Dne 10. 10. 23 v 18:26 Barton, Alana Charlotte napsal(a):
Hello,
I was recommended this email by another student for R help;
I am currently analyzing differences in fish assemblages due to temperature
extremes. I am looking at data over multiple years.
I chose three sampling sites within a single bay, the sites are used as
replicates for my analysis. The ultimate goal would be to determine if
there is any difference between assemblages in a given year and is it the
significant difference occurring between a warm and cold year.
I am interested in determining if the assemblages have a significant
difference between species in the the years, thus I am looking to use Bray
Curtis dissimilarity and displaying it in NMDS.
I want to use the transformed Bray-Curtis for NMDS rather than Hellinger
transformation but it is not displaying properly as Bray Curtis will only
display the years not the species names in the matrix. I am fairly new to
vegan and multivariate analysis.
Thank you for your time and help,
Data Code:
#Packages
library(vegan)
library(ggvegan)
library(ggplot2)
library(tidyverse)
#4th root transformation for count data
>BBcounts_transformed<-siteBB^(1/4)
#Bray-Curtis analysis
>BBtrans_bray<-vegdist(BBcounts_transformed, method="bray")
Wish to replace with hellinger transformation for this given code:
#NMDS-unconstrained
#Transform using Hellinger
BB_hel<-decostand(siteBB, method="hellinger")
BB_nmds<-metaMDS(BB_hel,autotransform = F)
#Fortify graphing
fort_BB<-fortify(BB_nmds)
ggplot() +
geom_point(data=subset(fort_BB, Score =='sites'),
mapping = aes(x = NMDS1, y = NMDS2),
color="black",
alpha=0.5) +
geom_segment(data=subset(fort_BB,Score == 'species'),
mapping=aes(x=0,y=0,xend=NMDS1,yend=NMDS2),
arrow=arrow(length = unit(0.015,"npc"),
type="closed"),
color="darkgrey",
size=0.8) +
geom_text(data=subset(fort_BB, Score == 'species'),
mapping=aes(label=Label, x= NMDS1 * 1.3, y= NMDS2 *1.3))+
geom_abline(intercept=0,slope=0,linetype="dashed", size=0.8,
color="gray") +
geom_vline(aes(xintercept=0), linetype="dashed", size=0.8, color="gray") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line=element_line(color="black")
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Dear Alana Charlotte Barton,
If you have a hypothesis to visualize/to test, i.e. the difference
between years, I suggest using some more regression-like technique than
the NMDS.
Given the choice of the package, what about cca() ?
In that, you use your communities directly (not the distances). First,
it might help if you exclude very rare species, e.g. species that
occurred just once or twice (or three times) in the whole dataset.
Then you just run:
cca(communityMatrix ~ yearTemperature), and you are done. Or, taken into
account the site identity as well:
model <- cca(communityMatrix ~ yearTemperature + Condition(Site))
then you just run
anova(model)
and you get the significance test of the hypothesis
This makes the plot:
plot(model)
(of course, you might take into account autocorrelation of temperature,
weird counts, etc., but that should be done anyway)
HTH,
Martin
--
Please expect long response time (3d+) - this is because of family reasons
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