R Group 7

library(vegan)

bird_Ohio

ncol(bird_Ohio)

[1] 48

sp.rich<-specnumber(bird_Ohio, MARGIN=1)
as.data.frame(sp.rich)

sp.even<-specnumber(bird_Ohio,MARGIN=2)
as.data.frame(sp.even)

shannon<-diversity(bird_Ohio, index = "shannon")
shannon

  [1] 1.812353 2.443171 2.502177 2.449965
  [5] 2.505759 2.508055 2.808416 2.632368
  [9] 2.573988 2.450999 2.394499 2.670755
 [13] 2.531704 2.538192 2.502138 2.671630
 [17] 2.157016 2.003041 2.458384 2.517153
 [21] 2.887236 2.238884 2.518361 2.459535
 [25] 2.299249 2.324144 2.388357 2.443737
 [29] 2.508801 2.239565 2.719239 2.669216
 [33] 2.564513 2.220202 2.371608 2.728757
 [37] 2.523222 2.698767 1.974502 2.403435
 [41] 3.027143 2.929427 2.232029 2.326931
 [45] 2.594871 2.428581 2.325057 1.853327
 [49] 2.769102 2.504465 2.236733 2.948820
 [53] 2.471493 2.608128 3.043401 2.600579
 [57] 2.280595 2.117042 2.003623 2.829905
 [61] 2.496714 2.873540 2.406037 2.131073
 [65] 2.430766 2.713367 2.503175 2.517794
 [69] 2.595210 2.233722 2.332257 2.371660
 [73] 2.292052 2.391518 2.657784 2.475737
 [77] 2.734494 2.356708 2.438921 2.292174
 [81] 2.230160 2.444628 1.498985 2.542779
 [85] 2.550645 2.736674 2.453161 2.339160
 [89] 2.291078 2.258330 2.477123 2.602002
 [93] 2.135238 2.001804 2.482235 2.668960
 [97] 2.858335 2.649933 2.453522 2.865006
[101] 2.783952 2.998991 2.771560 2.295914
[105] 2.824489 2.383109 2.503965 2.634558
[109] 2.629183 2.519069 1.944464 2.445383
[113] 2.350606 2.406909 2.321891 2.375944
[117] 2.767400 2.843069 2.707323 2.148480
[121] 2.363373 2.489638 2.574951 2.517391
[125] 2.515402 2.707214 2.297690 2.547743
[129] 2.178186 2.640613 2.353198 2.386311
[133] 2.588825 2.240908 2.631116 3.003723
[137] 2.852913 2.490412 2.839435 2.716773
[141] 2.980252 2.197722 1.798709 2.885919
[145] 3.032903 2.485085 2.314192 2.241601
[149] 1.916081 2.567405 3.158537 2.891359
[153] 2.280094 2.767268 3.111523 2.444470
[157] 2.548230 2.518348 2.349092 2.625978
[161] 2.451639 2.764880 2.267142 3.161186
[165] 2.631644 2.692776 2.861356 2.906638
[169] 2.784373 2.308865 2.092852 2.622334
[173] 2.513477 2.282174 2.446827 2.388816
[177] 2.275937 2.354834 2.518677 2.193424
[181] 2.352495 2.779190 2.201542 2.549830
[185] 2.561493 2.912577 2.307692 2.637404
[189] 2.831398 2.850695 2.344426 2.665442
[193] 2.467880 2.850518 2.260180 2.756500
[197] 2.578963 2.309209 2.595555 2.466597
[201] 2.418257 2.351541 2.665416 1.990114
[205] 2.435551 2.375377 2.154783 2.893215
[209] 2.821822 2.495723

simpson<-diversity(bird_Ohio, index = "simpson")
simpson

  [1] 0.7805326 0.8960000 0.9075255 0.8855556
  [5] 0.9026709 0.9011446 0.9240237 0.9110302
  [9] 0.9070295 0.8999270 0.8799049 0.9196694
 [13] 0.8888889 0.8962500 0.9007561 0.9142661
 [17] 0.8620038 0.8197531 0.8960302 0.9045369
 [21] 0.9357639 0.8616864 0.8697979 0.8830796
 [25] 0.8786848 0.8776042 0.8829630 0.8850442
 [29] 0.8976082 0.8719723 0.9245578 0.9113564
 [33] 0.9050365 0.8360000 0.8854685 0.9157440
 [37] 0.8912000 0.9079717 0.8266667 0.8847737
 [41] 0.9387269 0.9382716 0.8691650 0.8791308
 [45] 0.9076543 0.8950000 0.8775000 0.7809917
 [49] 0.9238683 0.8980229 0.8823143 0.9409722
 [53] 0.8960459 0.9151874 0.9464575 0.9135355
 [57] 0.8824142 0.8347107 0.8395062 0.9347352
 [61] 0.9092971 0.9273192 0.8870523 0.8546384
 [65] 0.8946281 0.9188345 0.8836806 0.8945578
 [69] 0.9047852 0.8755556 0.8977778 0.8900227
 [73] 0.8383743 0.8843537 0.9178994 0.8921324
 [77] 0.9271163 0.8833792 0.8899955 0.8757396
 [81] 0.8698061 0.8863772 0.7321429 0.9112500
 [85] 0.9088757 0.9159111 0.8846154 0.8792000
 [89] 0.8742791 0.8650765 0.9047619 0.9126276
 [93] 0.8577610 0.8448118 0.9032922 0.9145881
 [97] 0.9304734 0.9073433 0.8827977 0.9330652
[101] 0.9225839 0.9427660 0.9207786 0.8814879
[105] 0.9275148 0.8897929 0.8994646 0.9161111
[109] 0.9120708 0.9000000 0.7976000 0.8922902
[113] 0.8741319 0.8870392 0.8729339 0.8923182
[117] 0.9202477 0.9193787 0.9149520 0.8515625
[121] 0.8741497 0.8921324 0.9022485 0.9037901
[125] 0.8891293 0.9149338 0.8655500 0.8978052
[129] 0.8395062 0.9145408 0.8800000 0.8966837
[133] 0.9054134 0.8526786 0.9104132 0.9400889
[137] 0.9307670 0.8930664 0.9362500 0.9239452
[141] 0.9383673 0.8441358 0.7929240 0.9388889
[145] 0.9432398 0.8966667 0.8854644 0.8702422
[149] 0.8138013 0.9071220 0.9499541 0.9307195
[153] 0.8691716 0.9120499 0.9468599 0.8955442
[157] 0.8949804 0.9037901 0.8696377 0.9083176
[161] 0.8940972 0.9189189 0.8529779 0.9519312
[165] 0.9084298 0.9137329 0.9341564 0.9375000
[169] 0.9126658 0.8780992 0.8480726 0.9066607
[173] 0.8948148 0.8680556 0.8999082 0.8753463
[177] 0.8734995 0.8911565 0.8966667 0.8577610
[181] 0.8888889 0.9228395 0.8440083 0.9104540
[185] 0.9053498 0.9396386 0.8804283 0.9167658
[189] 0.9286332 0.9297778 0.8928200 0.9150327
[193] 0.8883929 0.9329660 0.8786848 0.9156283
[197] 0.9032922 0.8792867 0.9061250 0.8888889
[201] 0.8756378 0.8664554 0.9191176 0.8337950
[205] 0.8915289 0.8835063 0.8700000 0.9325017
[209] 0.9287965 0.8692904

inv.simpson<-diversity(bird_Ohio, index = "invsimpson")
inv.simpson

  [1]  4.556485  9.615385 10.813793  8.737864
  [5] 10.274419 10.115789 13.161994 11.239766
  [9] 10.756098  9.992701  8.326733 12.448560
 [13]  9.000000  9.638554 10.076190 11.664000
 [17]  7.246575  5.547945  9.618182 10.475248
 [21] 15.567568  7.229947  7.680365  8.552826
 [25]  8.242991  8.170213  8.544304  8.698997
 [29]  9.766404  7.810811 13.255172 11.281124
 [33] 10.530364  6.097561  8.731225 11.868597
 [37]  9.191176 10.866221  5.769231  8.678571
 [41] 16.320388 16.200000  7.643216  8.273408
 [45] 10.828877  9.523810  8.163265  4.566038
 [49] 13.135135  9.806122  8.497207 16.941176
 [53]  9.619632 11.790698 18.676768 11.565445
 [57]  8.504425  6.050000  6.230769 15.322188
 [61] 11.025000 13.758794  8.853659  6.879397
 [65]  9.490196 12.320513  8.597015  9.483871
 [69] 10.502564  8.035714  9.782609  9.092784
 [73]  6.187135  8.647059 12.180180  9.270627
 [77] 13.720497  8.574803  9.090535  8.047619
 [81]  7.680851  8.801047  3.733333 11.267606
 [85] 10.974026 11.892178  8.666667  8.278146
 [89]  7.954128  7.411609 10.500000 11.445255
 [93]  7.030418  6.443787 10.340426 11.707965
 [97] 14.382979 10.792531  8.532258 14.939914
[101] 12.917211 17.472131 12.622857  8.437956
[105] 13.795918  9.073826  9.946746 11.920530
[109] 11.372781 10.000000  4.940711  9.284211
[113]  7.944828  8.852632  7.869919  9.286624
[117] 12.538824 12.403670 11.758065  6.736842
[121]  7.945946  9.270627 10.230024 10.393939
[125]  9.019512 11.755556  7.437710  9.785235
[129]  6.230769 11.701493  8.333333  9.679012
[133] 10.572327  6.787879 11.162362 16.691395
[137] 14.443983  9.351598 15.686275 13.148410
[141] 16.225166  6.415842  4.829146 16.363636
[145] 17.617978  9.677419  8.730909  7.706667
[149]  5.370607 10.766816 19.981651 14.434084
[153]  7.643599 11.370079 18.818182  9.573427
[157]  9.522034 10.393939  7.670927 10.907216
[161]  9.442623 12.333333  6.801700 20.803509
[165] 10.920578 11.591900 15.187500 16.000000
[169] 11.450262  8.203390  6.582090 10.713604
[173]  9.507042  7.578947  9.990826  8.022222
[177]  7.905109  9.187500  9.677419  7.030418
[181]  9.000000 12.960000  6.410596 11.167442
[185] 10.565217 16.566879  8.363184 12.014286
[189] 14.012121 14.240506  9.330097 11.769231
[193]  8.960000 14.917808  8.242991 11.852321
[197] 10.340426  8.284091 10.652459  9.000000
[201]  8.041026  7.488136 12.363636  6.016667
[205]  9.219048  8.584158  7.692308 14.815182
[209] 14.044248  7.650549

fish.alp<-fisher.alpha(bird_Ohio)
fish.alp

  [1]  4.879601  7.265437  5.991450  7.139089
  [5]  7.612305  6.987499 11.700302  8.514440
  [9]  7.645338  8.203268  8.096417  9.316717
 [13] 10.625244 11.170180  8.704701 10.438229
 [17]  5.277516  5.354320  7.741632  8.704701
 [21] 14.235471  5.563489 10.802080  8.827385
 [25]  6.444680  6.644209  7.878918  7.161667
 [29]  7.814550  6.610003  8.514440 11.689884
 [33]  8.929434  8.136595  6.745910  9.869081
 [37]  9.074933 12.010405  5.252615  6.879711
 [41] 15.000086 13.840139  5.922455  6.745910
 [45]  9.946552  7.656867  6.691741  5.685388
 [49] 11.492806  6.987499  5.098572 15.718214
 [53]  7.483241 10.136431 22.398229  8.549933
 [57]  7.182494  6.227126  4.586486  9.261872
 [61]  7.353659 12.333742  9.181666  5.292913
 [65]  7.088144  9.348894  9.392030 10.625244
 [69]  8.326654  6.264404  6.264404  6.444680
 [73]  8.704701  8.347135  8.791663  8.660992
 [77] 10.665986  7.912277  7.612305  6.828399
 [81]  6.040326  9.650566  2.342557  8.717218
 [85]  6.781181 10.483605  7.896600  6.456369
 [89]  6.369414  6.207709  7.353659  8.306262
 [93]  4.776899  4.076606  6.879711  9.238619
 [97] 14.386674  9.915005  9.749784 12.719495
[101]  9.512046 12.467435 14.572309  4.771091
[105] 13.095785  6.286631  8.525511  7.904514
[109]  8.514440  8.136595  5.007071  8.347135
[113]  7.490234  6.559906  7.088144  6.132369
[117] 10.691663 15.780397 11.492806  6.973786
[121]  8.347135  8.660992  9.031307  8.266633
[125] 11.641119 13.465722  6.745910  8.536862
[129]  6.127733  9.185480  6.456369  5.316444
[133] 10.886950  6.712734 10.277584 14.105449
[137] 12.719495  7.565648 14.166726  8.614305
[141] 13.911898  8.416321  4.257215 10.505202
[145] 17.331388  7.139089  5.780910  6.610003
[149]  4.928004  9.228867 18.363340 12.375997
[153]  6.745910 11.212185 16.327612  8.203268
[157]  8.660992  7.374796  8.266633 10.885075
[161]  7.490234 17.764531  7.374796 15.828827
[165] 10.277584 10.366882 12.624488 11.851265
[169] 11.519965  7.088144  4.850244  8.071292
[173]  9.946552  6.644209  9.181666  8.198934
[177]  5.925432  6.444680  7.904514  5.521350
[181]  6.444680 18.528711  6.227126  8.266633
[185]  8.536862 13.353647  6.564138  8.096417
[189] 11.284101 11.323066  7.182494 10.976162
[193]  8.306262 21.505636  5.612435 14.105050
[197]  9.454690  6.132369  9.060475  8.198934
[201]  8.306262  7.612305  7.992375  3.725191
[205]  8.024966  7.161667  7.959047 12.375997
[209] 11.156386  9.710044

Div.Ind<-cbind.data.frame(shannon, simpson, inv.simpson,fish.alp)
Div.Ind

summary(Div.Ind)

    shannon         simpson      
 Min.   :1.499   Min.   :0.7321  
 1st Qu.:2.334   1st Qu.:0.8788  
 Median :2.499   Median :0.8961  
 Mean   :2.502   Mean   :0.8936  
 3rd Qu.:2.669   3rd Qu.:0.9148  
 Max.   :3.161   Max.   :0.9519  
  inv.simpson        fish.alp     
 Min.   : 3.733   Min.   : 2.343  
 1st Qu.: 8.251   1st Qu.: 6.746  
 Median : 9.629   Median : 8.267  
 Mean   :10.239   Mean   : 8.912  
 3rd Qu.:11.744   3rd Qu.:10.420  
 Max.   :20.804   Max.   :22.398  

env_Ohio

Ohio.env.Div<-cbind.data.frame(env_Ohio, Div.Ind)
Ohio.env.Div

library("rstatix")

Ohio.env.Div %>%
  group_by(ELT) %>%
  get_summary_stats(shannon, type = "mean_sd")

Ohio.env.Div %>%
  group_by(ELT) %>%
  get_summary_stats(simpson, type = "mean_sd")

Ohio.env.Div %>%
  group_by(ELT) %>%
  get_summary_stats(inv.simpson, type = "mean_sd")

Ohio.env.Div %>%
  group_by(ELT) %>%
  get_summary_stats(fish.alp, type = "mean_sd")

Shanon.ELT<-ggplot(Ohio.env.Div, aes(x = ELT, y = shannon, fill = ELT)) +
  geom_boxplot() + 
  stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")

Simp.ELT<-ggplot(Ohio.env.Div, aes(x = ELT, y = simpson, fill = ELT)) +
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")

InvSimp.ELT<-ggplot(Ohio.env.Div, aes(x = ELT, y = inv.simpson, fill = ELT)) +
  geom_boxplot()  + 
  stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")

Fish.ELT<-ggplot(Ohio.env.Div, aes(x = ELT, y = fish.alp, fill = ELT)) +
  geom_boxplot()  + 
  stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")

grid.arrange(Shanon.ELT, Simp.ELT, InvSimp.ELT, Fish.ELT,
             nrow=2, ncol=2)

grid.arrange(Shanon.ELT, Simp.ELT, InvSimp.ELT, Fish.ELT, nrow=2, ncol=2)

shannon_aov <- aov(Ohio.env.Div$shannon ~ Ohio.env.Div$ELT)

summary(shannon_aov)

                  Df Sum Sq Mean Sq F value
Ohio.env.Div$ELT   2  0.399 0.19958   2.817
Residuals        207 14.668 0.07086        
                 Pr(>F)  
Ohio.env.Div$ELT 0.0621 .
Residuals                
---
Signif. codes:  
  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
  ‘ ’ 1

simpson_aov <- aov(Ohio.env.Div$simpson ~ Ohio.env.Div$ELT)

summary(simpson_aov)

                  Df  Sum Sq  Mean Sq
Ohio.env.Div$ELT   2 0.00583 0.002916
Residuals        207 0.21744 0.001050
                 F value Pr(>F)  
Ohio.env.Div$ELT   2.776 0.0646 .
Residuals                        
---
Signif. codes:  
  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
  ‘ ’ 1

inv.simpson_aov <- aov(Ohio.env.Div$inv.simpson ~ Ohio.env.Div$ELT)

summary(inv.simpson_aov)

                  Df Sum Sq Mean Sq F value
Ohio.env.Div$ELT   2   44.5  22.262   2.438
Residuals        207 1890.5   9.133        
                 Pr(>F)  
Ohio.env.Div$ELT 0.0899 .
Residuals                
---
Signif. codes:  
  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
  ‘ ’ 1

fish.alp_aov <- aov(Ohio.env.Div$fish.alp ~ Ohio.env.Div$ELT)

summary(fish.alp_aov)

                  Df Sum Sq Mean Sq F value
Ohio.env.Div$ELT   2   11.3   5.639   0.562
Residuals        207 2077.8  10.037        
                 Pr(>F)
Ohio.env.Div$ELT  0.571
Residuals              

distance_matrix<-vegdist(Ohio.env.Div[,9:9], method="bray", binary=FALSE)

adonis2(distance_matrix ~ ELT, data=Ohio.env.Div)

Permutation test for adonis under reduced model
Permutation: free
Number of permutations: 999

adonis2(formula = distance_matrix ~ ELT, data = Ohio.env.Div)
          Df SumOfSqs      R2      F Pr(>F)
Model      2   0.0387 0.00746 0.7778  0.487
Residual 207   5.1467 0.99254              
Total    209   5.1854 1.00000              

shannon_Tukey<-TukeyHSD(shannon_aov, conf.level=.95, ordered = TRUE)
shannon_Tukey

  Tukey multiple comparisons of means
    95% family-wise confidence level
    factor levels have been ordered

Fit: aov(formula = Ohio.env.Div$shannon ~ Ohio.env.Div$ELT)

$`Ohio.env.Div$ELT`
            diff           lwr       upr
dm-do 0.04362627 -0.0603278576 0.1475804
wm-do 0.10671316  0.0004435631 0.2129827
wm-dm 0.06308689 -0.0546961030 0.1808699
          p adj
dm-do 0.5835556
wm-do 0.0487920
wm-dm 0.4169311

simpson_Tukey<-TukeyHSD(simpson_aov, conf.level=.95, ordered = TRUE)
simpson_Tukey

  Tukey multiple comparisons of means
    95% family-wise confidence level
    factor levels have been ordered

Fit: aov(formula = Ohio.env.Div$simpson ~ Ohio.env.Div$ELT)

$`Ohio.env.Div$ELT`
             diff           lwr        upr
dm-do 0.003429337 -9.227491e-03 0.01608616
wm-do 0.012841548 -9.719656e-05 0.02578029
wm-dm 0.009412211 -4.928335e-03 0.02375276
          p adj
dm-do 0.7984209
wm-do 0.0522377
wm-dm 0.2701554

inv.simpson_Tukey<-TukeyHSD(inv.simpson_aov, conf.level=.95, ordered = TRUE)
inv.simpson_Tukey

  Tukey multiple comparisons of means
    95% family-wise confidence level
    factor levels have been ordered

Fit: aov(formula = Ohio.env.Div$inv.simpson ~ Ohio.env.Div$ELT)

$`Ohio.env.Div$ELT`
           diff         lwr      upr
dm-do 0.3772352 -0.80294964 1.557420
wm-do 1.1278123 -0.07865997 2.334284
wm-dm 0.7505770 -0.58660612 2.087760
          p adj
dm-do 0.7312016
wm-do 0.0723818
wm-dm 0.3828643

fish.alp_Tukey<-TukeyHSD(fish.alp_aov, conf.level=.95, ordered = TRUE)
fish.alp_Tukey

  Tukey multiple comparisons of means
    95% family-wise confidence level
    factor levels have been ordered

Fit: aov(formula = Ohio.env.Div$fish.alp ~ Ohio.env.Div$ELT)

$`Ohio.env.Div$ELT`
            diff        lwr      upr
dm-do 0.07742881 -1.1598183 1.314676
wm-do 0.55140864 -0.7133968 1.816214
wm-dm 0.47397984 -0.9278564 1.875816
          p adj
dm-do 0.9880401
wm-do 0.5593155
wm-dm 0.7045578

shannon_Tukey_plot <- as.data.frame(shannon_Tukey$`Ohio.env.Div$ELT`)

shannon_Tukey_plot$comparison <- rownames(shannon_Tukey_plot)

shannon_Tukey_plot

simpson_Tukey_plot <- as.data.frame(simpson_Tukey$`Ohio.env.Div$ELT`)

simpson_Tukey_plot$comparison <- rownames(simpson_Tukey_plot)

simpson_Tukey_plot

inv.simpson_Tukey_plot <- as.data.frame(inv.simpson_Tukey$`Ohio.env.Div$ELT`)

inv.simpson_Tukey_plot$comparison <- rownames(inv.simpson_Tukey_plot)

inv.simpson_Tukey_plot

fish.alp_Tukey_plot <- as.data.frame(fish.alp_Tukey$`Ohio.env.Div$ELT`)

fish.alp_Tukey_plot$comparison <- rownames(fish.alp_Tukey_plot)

fish.alp_Tukey_plot

shannon_Tukey_plot_result<-ggplot(shannon_Tukey_plot, aes(x = comparison, y = diff)) +
  geom_point() +
  geom_errorbar(aes(ymin = lwr, ymax = upr), width = 0.2) +
  labs(
    title = "Tukey HSD Test Results for Shannon Diversity",
    x = "ELT Comparison",
    y = "Pairwise Difference in Mean"
  )

shannon_Tukey_plot_result

shannon_Tukey_plot_result_meandiff<-shannon_Tukey_plot_result + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  geom_hline(yintercept=0, linetype="dashed", color = "red")

shannon_Tukey_plot_result_meandiff

simpson_Tukey_plot_result<-ggplot(simpson_Tukey_plot, aes(x = comparison, y = diff)) +
  geom_point() +
  geom_errorbar(aes(ymin = lwr, ymax = upr), width = 0.2) +
  labs(
    title = "Tukey HSD Test Results for Simpson Diversity",
    x = "ELT Comparison",
    y = "Pairwise Difference in Mean"
  )

simpson_Tukey_plot_result

simpson_Tukey_plot_result_meandiff<-simpson_Tukey_plot_result + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  geom_hline(yintercept=0, linetype="dashed", color = "red")

simpson_Tukey_plot_result_meandiff

inv.simpson_Tukey_plot_result<-ggplot(inv.simpson_Tukey_plot, aes(x = comparison, y = diff)) +
  geom_point() +
  geom_errorbar(aes(ymin = lwr, ymax = upr), width = 0.2) +
  labs(
    title = "Tukey HSD Test Results for inv.Simpson Diversity",
    x = "ELT Comparison",
    y = "Pairwise Difference in Mean"
  )

inv.simpson_Tukey_plot_result

inv.simpson_Tukey_plot_result_meandiff<-inv.simpson_Tukey_plot_result + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  geom_hline(yintercept=0, linetype="dashed", color = "red")

inv.simpson_Tukey_plot_result_meandiff

fish.alp_Tukey_plot_result<-ggplot(fish.alp_Tukey_plot, aes(x = comparison, y = diff)) +
  geom_point() +
  geom_errorbar(aes(ymin = lwr, ymax = upr), width = 0.2) +
  labs(
    title = "Tukey HSD Test Results for fish alpha Diversity",
    x = "ELT Comparison",
    y = "Pairwise Difference in Mean"
  )

fish.alp_Tukey_plot_result

fish.alp_Tukey_plot_result_meandiff<-fish.alp_Tukey_plot_result + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  geom_hline(yintercept=0, linetype="dashed", color = "red")

fish.alp_Tukey_plot_result_meandiff

grid.arrange(shannon_Tukey_plot_result_meandiff,Shanon.ELT,nrow=1, ncol=2)

grid.arrange(simpson_Tukey_plot_result_meandiff,Simp.ELT,nrow=1, ncol=2)

grid.arrange(inv.simpson_Tukey_plot_result_meandiff,InvSimp.ELT,nrow=1, ncol=2)

grid.arrange(fish.alp_Tukey_plot_result_meandiff,Fish.ELT,nrow=1, ncol=2)