(0.794290128748461, 
0.676055337749016, 0.649817348325361, 0.490593956384099, 0.460140400409192, 
0.356605528960497, 0, 0.410011047234125, 0.485048880341384, 0.380487296882943, 
0.478130491326628, 0.393925420118031, 0.509315406411044, 0.548767646671349, 
0.249824697144633, 0.588139655317169, 0.458489317544165, 0.508041166651013, 
0.489174836104582, 0.508196906269527, 0.627466911428099, 0.774643095726598, 
0.543312871998383, 0.430149253292226, 0.488483347062045), Sp2 = c(0.31950289567597, 
0.658313387776009, 0.688008327519027, 0.586911420488643, 0.580577992032451, 
0.356605528960497, 0.239378990887995, 0.225481709273101, 0.242524440170692, 
0.194777553950768, 0.888178597896676, 0.996509961427144, 0.795541435184066, 
0.713158936565669, 0.802719823057339, 0.543860201049459, 0.495818132429415, 
0.455672570231077, 0.36522265070534, 0.368021150346472, 0.774093342394432, 
0, 0.38506500075798, 0.524676787317157, 0.394399669660163), Sp3 = c(0, 
0, 0, 0, 0, 0, 0.0926044346179928, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0.0913056626763351, 0.145824930692855, 0, 0.127252209323957, 
0.0962662501894949, 0, 0), Sp4 = c(0, 0, 0, 0, 0, 0.257361359186398, 
0.146774556270003, 0.174456464424683, 0.187642521006036, 0.154357559497079, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Sp5 = c(0.31950289567597, 
0, 0.419594374295962, 0.316782038357181, 0.338654327178036, 0, 
0, 0, 0, 0, 0.35726949591146, 0.304782309227413, 0.440485455199368, 
0.233496302294192, 0.322893736936447, 0, 0, 0, 0, 0, 0.261044554272076, 
0.201689979912391, 0.333026509713188, 0.391728749895399, 0.254877089092083
), Sp6 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.214884344687665, 
0.0919770920816606, 0, 0, 0, 0, 0, 0, 0), Sp7 = c(0.31950289567597, 
0.238365530436068, 0, 0, 0, 0.43497144733486, 0.525291741706045, 
0.467329790348523, 0.485048880341384, 0.389555107901536, 0, 0, 
0, 0, 0, 0.5386670892028, 0.406489870695075, 0.350189434253656, 
0.464514166061593, 0.464110931651819, 0, 0.328942189236348, 0.248844646824885, 
0.253151032233496, 0.341099671900618), Sp8 = c(0.247201184223628, 
0.238365530436068, 0.24257994985965, 0.514142039871732, 0.465470460285241, 
0.16237693893029, 0.185208869235986, 0.174456464424683, 0.187642521006036, 
0.194777553950768, 0.276421414865236, 0.304782309227413, 0.254657703205522, 
0.233496302294192, 0.249824697144633, 0.219555369620381, 0.18509129990788, 
0.183954184163321, 0.18261132535267, 0.184010575173236, 0.337395191905393, 
0.567472525800706, 0.393484720516069, 0.400294177261723, 0.521140222285091
), Sp9 = c(0, 0, 0, 0, 0, 0.269702257697169, 0.440323668810008, 
0.410011047234125, 0.41209275713447, 0.588656150841522, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Sp10 = c(0, 0.188900213602838, 
0, 0, 0.15515682009508, 0, 0, 0, 0, 0, 0, 0, 0, 0.271080812174598, 
0.374737045716949, 0.109777684810191, 0.239227034735801, 0.318188460537611, 
0.315865696423145, 0.237830218279473, 0, 0, 0, 0, 0)), class = "data.frame", row.names = c(NA, 
-25L))

dist <- vegdist(meus_dados[,-c(1:3)], method="bray")

groups <-meus_dados$Treatment
groups=as.factor(groups)


pcoa <- cmdscale(dist)
efit <- envfit(pcoa, meus_dados[,2:3])
plot(pcoa, col = c("black", "orange", "pink", "blue", "green")[groups], pch = c(19,1, 24,5,6,7,9)[groups],
     xlim = c(-.3,0.3), ylim=c(-.3, .2),
     xlab = "PCoA 1", ylab = "PCoA 2")
abline(h = 0, v = 0, lty = 2)
plot(efit, col = "red", cex = 0.9)

这就是我得到的（agua表示水，sal表示盐度）：

当前结果

我想绘制物种（名称）来代替点和处理，如矢量。这可能吗？

我已经为每种治疗分别尝试了另一种方法，如 MDS（使用 metaMDS 函数），但我得到了错误：

min 没有不可缺少的参数；返回 Inf

有人可以建议我如何以更好的可视化方式绘制 PCoA 或提出更好的建议吗？我在这里阅读了其他问题，但我真的找不到任何我可以使用或适应的东西。

非常感谢，T。

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