detach(tab)
tab1=read.table("clipboard",h=T)
tab1
attach(tab1)
treatment	sample chloro	abund	rich	WB	LC	SS
with	2	48	19	2	19	0	0
with	3	45.5	81	2	80	1	0
with	7	46.8	32	3	31	0	1
with	8	45.6	74	1	74	0	0
with	11	40.6	26	4	23	0	3
with	12	46.7	112	2	112	0	0
with	13	43.4	96	2	95	1	0
with	14	39	155	1	155	0	0
with	15	38.5	30	3	29	0	1
with	16	38.3	317	4	314	2	1
with	17	44.6	156	4	154	1	1
with	19	42.9	51	1	51	0	0
with	20	40.7	111	4	110	0	1
with	21	37.8	28	1	28	0	0
with	22	48.8	43	1	43	0	0
with	24	52.2	17	1	17	0	0
with	25	45.9	109	3	107	1	1
with	26	45.4	180	2	180	0	0
with	27	43.8	9	2	7	0	2
with	28	48.3	8	4	5	2	1
with	29	44.4	50	1	50	0	0
with	30	43.6	50	7	47	2	1
with	40	40.9	507	4	505	1	1
with	41	52.5	125	5	119	1	5
with	43	52.9	22	2	21	1	0
with	44	48.2	381	3	380	1	0
with	45	46.9	168	2	168	0	0
wo	1	40.2	23	1	23	0	0
wo	2	40.3	24	1	24	0	0
wo	3	40.7	1	1	1	0	0
wo	4	43.2	64	1	64	0	0
wo	5	42.8	1	1	1	0	0
wo	7	40.5	7	2	4	0	3
wo	9	38.7	1	1	1	0	0
wo	11	43.1	8	3	7	0	1
wo	12	43.9	18	1	18	0	0
wo	13	42.7	14	1	14	0	0
wo	15	42.3	19	1	19	0	0
wo	17	42.6	3	1	3	0	0
wo	18	45.2	16	1	16	0	0
wo	19	38.1	22	1	22	0	0
wo	21	38.9	1	1	1	0	0
wo	23	39.7	1	1	1	0	0
wo	24	39.5	93	2	93	0	0
wo	25	39.4	1	1	1	0	0
wo	28	37.2	1	1	1	0	0
wo	29	39.9	0	0	0	0	0
wo	30	39	2	1	2	0	0
wo	46	46.5	53	1	53	0	0
wo	47	37.6	31	1	31	0	0
wo	49	43.3	5	1	5	0	0
wo	50	44.8	3	1	3	0	0
wo	51	38.1	10	2	10	0	0
#legend: abund=insect abundance; rich = insect richness; WB=wood-boring insects;LC = leaf-chewing;
SS = sap-sucking insects
 
attach(tab)
plot(log(abund)~chloro,pch=16,cex=1.2,las=1)
tapply(chloro,treatment,mean)
shapiro.test(chloro)
shapiro.test(rich)
wilcox.test(rich~treatment)
Wilcoxon rank sum test with continuity correction

data:  rich by treatment
W = 578.5, p-value = 1.07e-05
alternative hypothesis: true location shift is not equal to 0

wilcox.test(abund~treatment)
 Wilcoxon rank sum test with continuity correction

data:  abund by treatment
W = 625.5, p-value = 1.055e-06

wilcox.test(chloro~treatment)
        Wilcoxon rank sum test with continuity correction

data:  chloro by treatment
W = 551, p-value = 0.0003856
alternative hypothesis: true location shift is not equal to 0



**********************************************

 m1=glm(log(abund+1)~chloro,quasipoisson)
 anova(m1,test="F")
 Analysis of Variance Table
 
 Response: log(abund + 1)
 Df  Sum Sq Mean Sq F value  Pr(>F)  
 chloro     1  11.472 11.4723  5.4424 0.02327 *
   Residuals 56 118.044  2.1079                  
 ---
   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 
 summary(m1)
 
 
 glm(formula = log(abund + 1) ~ chloro, family = quasipoisson())
 
 Deviance Residuals: 
   Min       1Q   Median       3Q      Max  
 -1.6636  -0.7773   0.1709   0.5287   1.6888  
 
 Coefficients:
   Estimate Std. Error t value Pr(>|t|)  
 (Intercept) -0.44968    0.71186  -0.632   0.5304  
 chloro       0.03709    0.01625   2.283   0.0266 *
   ---
   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 
 (Dispersion parameter for quasipoisson family taken to be 0.7485725)
 
 Null deviance: 45.270  on 52  degrees of freedom
 Residual deviance: 41.471  on 51  degrees of freedom
 AIC: NA
 
  curve(exp(-0.45+(0.037*x)),add=T,lwd=2)

*********************************************************


#MDS analysys ###
# Anosim ###
detach(tab)
tab1=read.table("clipboard",h=T)
tab1
attach(tab1)
treatment	sample	chloro	abund	rich	wb	lc	ss	wb1	wb2	wb3	wb4	wb5	wb6	wb7	wb8	wb9	wb10	lc1	lc2	lc3	lc4	lc5	lc6	lc7	ss1	ss2	ss3	ss4	ss5	ss6	ss7
with	2	48	19	2	19	0	0	0	0	0	0	0	0	1	0	0	18	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	3	45.5	81	2	80	1	0	0	0	0	0	0	0	0	0	0	80	0	1	0	0	0	0	0	0	0	0	0	0	0	0
with	7	46.8	32	3	31	0	1	0	1	0	0	0	0	0	0	0	30	0	0	0	0	0	0	0	0	1	0	0	0	0	0
with	8	45.6	74	1	74	0	0	0	0	0	0	0	0	0	0	0	74	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	11	40.6	26	4	23	0	3	0	1	0	0	0	0	0	0	0	22	0	0	0	0	0	0	0	0	2	0	1	0	0	0
with	12	46.7	112	2	112	0	0	0	0	0	0	0	0	1	0	0	111	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	13	43.4	96	2	95	1	0	0	0	0	0	0	0	0	0	0	95	0	0	0	1	0	0	0	0	0	0	0	0	0	0
with	14	39	155	1	155	0	0	0	0	0	0	0	0	0	0	0	155	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	15	38.5	30	3	29	0	1	0	0	0	0	0	0	1	0	0	28	0	0	0	0	0	0	0	0	1	0	0	0	0	0
with	16	38.3	317	4	314	2	1	0	0	0	0	0	0	0	0	0	314	1	0	0	0	1	0	0	0	0	0	0	0	1	0
with	17	44.6	156	4	154	1	1	0	1	0	0	0	0	0	0	0	153	0	1	0	0	0	0	0	0	0	1	0	0	0	0
with	19	42.9	51	1	51	0	0	0	0	0	0	0	0	0	0	0	51	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	20	40.7	111	4	110	0	1	0	1	0	0	1	0	0	0	0	108	0	0	0	0	0	0	0	0	0	1	0	0	0	0
with	21	37.8	28	1	28	0	0	0	0	0	0	0	0	0	0	0	28	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	22	48.8	43	1	43	0	0	0	0	0	0	0	0	0	0	0	43	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	24	52.2	17	1	17	0	0	0	0	0	0	0	0	0	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	25	45.9	109	3	107	1	1	0	0	0	0	0	0	0	0	0	107	0	0	0	1	0	0	0	0	1	0	0	0	0	0
with	26	45.4	180	2	180	0	0	0	1	0	0	0	0	0	0	0	179	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	27	43.8	9	2	7	0	2	0	0	0	0	0	0	0	0	0	7	0	0	0	0	0	0	0	0	2	0	0	0	0	0
with	28	48.3	8	4	5	2	1	0	0	0	0	0	0	0	0	0	5	1	0	0	0	0	0	1	0	1	0	0	0	0	0
with	29	44.4	50	1	50	0	0	0	0	0	0	0	0	0	0	0	50	0	0	0	0	0	0	0	0	0	0	0	0	0	0
with	30	43.6	50	7	47	2	1	0	0	0	1	0	1	1	0	0	44	1	0	0	0	1	0	0	0	1	0	0	0	0	0
with	40	40.9	507	4	505	1	1	0	0	0	0	0	0	0	0	1	504	0	1	0	0	0	0	0	0	0	0	0	0	0	1
with	41	52.5	125	5	119	1	5	0	0	1	0	0	0	0	0	0	118	0	0	0	0	0	1	0	0	0	0	0	1	0	4
with	43	52.9	22	2	21	1	0	0	0	0	0	0	0	0	0	0	21	0	0	1	0	0	0	0	0	0	0	0	0	0	0
with	44	48.2	381	3	380	1	0	0	1	0	0	0	0	0	0	0	379	0	1	0	0	0	0	0	0	0	0	0	0	0	0
with	45	46.9	168	2	168	0	0	0	2	0	0	0	0	0	0	0	166	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	1	40.2	23	1	23	0	0	0	0	0	0	0	0	0	0	0	23	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	2	40.3	24	1	24	0	0	0	0	0	0	0	0	0	0	0	24	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	3	40.7	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	4	43.2	64	1	64	0	0	0	0	0	0	0	0	0	0	0	64	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	5	42.8	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	7	40.5	7	2	4	0	3	0	0	0	0	0	0	0	0	0	4	0	0	0	0	0	0	0	3	0	0	0	0	0	0
wo	9	38.7	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	11	43.1	8	3	7	0	1	0	0	0	0	0	0	1	0	0	6	0	0	0	0	0	0	0	0	1	0	0	0	0	0
wo	12	43.9	18	1	18	0	0	0	0	0	0	0	0	0	0	0	18	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	13	42.7	14	1	14	0	0	0	0	0	0	0	0	0	0	0	14	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	15	42.3	19	1	19	0	0	0	0	0	0	0	0	0	0	0	19	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	17	42.6	3	1	3	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	18	45.2	16	1	16	0	0	0	0	0	0	0	0	0	0	0	16	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	19	38.1	22	1	22	0	0	0	0	0	0	0	0	0	0	0	22	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	21	38.9	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	23	39.7	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	24	39.5	93	2	93	0	0	1	0	0	0	0	0	0	0	0	92	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	25	39.4	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	28	37.2	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	29	39.9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	30	39	2	1	2	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	46	46.5	53	1	53	0	0	0	0	0	0	0	0	0	0	0	53	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	47	37.6	31	1	31	0	0	0	0	0	0	0	0	0	0	0	31	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	49	43.3	5	1	5	0	0	0	0	0	0	0	0	0	0	0	5	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	50	44.8	3	1	3	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0
wo	51	38.1	10	2	10	0	0	0	0	0	0	0	0	0	3	0	7	0	0	0	0	0	0	0	0	0	0	0	0	0	0

attach(tab1)
library(vegan)
insetos = tab1[ ,9:32]
anosim(insetos,grouping=tab1$treatment,distance="bray")
Call:
anosim(x = insetos, grouping = tab1$treatment, distance = "bray") 
Dissimilarity: bray 

ANOSIM statistic R: 0.3361 
      Significance: 0.001 

Permutation: free
Number of permutations: 999

#Composition differ