library(readxl)
## Warning: package 'readxl' was built under R version 4.4.2
bird_Ohio <- read_excel("C:/Users/LENOVO/Downloads/bird_Ohio.xlsx")
## New names:
## • `` -> `...1`
env_Ohio <- read_excel("env_Ohio.xlsx")
## New names:
## • `` -> `...1`
library(vegan)
## Warning: package 'vegan' was built under R version 4.4.2
## Loading required package: permute
## Warning: package 'permute' was built under R version 4.4.2
## Loading required package: lattice
## This is vegan 2.6-8
bird_Ohio
## # A tibble: 210 × 49
## ...1 ACFL AMCR AMGO AMRE AMRO BAOR BAWW BGGN BHCO BLBW BLJA BRTH
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 0 1 0 0 0 1 0 0 0 1 0
## 2 2 9 0 0 0 3 0 2 2 1 0 1 0
## 3 3 4 0 0 0 3 0 0 4 4 0 0 0
## 4 4 7 1 0 1 0 0 3 3 0 0 0 0
## 5 5 2 0 0 0 2 0 1 0 1 0 0 0
## 6 6 8 0 0 5 1 0 4 0 3 0 0 0
## 7 7 3 3 2 2 0 0 0 3 1 0 0 1
## 8 8 11 0 0 4 0 0 2 2 0 0 0 0
## 9 9 10 0 0 1 1 0 3 1 3 0 0 0
## 10 10 1 0 0 0 0 0 2 1 0 0 1 1
## # ℹ 200 more rows
## # ℹ 36 more variables: BTNW <dbl>, BWWA <dbl>, CACH <dbl>, CARW <dbl>,
## # CEDW <dbl>, CERW <dbl>, CHSP <dbl>, COYE <dbl>, EAPH <dbl>, EATO <dbl>,
## # EAWP <dbl>, ETTI <dbl>, FISP <dbl>, GCFL <dbl>, GRCA <dbl>, HOWA <dbl>,
## # INBU <dbl>, KEWA <dbl>, LOWA <dbl>, NOCA <dbl>, NOPA <dbl>, OVEN <dbl>,
## # PIWA <dbl>, PRAW <dbl>, RBGR <dbl>, REVI <dbl>, RWBL <dbl>, SCTA <dbl>,
## # SUTA <dbl>, WBNU <dbl>, WEVI <dbl>, WEWA <dbl>, WOTH <dbl>, YBCH <dbl>, …
ncol(bird_Ohio)
## [1] 49
sp.rich<-specnumber(bird_Ohio, MARGIN=1)
as.data.frame(sp.rich)
## sp.rich
## 1 11
## 2 16
## 3 15
## 4 17
## 5 16
## 6 17
## 7 23
## 8 19
## 9 18
## 10 15
## 11 18
## 12 19
## 13 18
## 14 18
## 15 17
## 16 20
## 17 13
## 18 13
## 19 16
## 20 17
## 21 22
## 22 14
## 23 21
## 24 19
## 25 14
## 26 15
## 27 16
## 28 16
## 29 18
## 30 13
## 31 19
## 32 21
## 33 18
## 34 17
## 35 15
## 36 22
## 37 18
## 38 22
## 39 11
## 40 16
## 41 29
## 42 23
## 43 13
## 44 15
## 45 18
## 46 15
## 47 14
## 48 10
## 49 21
## 50 17
## 51 12
## 52 23
## 53 17
## 54 17
## 55 25
## 56 17
## 57 13
## 58 14
## 59 11
## 60 21
## 61 15
## 62 25
## 63 15
## 64 12
## 65 15
## 66 20
## 67 18
## 68 18
## 69 19
## 70 12
## 71 12
## 72 14
## 73 17
## 74 16
## 75 18
## 76 18
## 77 19
## 78 14
## 79 16
## 80 14
## 81 13
## 82 17
## 83 7
## 84 16
## 85 17
## 86 23
## 87 17
## 88 15
## 89 15
## 90 15
## 91 15
## 92 18
## 93 12
## 94 10
## 95 16
## 96 20
## 97 23
## 98 19
## 99 18
## 100 23
## 101 22
## 102 25
## 103 22
## 104 14
## 105 22
## 106 15
## 107 16
## 108 18
## 109 19
## 110 17
## 111 13
## 112 16
## 113 16
## 114 16
## 115 15
## 116 15
## 117 23
## 118 24
## 119 21
## 120 13
## 121 16
## 122 18
## 123 20
## 124 17
## 125 19
## 126 21
## 127 15
## 128 18
## 129 14
## 130 19
## 131 15
## 132 14
## 133 18
## 134 16
## 135 20
## 136 27
## 137 23
## 138 18
## 139 20
## 140 19
## 141 26
## 142 15
## 143 10
## 144 21
## 145 26
## 146 17
## 147 14
## 148 13
## 149 12
## 150 18
## 151 29
## 152 24
## 153 15
## 154 24
## 155 28
## 156 15
## 157 18
## 158 16
## 159 17
## 160 19
## 161 16
## 162 21
## 163 16
## 164 29
## 165 20
## 166 21
## 167 22
## 168 23
## 169 25
## 170 15
## 171 12
## 172 19
## 173 18
## 174 15
## 175 15
## 176 18
## 177 15
## 178 14
## 179 18
## 180 13
## 181 14
## 182 21
## 183 14
## 184 17
## 185 18
## 186 22
## 187 14
## 188 18
## 189 23
## 190 24
## 191 13
## 192 20
## 193 18
## 194 21
## 195 13
## 196 23
## 197 19
## 198 15
## 199 19
## 200 18
## 201 18
## 202 16
## 203 19
## 204 10
## 205 16
## 206 16
## 207 11
## 208 24
## 209 23
## 210 20
sp.even<-specnumber(bird_Ohio,MARGIN=2)
as.data.frame(sp.even)
## sp.even
## ...1 210
## ACFL 181
## AMCR 43
## AMGO 34
## AMRE 81
## AMRO 78
## BAOR 16
## BAWW 149
## BGGN 136
## BHCO 133
## BLBW 12
## BLJA 102
## BRTH 9
## BTNW 18
## BWWA 28
## CACH 54
## CARW 32
## CEDW 23
## CERW 61
## CHSP 5
## COYE 25
## EAPH 32
## EATO 141
## EAWP 127
## ETTI 147
## FISP 6
## GCFL 37
## GRCA 24
## HOWA 187
## INBU 52
## KEWA 62
## LOWA 35
## NOCA 70
## NOPA 12
## OVEN 196
## PIWA 10
## PRAW 26
## RBGR 24
## REVI 208
## RWBL 6
## SCTA 172
## SUTA 20
## WBNU 157
## WEVI 48
## WEWA 157
## WOTH 173
## YBCH 24
## YTVI 95
## YTWA 14
shannon<-diversity(bird_Ohio, index = "shannon")
shannon
## [1] 1.8917399 2.5122269 2.5759511 2.5306341 2.5813695 2.5851922 2.8543118
## [8] 2.6869090 2.6290101 2.4471082 2.4514742 2.6624436 2.4801533 2.4524231
## [15] 2.4446459 2.5985159 2.1580650 2.0290131 2.3439848 2.3677896 2.6230152
## [22] 2.1818266 2.3999167 2.3497389 2.1019228 2.1558323 2.1542864 2.2277483
## [29] 2.3289428 1.8809617 2.4493401 2.3266446 2.2270348 1.9964440 2.0417370
## [36] 2.4618897 2.1320689 2.2922720 1.5430953 2.0627115 2.6546098 2.3331170
## [43] 1.7535317 1.8944254 1.9905828 1.8202840 1.7588983 1.2049611 2.1437307
## [50] 2.0737966 1.6534827 2.1077805 1.9625262 1.7738141 2.0210011 1.8759969
## [57] 1.4522335 1.5969316 1.4228140 2.2233828 1.6941131 2.2527933 1.4705667
## [64] 1.4376671 1.6556983 2.0069460 1.7242396 1.6262868 1.9412633 1.2809809
## [71] 1.3010760 1.5318794 1.5531853 1.5204937 1.7648837 1.6943305 1.7000499
## [78] 1.3091997 1.5702975 1.3837769 1.3385135 1.4513903 0.9429183 1.4490505
## [85] 1.7895111 1.9656594 1.5788314 1.5022623 1.4904512 1.4962931 1.4059042
## [92] 1.6478061 1.2990656 1.0930459 1.5543977 1.7289643 1.6443609 1.5495614
## [99] 1.4031313 1.7226377 1.8883235 1.9303606 1.4901640 1.5787674 1.5705318
## [106] 1.4178638 1.2837894 1.5926701 1.6081552 1.4082954 1.2234179 1.2528753
## [113] 1.3100790 1.4507992 1.2323738 1.3797947 1.7293521 1.4854109 1.4658616
## [120] 0.9669648 1.1795715 1.3672704 1.5350481 1.3090028 1.2126036 1.3047201
## [127] 1.2040135 1.3639687 1.1349332 1.4067959 1.2394246 1.3198667 1.1560033
## [134] 1.2668167 1.3633187 1.7184293 1.4705114 1.4134997 1.1657677 1.4382794
## [141] 1.6241226 0.9479850 0.7890530 1.4545973 1.4366209 1.3270892 1.1408832
## [148] 0.9003295 0.9349969 1.1903317 1.5749987 1.5003813 1.0810697 1.5488876
## [155] 1.5759803 0.9573217 1.2080605 1.1433944 1.0993195 1.1174376 1.1019272
## [162] 0.9943400 1.0646499 1.6364990 1.2202461 1.3055935 1.2551904 1.3908337
## [169] 1.5319849 0.9827985 0.9091475 1.3284004 1.0280144 1.0155201 0.8256294
## [176] 1.1407526 1.1095773 0.9370976 1.1957853 0.9132408 0.9268789 0.9070407
## [183] 0.9184634 1.0505849 1.1130730 1.1475092 0.8861070 1.1679846 1.3269686
## [190] 1.4025824 0.7316835 1.0732068 1.0880649 0.8289934 0.8676827 1.1044350
## [197] 1.0755235 1.0144104 1.1081660 1.0762302 1.0511711 0.9282779 1.2321592
## [204] 0.7472004 0.8967415 0.9696155 0.4879819 1.2601278 1.2607309 1.0734488
simpson<-diversity(bird_Ohio, index = "simpson")
simpson
## [1] 0.7923875 0.9023669 0.9141051 0.8955078 0.9112426 0.9100346 0.9286265
## [8] 0.9171429 0.9131944 0.8927116 0.8897290 0.9137893 0.8793388 0.8758573
## [15] 0.8830959 0.8967347 0.8536155 0.8264046 0.8624852 0.8617998 0.8762865
## [22] 0.8433163 0.8526139 0.8573088 0.8130987 0.8250548 0.8136574 0.8264701
## [29] 0.8491358 0.7441406 0.8553590 0.8238062 0.8106576 0.7780612 0.7801904
## [36] 0.8531268 0.7831946 0.8068698 0.6477631 0.7808963 0.8616564 0.7890625
## [43] 0.6954194 0.7339693 0.7269136 0.6911844 0.6822566 0.5081633 0.7527571
## [50] 0.7694515 0.6567901 0.7160000 0.7361333 0.6479362 0.6747189 0.6863983
## [57] 0.5658574 0.6459054 0.5912465 0.7710506 0.6341785 0.7706531 0.5559896
## [64] 0.5789628 0.6272199 0.7150879 0.6403025 0.6024793 0.7088021 0.4988000
## [71] 0.4968140 0.5861804 0.5995339 0.5778835 0.6374853 0.6346974 0.6039282
## [78] 0.4959013 0.5915848 0.5347080 0.5234094 0.5429308 0.4238292 0.5318678
## [85] 0.6617778 0.6964237 0.5921019 0.5775047 0.5791837 0.5853587 0.5223585
## [92] 0.6010774 0.5181661 0.4416889 0.5807846 0.6220482 0.5677222 0.5565515
## [99] 0.5220452 0.5952296 0.6635526 0.6503184 0.5207111 0.6158734 0.5447686
## [106] 0.5379747 0.4695946 0.5760346 0.5821278 0.5175781 0.5051503 0.4630629
## [113] 0.4962000 0.5478637 0.4671492 0.5235294 0.6090305 0.5106574 0.5185606
## [120] 0.3701524 0.4405887 0.5040980 0.5602648 0.4785325 0.4391298 0.4572742
## [127] 0.4574581 0.4963772 0.4396221 0.5037577 0.4670187 0.4978497 0.4104902
## [134] 0.4898061 0.4876454 0.5769861 0.5051541 0.5225468 0.3938079 0.5078587
## [141] 0.5466634 0.3572150 0.3180077 0.4964437 0.4751863 0.4889245 0.4303415
## [148] 0.3341988 0.3763435 0.4262014 0.5111597 0.5117908 0.4075500 0.5420794
## [155] 0.5161432 0.3428280 0.4343764 0.4120049 0.4084227 0.3921670 0.4009981
## [162] 0.3344865 0.4009879 0.5320156 0.4317769 0.4590037 0.4250527 0.4708680
## [169] 0.5338560 0.3637872 0.3495779 0.4747466 0.3657520 0.3795146 0.2896172
## [176] 0.4219639 0.4203375 0.3414050 0.4325555 0.3431800 0.3372680 0.3009006
## [183] 0.3442333 0.3724143 0.3960015 0.3812779 0.3234457 0.4113292 0.4541780
## [190] 0.4803133 0.2576901 0.3719623 0.3935743 0.2681985 0.3192152 0.3765746
## [197] 0.3795178 0.3771101 0.3910828 0.3889234 0.3824131 0.3371236 0.4337904
## [204] 0.2852947 0.3188013 0.3529198 0.1674397 0.4239074 0.4304125 0.3842678
inv.simpson<-diversity(bird_Ohio, index = "invsimpson")
inv.simpson
## [1] 4.816667 10.242424 11.642140 9.570093 11.266667 11.115385 14.010811
## [8] 12.068966 11.520000 9.320675 9.068571 11.599483 8.287671 8.055249
## [15] 8.554023 9.683794 6.831325 5.760522 7.271945 7.235880 8.083192
## [22] 6.382284 6.784902 7.008138 5.350417 5.716075 5.366460 5.762696
## [29] 6.628478 3.908397 6.913669 5.675570 5.281437 4.505747 4.549391
## [36] 6.808596 4.612431 5.177854 2.838998 4.564050 7.228380 4.740741
## [43] 3.283203 3.758965 3.661844 3.238179 3.147193 2.033195 4.044605
## [50] 4.337483 2.913669 3.521127 3.789793 2.840394 3.074264 3.188759
## [57] 2.303391 2.824104 2.446462 4.367778 2.733574 4.360207 2.252199
## [64] 2.375087 2.682547 3.509854 2.780114 2.515593 3.434090 1.995211
## [71] 1.987337 2.416512 2.497090 2.369014 2.758509 2.737457 2.524795
## [78] 1.983739 2.448489 2.149188 2.098237 2.187852 1.735597 2.136149
## [85] 2.956636 3.294065 2.451592 2.366890 2.376334 2.411723 2.093621
## [92] 2.506752 2.075404 1.791116 2.385409 2.645840 2.313327 2.255053
## [99] 2.092248 2.470536 2.972233 2.859744 2.086424 2.603309 2.196685
## [106] 2.164384 1.885350 2.358683 2.393076 2.072874 2.020815 1.862416
## [113] 1.984915 2.211722 1.876698 2.098765 2.557744 2.043558 2.077105
## [120] 1.587686 1.787593 2.016527 2.274096 1.917665 1.782944 1.842551
## [127] 1.843175 1.985613 1.784510 2.015144 1.876238 1.991436 1.696325
## [134] 1.960039 1.951773 2.363989 2.020831 2.094446 1.649642 2.031937
## [141] 2.205866 1.555730 1.466292 1.985875 1.905438 1.956658 1.755438
## [148] 1.501950 1.603447 1.742772 2.045658 2.048302 1.687906 2.183785
## [155] 2.066727 1.521672 1.767960 1.700695 1.690396 1.645189 1.669444
## [162] 1.502599 1.669415 2.136824 1.759872 1.848441 1.739290 1.889888
## [169] 2.145260 1.571801 1.537463 1.903843 1.576670 1.611642 1.407692
## [176] 1.729996 1.725142 1.518384 1.762287 1.522487 1.508906 1.430412
## [183] 1.524933 1.593408 1.655633 1.616235 1.478078 1.698742 1.832099
## [190] 1.924236 1.347146 1.592261 1.649007 1.366491 1.468893 1.604041
## [197] 1.611650 1.605420 1.642259 1.636456 1.619205 1.508577 1.766130
## [204] 1.399178 1.468000 1.545403 1.201114 1.735832 1.755657 1.624083
fish.alp<-fisher.alpha(bird_Ohio)
fish.alp
## [1] 5.642066 7.896600 6.488217 7.565648 7.896600 7.275326 11.680602
## [8] 8.578491 7.703286 7.612305 7.916266 8.839863 9.316717 9.454690
## [15] 7.814550 9.354043 4.970140 4.970140 6.781181 7.415136 11.156386
## [22] 5.110244 9.198544 7.705935 5.389613 5.679263 6.377738 6.055108
## [29] 6.765902 4.927590 7.224522 8.920683 7.027652 6.430958 5.381526
## [36] 8.310228 6.891754 8.984035 3.690822 5.538659 11.970603 9.586698
## [43] 4.350582 5.112918 6.765902 5.254828 4.718079 3.192559 7.976087
## [50] 5.576450 3.718729 9.354332 5.646225 6.096825 10.837223 5.798205
## [57] 4.212510 4.392532 3.217794 7.064403 4.829737 8.991549 4.986195
## [64] 3.546422 4.711590 6.648249 5.988234 6.114229 6.065899 3.560586
## [71] 3.546422 4.192993 5.427384 5.030672 5.726751 5.687885 6.259378
## [78] 4.238987 4.858106 4.121275 3.717707 5.349775 1.659647 4.890459
## [85] 4.931276 7.342929 5.082987 4.281400 4.257961 4.223961 4.342856
## [92] 5.369842 3.173985 2.544462 4.545684 6.048954 7.602821 5.778996
## [99] 5.414712 7.383522 6.606168 7.981444 7.105981 3.601971 6.964669
## [106] 4.071694 4.557373 5.109555 5.469168 4.810390 3.335706 4.489371
## [113] 4.415941 4.311580 4.062475 3.967505 6.848485 7.622560 6.259936
## [120] 3.401558 4.396019 5.031600 5.661968 4.672620 5.506903 6.274631
## [127] 3.935634 4.959159 3.588003 5.297514 3.882807 3.497519 5.042383
## [134] 4.164308 5.639364 8.224130 6.763796 4.777415 5.769443 5.149475
## [141] 7.797693 3.904973 2.305868 5.871991 7.955229 4.394232 3.450861
## [148] 3.204387 2.846412 4.802582 8.995673 6.871785 3.756033 6.745033
## [155] 8.446768 3.800210 4.713438 4.046218 4.379899 5.104476 4.033430
## [162] 5.926942 4.014640 8.614307 5.345689 5.647828 6.075704 6.342263
## [169] 6.915739 3.675202 2.750911 4.849896 4.653569 3.632950 3.708700
## [176] 4.551192 3.574193 3.328464 4.513385 3.010270 3.314740 5.730437
## [183] 3.296916 4.218990 4.513385 5.923958 3.292541 4.471297 6.113701
## [190] 6.406272 3.014282 5.165024 4.453883 5.647828 2.957055 6.182347
## [197] 4.772041 3.494964 4.741473 4.409155 4.409155 3.815290 4.655622
## [204] 2.103496 3.815290 3.778652 2.415700 6.323830 5.950260 4.993876
Div.Ind<-cbind.data.frame(shannon, simpson, inv.simpson,fish.alp)
Div.Ind
## shannon simpson inv.simpson fish.alp
## 1 1.8917399 0.7923875 4.816667 5.642066
## 2 2.5122269 0.9023669 10.242424 7.896600
## 3 2.5759511 0.9141051 11.642140 6.488217
## 4 2.5306341 0.8955078 9.570093 7.565648
## 5 2.5813695 0.9112426 11.266667 7.896600
## 6 2.5851922 0.9100346 11.115385 7.275326
## 7 2.8543118 0.9286265 14.010811 11.680602
## 8 2.6869090 0.9171429 12.068966 8.578491
## 9 2.6290101 0.9131944 11.520000 7.703286
## 10 2.4471082 0.8927116 9.320675 7.612305
## 11 2.4514742 0.8897290 9.068571 7.916266
## 12 2.6624436 0.9137893 11.599483 8.839863
## 13 2.4801533 0.8793388 8.287671 9.316717
## 14 2.4524231 0.8758573 8.055249 9.454690
## 15 2.4446459 0.8830959 8.554023 7.814550
## 16 2.5985159 0.8967347 9.683794 9.354043
## 17 2.1580650 0.8536155 6.831325 4.970140
## 18 2.0290131 0.8264046 5.760522 4.970140
## 19 2.3439848 0.8624852 7.271945 6.781181
## 20 2.3677896 0.8617998 7.235880 7.415136
## 21 2.6230152 0.8762865 8.083192 11.156386
## 22 2.1818266 0.8433163 6.382284 5.110244
## 23 2.3999167 0.8526139 6.784902 9.198544
## 24 2.3497389 0.8573088 7.008138 7.705935
## 25 2.1019228 0.8130987 5.350417 5.389613
## 26 2.1558323 0.8250548 5.716075 5.679263
## 27 2.1542864 0.8136574 5.366460 6.377738
## 28 2.2277483 0.8264701 5.762696 6.055108
## 29 2.3289428 0.8491358 6.628478 6.765902
## 30 1.8809617 0.7441406 3.908397 4.927590
## 31 2.4493401 0.8553590 6.913669 7.224522
## 32 2.3266446 0.8238062 5.675570 8.920683
## 33 2.2270348 0.8106576 5.281437 7.027652
## 34 1.9964440 0.7780612 4.505747 6.430958
## 35 2.0417370 0.7801904 4.549391 5.381526
## 36 2.4618897 0.8531268 6.808596 8.310228
## 37 2.1320689 0.7831946 4.612431 6.891754
## 38 2.2922720 0.8068698 5.177854 8.984035
## 39 1.5430953 0.6477631 2.838998 3.690822
## 40 2.0627115 0.7808963 4.564050 5.538659
## 41 2.6546098 0.8616564 7.228380 11.970603
## 42 2.3331170 0.7890625 4.740741 9.586698
## 43 1.7535317 0.6954194 3.283203 4.350582
## 44 1.8944254 0.7339693 3.758965 5.112918
## 45 1.9905828 0.7269136 3.661844 6.765902
## 46 1.8202840 0.6911844 3.238179 5.254828
## 47 1.7588983 0.6822566 3.147193 4.718079
## 48 1.2049611 0.5081633 2.033195 3.192559
## 49 2.1437307 0.7527571 4.044605 7.976087
## 50 2.0737966 0.7694515 4.337483 5.576450
## 51 1.6534827 0.6567901 2.913669 3.718729
## 52 2.1077805 0.7160000 3.521127 9.354332
## 53 1.9625262 0.7361333 3.789793 5.646225
## 54 1.7738141 0.6479362 2.840394 6.096825
## 55 2.0210011 0.6747189 3.074264 10.837223
## 56 1.8759969 0.6863983 3.188759 5.798205
## 57 1.4522335 0.5658574 2.303391 4.212510
## 58 1.5969316 0.6459054 2.824104 4.392532
## 59 1.4228140 0.5912465 2.446462 3.217794
## 60 2.2233828 0.7710506 4.367778 7.064403
## 61 1.6941131 0.6341785 2.733574 4.829737
## 62 2.2527933 0.7706531 4.360207 8.991549
## 63 1.4705667 0.5559896 2.252199 4.986195
## 64 1.4376671 0.5789628 2.375087 3.546422
## 65 1.6556983 0.6272199 2.682547 4.711590
## 66 2.0069460 0.7150879 3.509854 6.648249
## 67 1.7242396 0.6403025 2.780114 5.988234
## 68 1.6262868 0.6024793 2.515593 6.114229
## 69 1.9412633 0.7088021 3.434090 6.065899
## 70 1.2809809 0.4988000 1.995211 3.560586
## 71 1.3010760 0.4968140 1.987337 3.546422
## 72 1.5318794 0.5861804 2.416512 4.192993
## 73 1.5531853 0.5995339 2.497090 5.427384
## 74 1.5204937 0.5778835 2.369014 5.030672
## 75 1.7648837 0.6374853 2.758509 5.726751
## 76 1.6943305 0.6346974 2.737457 5.687885
## 77 1.7000499 0.6039282 2.524795 6.259378
## 78 1.3091997 0.4959013 1.983739 4.238987
## 79 1.5702975 0.5915848 2.448489 4.858106
## 80 1.3837769 0.5347080 2.149188 4.121275
## 81 1.3385135 0.5234094 2.098237 3.717707
## 82 1.4513903 0.5429308 2.187852 5.349775
## 83 0.9429183 0.4238292 1.735597 1.659647
## 84 1.4490505 0.5318678 2.136149 4.890459
## 85 1.7895111 0.6617778 2.956636 4.931276
## 86 1.9656594 0.6964237 3.294065 7.342929
## 87 1.5788314 0.5921019 2.451592 5.082987
## 88 1.5022623 0.5775047 2.366890 4.281400
## 89 1.4904512 0.5791837 2.376334 4.257961
## 90 1.4962931 0.5853587 2.411723 4.223961
## 91 1.4059042 0.5223585 2.093621 4.342856
## 92 1.6478061 0.6010774 2.506752 5.369842
## 93 1.2990656 0.5181661 2.075404 3.173985
## 94 1.0930459 0.4416889 1.791116 2.544462
## 95 1.5543977 0.5807846 2.385409 4.545684
## 96 1.7289643 0.6220482 2.645840 6.048954
## 97 1.6443609 0.5677222 2.313327 7.602821
## 98 1.5495614 0.5565515 2.255053 5.778996
## 99 1.4031313 0.5220452 2.092248 5.414712
## 100 1.7226377 0.5952296 2.470536 7.383522
## 101 1.8883235 0.6635526 2.972233 6.606168
## 102 1.9303606 0.6503184 2.859744 7.981444
## 103 1.4901640 0.5207111 2.086424 7.105981
## 104 1.5787674 0.6158734 2.603309 3.601971
## 105 1.5705318 0.5447686 2.196685 6.964669
## 106 1.4178638 0.5379747 2.164384 4.071694
## 107 1.2837894 0.4695946 1.885350 4.557373
## 108 1.5926701 0.5760346 2.358683 5.109555
## 109 1.6081552 0.5821278 2.393076 5.469168
## 110 1.4082954 0.5175781 2.072874 4.810390
## 111 1.2234179 0.5051503 2.020815 3.335706
## 112 1.2528753 0.4630629 1.862416 4.489371
## 113 1.3100790 0.4962000 1.984915 4.415941
## 114 1.4507992 0.5478637 2.211722 4.311580
## 115 1.2323738 0.4671492 1.876698 4.062475
## 116 1.3797947 0.5235294 2.098765 3.967505
## 117 1.7293521 0.6090305 2.557744 6.848485
## 118 1.4854109 0.5106574 2.043558 7.622560
## 119 1.4658616 0.5185606 2.077105 6.259936
## 120 0.9669648 0.3701524 1.587686 3.401558
## 121 1.1795715 0.4405887 1.787593 4.396019
## 122 1.3672704 0.5040980 2.016527 5.031600
## 123 1.5350481 0.5602648 2.274096 5.661968
## 124 1.3090028 0.4785325 1.917665 4.672620
## 125 1.2126036 0.4391298 1.782944 5.506903
## 126 1.3047201 0.4572742 1.842551 6.274631
## 127 1.2040135 0.4574581 1.843175 3.935634
## 128 1.3639687 0.4963772 1.985613 4.959159
## 129 1.1349332 0.4396221 1.784510 3.588003
## 130 1.4067959 0.5037577 2.015144 5.297514
## 131 1.2394246 0.4670187 1.876238 3.882807
## 132 1.3198667 0.4978497 1.991436 3.497519
## 133 1.1560033 0.4104902 1.696325 5.042383
## 134 1.2668167 0.4898061 1.960039 4.164308
## 135 1.3633187 0.4876454 1.951773 5.639364
## 136 1.7184293 0.5769861 2.363989 8.224130
## 137 1.4705114 0.5051541 2.020831 6.763796
## 138 1.4134997 0.5225468 2.094446 4.777415
## 139 1.1657677 0.3938079 1.649642 5.769443
## 140 1.4382794 0.5078587 2.031937 5.149475
## 141 1.6241226 0.5466634 2.205866 7.797693
## 142 0.9479850 0.3572150 1.555730 3.904973
## 143 0.7890530 0.3180077 1.466292 2.305868
## 144 1.4545973 0.4964437 1.985875 5.871991
## 145 1.4366209 0.4751863 1.905438 7.955229
## 146 1.3270892 0.4889245 1.956658 4.394232
## 147 1.1408832 0.4303415 1.755438 3.450861
## 148 0.9003295 0.3341988 1.501950 3.204387
## 149 0.9349969 0.3763435 1.603447 2.846412
## 150 1.1903317 0.4262014 1.742772 4.802582
## 151 1.5749987 0.5111597 2.045658 8.995673
## 152 1.5003813 0.5117908 2.048302 6.871785
## 153 1.0810697 0.4075500 1.687906 3.756033
## 154 1.5488876 0.5420794 2.183785 6.745033
## 155 1.5759803 0.5161432 2.066727 8.446768
## 156 0.9573217 0.3428280 1.521672 3.800210
## 157 1.2080605 0.4343764 1.767960 4.713438
## 158 1.1433944 0.4120049 1.700695 4.046218
## 159 1.0993195 0.4084227 1.690396 4.379899
## 160 1.1174376 0.3921670 1.645189 5.104476
## 161 1.1019272 0.4009981 1.669444 4.033430
## 162 0.9943400 0.3344865 1.502599 5.926942
## 163 1.0646499 0.4009879 1.669415 4.014640
## 164 1.6364990 0.5320156 2.136824 8.614307
## 165 1.2202461 0.4317769 1.759872 5.345689
## 166 1.3055935 0.4590037 1.848441 5.647828
## 167 1.2551904 0.4250527 1.739290 6.075704
## 168 1.3908337 0.4708680 1.889888 6.342263
## 169 1.5319849 0.5338560 2.145260 6.915739
## 170 0.9827985 0.3637872 1.571801 3.675202
## 171 0.9091475 0.3495779 1.537463 2.750911
## 172 1.3284004 0.4747466 1.903843 4.849896
## 173 1.0280144 0.3657520 1.576670 4.653569
## 174 1.0155201 0.3795146 1.611642 3.632950
## 175 0.8256294 0.2896172 1.407692 3.708700
## 176 1.1407526 0.4219639 1.729996 4.551192
## 177 1.1095773 0.4203375 1.725142 3.574193
## 178 0.9370976 0.3414050 1.518384 3.328464
## 179 1.1957853 0.4325555 1.762287 4.513385
## 180 0.9132408 0.3431800 1.522487 3.010270
## 181 0.9268789 0.3372680 1.508906 3.314740
## 182 0.9070407 0.3009006 1.430412 5.730437
## 183 0.9184634 0.3442333 1.524933 3.296916
## 184 1.0505849 0.3724143 1.593408 4.218990
## 185 1.1130730 0.3960015 1.655633 4.513385
## 186 1.1475092 0.3812779 1.616235 5.923958
## 187 0.8861070 0.3234457 1.478078 3.292541
## 188 1.1679846 0.4113292 1.698742 4.471297
## 189 1.3269686 0.4541780 1.832099 6.113701
## 190 1.4025824 0.4803133 1.924236 6.406272
## 191 0.7316835 0.2576901 1.347146 3.014282
## 192 1.0732068 0.3719623 1.592261 5.165024
## 193 1.0880649 0.3935743 1.649007 4.453883
## 194 0.8289934 0.2681985 1.366491 5.647828
## 195 0.8676827 0.3192152 1.468893 2.957055
## 196 1.1044350 0.3765746 1.604041 6.182347
## 197 1.0755235 0.3795178 1.611650 4.772041
## 198 1.0144104 0.3771101 1.605420 3.494964
## 199 1.1081660 0.3910828 1.642259 4.741473
## 200 1.0762302 0.3889234 1.636456 4.409155
## 201 1.0511711 0.3824131 1.619205 4.409155
## 202 0.9282779 0.3371236 1.508577 3.815290
## 203 1.2321592 0.4337904 1.766130 4.655622
## 204 0.7472004 0.2852947 1.399178 2.103496
## 205 0.8967415 0.3188013 1.468000 3.815290
## 206 0.9696155 0.3529198 1.545403 3.778652
## 207 0.4879819 0.1674397 1.201114 2.415700
## 208 1.2601278 0.4239074 1.735832 6.323830
## 209 1.2607309 0.4304125 1.755657 5.950260
## 210 1.0734488 0.3842678 1.624083 4.993876
summary(Div.Ind)
## shannon simpson inv.simpson fish.alp
## Min. :0.488 Min. :0.1674 Min. : 1.201 Min. : 1.660
## 1st Qu.:1.171 1st Qu.:0.4272 1st Qu.: 1.746 1st Qu.: 4.214
## Median :1.453 Median :0.5277 Median : 2.117 Median : 5.112
## Mean :1.559 Mean :0.5678 Mean : 3.192 Mean : 5.521
## 3rd Qu.:1.891 3rd Qu.:0.6944 3rd Qu.: 3.272 3rd Qu.: 6.638
## Max. :2.854 Max. :0.9286 Max. :14.011 Max. :11.971
env_Ohio
## # A tibble: 210 × 6
## ...1 all_stem_den big_stem_bas can_covr_mea can_heig_mea Habitat
## <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 11017. 29.1 92.2 22.6 dry-oak
## 2 2 3200. 25.9 92.7 22.9 dry-oak
## 3 3 7975. 29.5 94.3 24.3 dry-oak
## 4 4 15294. 27.0 97.9 18.9 dry-mesic
## 5 5 17181. 36.2 97.9 26 dry-mesic
## 6 6 7018. 27.7 91.7 26.8 wet-mesic
## 7 7 26318. 38.1 92.2 15.5 dry-oak
## 8 8 10459. 26.9 94.3 29 dry-mesic
## 9 9 5366. 25.8 99.5 27.7 wet-mesic
## 10 10 21003. 13.9 99.5 21.2 dry-oak
## # ℹ 200 more rows
Ohio.env.Div<-cbind.data.frame(env_Ohio, Div.Ind)
Ohio.env.Div
## ...1 all_stem_den big_stem_bas can_covr_mea can_heig_mea Habitat
## 1 1 11016.5982 29.104188 92.20000 22.600000 dry-oak
## 2 2 3199.9803 25.923036 92.72000 22.900000 dry-oak
## 3 3 7974.6286 29.470877 94.28000 24.300000 dry-oak
## 4 4 15294.4149 27.040870 97.92000 18.900000 dry-mesic
## 5 5 17180.6870 36.192879 97.92000 26.000000 dry-mesic
## 6 6 7017.9108 27.663756 91.68000 26.800000 wet-mesic
## 7 7 26317.7188 38.088936 92.20000 15.500000 dry-oak
## 8 8 10458.7869 26.917779 94.28000 29.000000 dry-mesic
## 9 9 5365.8287 25.780004 99.48000 27.700000 wet-mesic
## 10 10 21003.0879 13.873316 99.48000 21.200000 dry-oak
## 11 11 5949.9095 35.891250 97.40000 24.200000 dry-mesic
## 12 12 20158.9946 19.835275 92.72000 21.000000 dry-mesic
## 13 13 12813.3328 16.903017 89.08000 12.600000 dry-oak
## 14 14 8877.0193 38.192311 97.40000 24.650000 dry-oak
## 15 15 8100.6114 18.553685 97.92000 24.500000 wet-mesic
## 16 16 13376.1148 18.041125 94.28000 26.100000 dry-mesic
## 17 17 7160.8282 22.766701 2.76000 25.300000 dry-oak
## 18 18 27098.1501 23.189384 98.44000 19.300000 dry-oak
## 19 19 10749.4861 21.345035 97.40000 19.900000 dry-oak
## 20 20 19115.4746 24.594643 90.64000 20.650000 dry-oak
## 21 21 3626.1817 37.749615 95.84000 39.400000 dry-mesic
## 22 22 12482.1648 34.777772 90.64000 22.000000 dry-oak
## 23 23 12747.9358 23.063846 99.48000 20.600000 dry-oak
## 24 24 4766.6014 19.451376 97.92000 26.500000 dry-mesic
## 25 25 6854.9825 19.273113 94.28000 28.600000 dry-mesic
## 26 26 4015.1219 29.121760 95.32000 23.000000 dry-mesic
## 27 27 10748.1450 35.978796 91.16000 26.000000 dry-mesic
## 28 28 30600.9000 26.873688 93.24000 27.100000 dry-mesic
## 29 29 31500.6084 20.085862 91.16000 27.000000 dry-oak
## 30 30 4498.1482 23.795657 95.84000 25.700000 dry-mesic
## 31 31 8189.2017 45.056406 93.24000 30.100000 dry-mesic
## 32 32 7530.3359 26.487734 50.08000 25.900000 dry-oak
## 33 33 11090.0421 28.899434 96.88000 17.900000 dry-oak
## 34 34 5601.3067 39.079333 91.16000 27.500000 dry-mesic
## 35 35 15536.5986 48.183433 88.04000 28.100000 wet-mesic
## 36 36 20287.6597 25.212928 92.20000 23.500000 wet-mesic
## 37 37 3350.8915 36.182914 98.44000 22.100000 dry-oak
## 38 38 7313.0805 33.735482 93.24000 30.000000 dry-mesic
## 39 39 4881.4612 21.474137 96.36000 25.900000 wet-mesic
## 40 40 706.0402 19.170207 92.72000 30.700000 wet-mesic
## 41 41 25250.6116 53.206300 94.80000 25.500000 wet-mesic
## 42 42 8764.8418 39.151999 94.80000 30.700000 dry-mesic
## 43 43 18382.9805 24.533023 90.98667 18.966667 dry-oak
## 44 44 4028.9272 22.170667 96.36000 29.100000 wet-mesic
## 45 45 17514.1433 15.353375 98.96000 30.100000 wet-mesic
## 46 46 8889.4835 21.182268 98.96000 23.600000 dry-oak
## 47 47 3555.6827 28.542819 96.88000 30.750000 dry-mesic
## 48 48 14417.3464 42.396967 97.92000 20.300000 dry-mesic
## 49 49 4982.5157 31.442504 95.49333 27.933333 wet-mesic
## 50 50 10381.3196 31.795863 93.76000 29.300000 dry-mesic
## 51 51 8099.2703 40.314825 94.80000 25.100000 wet-mesic
## 52 52 13399.7020 56.411250 87.00000 32.000000 wet-mesic
## 53 53 13295.9652 29.939159 97.92000 23.500000 dry-oak
## 54 54 14722.7982 16.751400 95.32000 13.600000 wet-mesic
## 55 55 16610.4115 16.342020 97.92000 18.400000 dry-oak
## 56 56 13037.2939 9.689551 80.76000 15.200000 wet-mesic
## 57 57 14753.4850 44.522594 95.84000 29.000000 dry-mesic
## 58 58 9948.1499 21.641789 90.12000 22.250000 dry-oak
## 59 59 11920.3301 38.436105 92.20000 21.500000 dry-oak
## 60 60 17032.9052 9.486563 92.02667 10.433333 dry-mesic
## 61 61 5143.2087 32.615201 96.88000 20.100000 dry-oak
## 62 62 9632.5223 19.721298 91.68000 21.200000 wet-mesic
## 63 63 12353.4997 36.139811 96.36000 26.700000 dry-mesic
## 64 64 18680.9638 28.144823 94.80000 21.400000 dry-oak
## 65 65 28685.6761 20.426766 96.88000 19.000000 wet-mesic
## 66 66 4594.0226 31.083216 93.41333 33.716667 dry-mesic
## 67 67 11257.3095 31.371081 96.18667 27.600000 wet-mesic
## 68 68 10814.4893 25.454088 94.28000 25.500000 wet-mesic
## 69 69 2156.4604 27.053685 93.76000 26.800000 wet-mesic
## 70 70 12417.1617 42.563004 89.08000 26.050000 dry-mesic
## 71 71 10814.4893 28.819690 94.80000 24.900000 dry-mesic
## 72 72 22986.3911 23.350458 97.40000 20.900000 wet-mesic
## 73 73 2193.8529 33.285927 95.32000 26.000000 dry-mesic
## 74 74 5047.9127 46.833699 93.76000 23.500000 dry-oak
## 75 75 7398.9885 34.091804 91.68000 28.200000 wet-mesic
## 76 76 13834.2128 33.181279 87.00000 25.000000 dry-oak
## 77 77 10940.4721 35.020675 95.32000 23.050000 wet-mesic
## 78 78 10146.6293 15.161798 85.96000 12.400000 wet-mesic
## 79 79 13045.4720 23.485101 88.56000 23.133333 dry-oak
## 80 80 3673.2248 34.509633 97.40000 30.600000 wet-mesic
## 81 81 7181.7331 30.754454 82.84000 20.250000 dry-oak
## 82 82 23264.6262 26.316107 90.12000 20.500000 dry-oak
## 83 83 11640.7538 20.824820 51.64000 25.800000 dry-mesic
## 84 84 28181.7449 19.802207 90.12000 24.700000 dry-oak
## 85 85 28195.5502 23.732624 95.32000 23.300000 dry-oak
## 86 86 10562.5236 19.380590 94.80000 35.250000 wet-mesic
## 87 87 21151.3168 23.157354 97.40000 24.800000 dry-oak
## 88 88 18697.4514 23.077747 91.16000 27.700000 dry-oak
## 89 89 14468.1503 21.461322 94.80000 26.100000 dry-mesic
## 90 90 5830.4480 32.953344 89.60000 27.200000 wet-mesic
## 91 91 20518.7204 15.732301 97.40000 23.200000 dry-oak
## 92 92 26653.8574 32.208053 95.32000 25.300000 dry-oak
## 93 93 3822.7948 40.413335 95.32000 28.750000 dry-mesic
## 94 94 15410.3000 32.868810 87.86667 19.833333 dry-oak
## 95 95 13920.1208 33.785986 95.84000 23.150000 dry-oak
## 96 96 6028.7179 16.245712 97.40000 27.100000 dry-mesic
## 97 97 33686.9678 21.094222 75.04000 21.400000 dry-oak
## 98 98 9793.2153 17.508008 94.28000 18.900000 dry-mesic
## 99 99 14101.3249 27.096552 95.32000 29.150000 dry-mesic
## 100 100 16834.6352 29.316522 88.90667 31.933333 wet-mesic
## 101 101 21995.4101 21.477122 96.88000 24.500000 dry-mesic
## 102 102 7744.9090 17.898485 92.72000 26.700000 dry-mesic
## 103 103 9593.7887 16.635563 91.68000 25.050000 dry-oak
## 104 104 24285.9001 24.399258 93.76000 30.150000 wet-mesic
## 105 105 12615.4317 25.290861 95.14667 34.466667 dry-mesic
## 106 106 10258.0191 25.972316 94.80000 35.850000 wet-mesic
## 107 107 13179.7643 25.789784 92.72000 22.200000 dry-oak
## 108 108 17257.2602 53.510899 90.98667 32.366667 dry-oak
## 109 109 7756.0321 44.066147 92.20000 27.550000 dry-oak
## 110 110 11881.5964 45.079313 96.36000 27.250000 dry-oak
## 111 111 2386.1799 25.552304 97.40000 26.150000 dry-oak
## 112 112 4528.4411 35.258899 90.12000 36.450000 dry-mesic
## 113 113 6856.3237 28.086362 94.80000 22.650000 dry-mesic
## 114 114 7451.5275 27.317615 93.24000 25.400000 dry-oak
## 115 115 15007.7390 17.066930 93.24000 32.000000 dry-mesic
## 116 116 14483.2968 22.782755 92.72000 28.300000 dry-mesic
## 117 117 16589.9004 17.396458 91.68000 19.050000 dry-oak
## 118 118 8625.0537 30.562182 91.16000 24.850000 dry-mesic
## 119 119 7195.5384 31.069054 92.72000 20.350000 dry-oak
## 120 120 28248.0891 36.652811 93.24000 24.800000 dry-oak
## 121 121 21141.5349 27.194025 96.88000 21.250000 dry-oak
## 122 122 23623.0109 33.590943 89.08000 26.900000 dry-mesic
## 123 123 8387.0247 19.212298 87.86667 23.933333 wet-mesic
## 124 124 9283.9195 18.587863 90.29333 18.000000 dry-mesic
## 125 125 9900.0814 16.116506 91.33333 26.466667 wet-mesic
## 126 126 8503.3569 11.243813 89.25333 17.433333 dry-oak
## 127 127 18974.2141 4.049234 62.90667 10.300000 dry-oak
## 128 128 10092.9869 15.391874 82.66667 21.100000 wet-mesic
## 129 129 20349.2177 11.932597 96.53333 14.633333 dry-oak
## 130 130 9126.0401 20.767856 88.90667 21.633333 dry-mesic
## 131 131 12472.9081 20.210830 80.93333 14.666667 wet-mesic
## 132 132 12232.5657 14.047589 81.97333 18.633333 dry-mesic
## 133 133 14276.2703 35.419747 86.48000 24.533333 dry-oak
## 134 134 9470.3037 19.816457 74.00000 19.933333 dry-oak
## 135 135 16205.4308 28.896716 87.17333 24.266667 dry-mesic
## 136 136 24215.5856 16.347861 58.74667 21.816667 dry-mesic
## 137 137 35732.9076 13.770257 75.38667 19.266667 dry-oak
## 138 138 17616.9860 9.126119 27.20000 5.366667 dry-oak
## 139 139 40402.3467 16.039614 38.64000 16.100000 dry-oak
## 140 140 12669.1273 21.789901 92.02667 23.200000 wet-mesic
## 141 141 12628.4742 24.606410 40.72000 13.400000 wet-mesic
## 142 142 4672.6465 41.939241 89.60000 25.966667 dry-oak
## 143 143 4487.4191 19.608511 76.77333 15.466667 dry-oak
## 144 144 21179.3744 15.307731 86.48000 13.800000 dry-oak
## 145 145 21661.6911 5.284569 16.10667 19.433333 dry-oak
## 146 146 14327.0210 24.792838 90.64000 28.866667 wet-mesic
## 147 147 17115.3681 31.592483 81.28000 21.266667 wet-mesic
## 148 148 7468.4090 18.921078 89.25333 16.133333 dry-oak
## 149 149 11978.1804 19.483913 85.44000 22.300000 dry-oak
## 150 150 12349.7921 24.521517 64.64000 24.666667 dry-oak
## 151 151 7753.3498 16.900129 84.74667 17.066667 wet-mesic
## 152 152 13473.4616 22.683557 90.64000 17.500000 dry-mesic
## 153 153 6794.9750 16.587686 91.68000 14.666667 dry-mesic
## 154 154 18811.8642 28.354145 95.49333 23.733333 wet-mesic
## 155 155 17860.6422 29.345955 85.44000 27.133333 wet-mesic
## 156 156 19429.4453 14.661126 68.80000 16.700000 wet-mesic
## 157 157 13096.2228 19.571580 92.02667 17.700000 dry-oak
## 158 158 10633.1007 24.871309 87.52000 17.223333 dry-oak
## 159 159 9839.4956 19.421346 81.62667 17.233333 wet-mesic
## 160 160 13903.5019 25.149450 90.98667 25.000000 dry-oak
## 161 161 7019.0674 20.364822 87.52000 19.366667 dry-oak
## 162 162 12054.4910 10.341282 85.78667 19.666667 wet-mesic
## 163 163 9442.6931 18.427794 84.05333 17.933333 dry-oak
## 164 164 27387.6927 12.906460 75.04000 19.066667 dry-oak
## 165 165 20938.6631 28.784504 92.02667 21.583333 dry-mesic
## 166 166 14979.3656 28.462722 92.37333 22.900000 dry-mesic
## 167 167 4543.2718 17.290489 67.76000 26.066667 wet-mesic
## 168 168 13498.3899 13.315438 74.00000 15.866667 wet-mesic
## 169 169 21045.5292 19.118196 97.22667 23.583333 wet-mesic
## 170 170 2858.6616 16.429112 94.10667 20.600000 dry-oak
## 171 171 3457.3105 29.545064 90.29333 33.333333 wet-mesic
## 172 172 12586.9270 29.319863 95.49333 23.833333 dry-oak
## 173 173 9569.1229 12.142800 62.56000 12.733333 dry-oak
## 174 174 9627.2890 20.551505 78.16000 15.600000 dry-mesic
## 175 175 19614.6728 4.411145 61.86667 10.000000 dry-oak
## 176 176 13437.5415 21.081519 76.08000 20.900000 dry-oak
## 177 177 14094.6192 19.109340 90.98667 17.466667 wet-mesic
## 178 178 15720.1692 23.404051 88.90667 21.133333 dry-oak
## 179 179 11112.4726 26.803913 89.94667 24.066667 dry-oak
## 180 180 13826.0347 23.429406 90.29333 20.566667 dry-oak
## 181 181 14837.4735 29.854889 81.97333 17.733333 wet-mesic
## 182 182 17745.2040 10.091687 57.70667 13.600000 dry-oak
## 183 183 13238.3775 27.585043 74.69333 17.466667 dry-mesic
## 184 184 10748.0137 23.852671 86.13333 27.166667 wet-mesic
## 185 185 26095.1520 9.328745 63.25333 8.400000 dry-oak
## 186 186 18309.9837 6.276581 95.84000 9.800000 dry-mesic
## 187 187 10487.3697 20.320281 86.13333 15.266667 dry-oak
## 188 188 12817.8032 18.314022 84.74667 17.790000 dry-mesic
## 189 189 29962.9391 20.851176 80.58667 21.000000 dry-oak
## 190 190 22338.0700 5.870011 34.13333 18.400000 dry-oak
## 191 191 12999.4543 16.983580 77.46667 11.133333 dry-oak
## 192 192 10126.4872 12.635413 57.01333 8.966667 dry-oak
## 193 193 4120.0153 33.505456 90.29333 23.533333 dry-oak
## 194 194 23438.8619 3.375055 42.10667 7.400000 dry-oak
## 195 195 7526.3125 45.167039 88.21333 25.133333 dry-oak
## 196 196 25378.3826 12.674609 80.58667 18.843333 dry-mesic
## 197 197 12485.4255 40.605261 82.32000 23.156667 dry-mesic
## 198 198 11716.1172 29.021132 92.72000 21.166667 wet-mesic
## 199 199 18906.2129 23.600876 74.34667 19.816667 dry-mesic
## 200 200 12960.5894 25.533512 90.64000 21.410000 dry-oak
## 201 201 2509.9275 24.636020 80.93333 22.066667 dry-mesic
## 202 202 14193.1758 26.843018 86.48000 20.466667 dry-mesic
## 203 203 12231.6717 29.587419 94.80000 27.066667 wet-mesic
## 204 204 6738.3345 40.580331 87.86667 24.466667 dry-oak
## 205 205 9116.2050 17.105751 83.36000 11.500000 dry-oak
## 206 206 7415.6074 25.667489 96.18667 17.433333 dry-oak
## 207 207 16024.9895 19.691871 95.84000 24.000000 dry-oak
## 208 208 19835.8204 7.986256 70.53333 16.200000 dry-oak
## 209 209 16300.4110 8.486471 51.81333 13.533333 wet-mesic
## 210 210 28912.0038 22.976962 36.56000 20.866667 wet-mesic
## shannon simpson inv.simpson fish.alp
## 1 1.8917399 0.7923875 4.816667 5.642066
## 2 2.5122269 0.9023669 10.242424 7.896600
## 3 2.5759511 0.9141051 11.642140 6.488217
## 4 2.5306341 0.8955078 9.570093 7.565648
## 5 2.5813695 0.9112426 11.266667 7.896600
## 6 2.5851922 0.9100346 11.115385 7.275326
## 7 2.8543118 0.9286265 14.010811 11.680602
## 8 2.6869090 0.9171429 12.068966 8.578491
## 9 2.6290101 0.9131944 11.520000 7.703286
## 10 2.4471082 0.8927116 9.320675 7.612305
## 11 2.4514742 0.8897290 9.068571 7.916266
## 12 2.6624436 0.9137893 11.599483 8.839863
## 13 2.4801533 0.8793388 8.287671 9.316717
## 14 2.4524231 0.8758573 8.055249 9.454690
## 15 2.4446459 0.8830959 8.554023 7.814550
## 16 2.5985159 0.8967347 9.683794 9.354043
## 17 2.1580650 0.8536155 6.831325 4.970140
## 18 2.0290131 0.8264046 5.760522 4.970140
## 19 2.3439848 0.8624852 7.271945 6.781181
## 20 2.3677896 0.8617998 7.235880 7.415136
## 21 2.6230152 0.8762865 8.083192 11.156386
## 22 2.1818266 0.8433163 6.382284 5.110244
## 23 2.3999167 0.8526139 6.784902 9.198544
## 24 2.3497389 0.8573088 7.008138 7.705935
## 25 2.1019228 0.8130987 5.350417 5.389613
## 26 2.1558323 0.8250548 5.716075 5.679263
## 27 2.1542864 0.8136574 5.366460 6.377738
## 28 2.2277483 0.8264701 5.762696 6.055108
## 29 2.3289428 0.8491358 6.628478 6.765902
## 30 1.8809617 0.7441406 3.908397 4.927590
## 31 2.4493401 0.8553590 6.913669 7.224522
## 32 2.3266446 0.8238062 5.675570 8.920683
## 33 2.2270348 0.8106576 5.281437 7.027652
## 34 1.9964440 0.7780612 4.505747 6.430958
## 35 2.0417370 0.7801904 4.549391 5.381526
## 36 2.4618897 0.8531268 6.808596 8.310228
## 37 2.1320689 0.7831946 4.612431 6.891754
## 38 2.2922720 0.8068698 5.177854 8.984035
## 39 1.5430953 0.6477631 2.838998 3.690822
## 40 2.0627115 0.7808963 4.564050 5.538659
## 41 2.6546098 0.8616564 7.228380 11.970603
## 42 2.3331170 0.7890625 4.740741 9.586698
## 43 1.7535317 0.6954194 3.283203 4.350582
## 44 1.8944254 0.7339693 3.758965 5.112918
## 45 1.9905828 0.7269136 3.661844 6.765902
## 46 1.8202840 0.6911844 3.238179 5.254828
## 47 1.7588983 0.6822566 3.147193 4.718079
## 48 1.2049611 0.5081633 2.033195 3.192559
## 49 2.1437307 0.7527571 4.044605 7.976087
## 50 2.0737966 0.7694515 4.337483 5.576450
## 51 1.6534827 0.6567901 2.913669 3.718729
## 52 2.1077805 0.7160000 3.521127 9.354332
## 53 1.9625262 0.7361333 3.789793 5.646225
## 54 1.7738141 0.6479362 2.840394 6.096825
## 55 2.0210011 0.6747189 3.074264 10.837223
## 56 1.8759969 0.6863983 3.188759 5.798205
## 57 1.4522335 0.5658574 2.303391 4.212510
## 58 1.5969316 0.6459054 2.824104 4.392532
## 59 1.4228140 0.5912465 2.446462 3.217794
## 60 2.2233828 0.7710506 4.367778 7.064403
## 61 1.6941131 0.6341785 2.733574 4.829737
## 62 2.2527933 0.7706531 4.360207 8.991549
## 63 1.4705667 0.5559896 2.252199 4.986195
## 64 1.4376671 0.5789628 2.375087 3.546422
## 65 1.6556983 0.6272199 2.682547 4.711590
## 66 2.0069460 0.7150879 3.509854 6.648249
## 67 1.7242396 0.6403025 2.780114 5.988234
## 68 1.6262868 0.6024793 2.515593 6.114229
## 69 1.9412633 0.7088021 3.434090 6.065899
## 70 1.2809809 0.4988000 1.995211 3.560586
## 71 1.3010760 0.4968140 1.987337 3.546422
## 72 1.5318794 0.5861804 2.416512 4.192993
## 73 1.5531853 0.5995339 2.497090 5.427384
## 74 1.5204937 0.5778835 2.369014 5.030672
## 75 1.7648837 0.6374853 2.758509 5.726751
## 76 1.6943305 0.6346974 2.737457 5.687885
## 77 1.7000499 0.6039282 2.524795 6.259378
## 78 1.3091997 0.4959013 1.983739 4.238987
## 79 1.5702975 0.5915848 2.448489 4.858106
## 80 1.3837769 0.5347080 2.149188 4.121275
## 81 1.3385135 0.5234094 2.098237 3.717707
## 82 1.4513903 0.5429308 2.187852 5.349775
## 83 0.9429183 0.4238292 1.735597 1.659647
## 84 1.4490505 0.5318678 2.136149 4.890459
## 85 1.7895111 0.6617778 2.956636 4.931276
## 86 1.9656594 0.6964237 3.294065 7.342929
## 87 1.5788314 0.5921019 2.451592 5.082987
## 88 1.5022623 0.5775047 2.366890 4.281400
## 89 1.4904512 0.5791837 2.376334 4.257961
## 90 1.4962931 0.5853587 2.411723 4.223961
## 91 1.4059042 0.5223585 2.093621 4.342856
## 92 1.6478061 0.6010774 2.506752 5.369842
## 93 1.2990656 0.5181661 2.075404 3.173985
## 94 1.0930459 0.4416889 1.791116 2.544462
## 95 1.5543977 0.5807846 2.385409 4.545684
## 96 1.7289643 0.6220482 2.645840 6.048954
## 97 1.6443609 0.5677222 2.313327 7.602821
## 98 1.5495614 0.5565515 2.255053 5.778996
## 99 1.4031313 0.5220452 2.092248 5.414712
## 100 1.7226377 0.5952296 2.470536 7.383522
## 101 1.8883235 0.6635526 2.972233 6.606168
## 102 1.9303606 0.6503184 2.859744 7.981444
## 103 1.4901640 0.5207111 2.086424 7.105981
## 104 1.5787674 0.6158734 2.603309 3.601971
## 105 1.5705318 0.5447686 2.196685 6.964669
## 106 1.4178638 0.5379747 2.164384 4.071694
## 107 1.2837894 0.4695946 1.885350 4.557373
## 108 1.5926701 0.5760346 2.358683 5.109555
## 109 1.6081552 0.5821278 2.393076 5.469168
## 110 1.4082954 0.5175781 2.072874 4.810390
## 111 1.2234179 0.5051503 2.020815 3.335706
## 112 1.2528753 0.4630629 1.862416 4.489371
## 113 1.3100790 0.4962000 1.984915 4.415941
## 114 1.4507992 0.5478637 2.211722 4.311580
## 115 1.2323738 0.4671492 1.876698 4.062475
## 116 1.3797947 0.5235294 2.098765 3.967505
## 117 1.7293521 0.6090305 2.557744 6.848485
## 118 1.4854109 0.5106574 2.043558 7.622560
## 119 1.4658616 0.5185606 2.077105 6.259936
## 120 0.9669648 0.3701524 1.587686 3.401558
## 121 1.1795715 0.4405887 1.787593 4.396019
## 122 1.3672704 0.5040980 2.016527 5.031600
## 123 1.5350481 0.5602648 2.274096 5.661968
## 124 1.3090028 0.4785325 1.917665 4.672620
## 125 1.2126036 0.4391298 1.782944 5.506903
## 126 1.3047201 0.4572742 1.842551 6.274631
## 127 1.2040135 0.4574581 1.843175 3.935634
## 128 1.3639687 0.4963772 1.985613 4.959159
## 129 1.1349332 0.4396221 1.784510 3.588003
## 130 1.4067959 0.5037577 2.015144 5.297514
## 131 1.2394246 0.4670187 1.876238 3.882807
## 132 1.3198667 0.4978497 1.991436 3.497519
## 133 1.1560033 0.4104902 1.696325 5.042383
## 134 1.2668167 0.4898061 1.960039 4.164308
## 135 1.3633187 0.4876454 1.951773 5.639364
## 136 1.7184293 0.5769861 2.363989 8.224130
## 137 1.4705114 0.5051541 2.020831 6.763796
## 138 1.4134997 0.5225468 2.094446 4.777415
## 139 1.1657677 0.3938079 1.649642 5.769443
## 140 1.4382794 0.5078587 2.031937 5.149475
## 141 1.6241226 0.5466634 2.205866 7.797693
## 142 0.9479850 0.3572150 1.555730 3.904973
## 143 0.7890530 0.3180077 1.466292 2.305868
## 144 1.4545973 0.4964437 1.985875 5.871991
## 145 1.4366209 0.4751863 1.905438 7.955229
## 146 1.3270892 0.4889245 1.956658 4.394232
## 147 1.1408832 0.4303415 1.755438 3.450861
## 148 0.9003295 0.3341988 1.501950 3.204387
## 149 0.9349969 0.3763435 1.603447 2.846412
## 150 1.1903317 0.4262014 1.742772 4.802582
## 151 1.5749987 0.5111597 2.045658 8.995673
## 152 1.5003813 0.5117908 2.048302 6.871785
## 153 1.0810697 0.4075500 1.687906 3.756033
## 154 1.5488876 0.5420794 2.183785 6.745033
## 155 1.5759803 0.5161432 2.066727 8.446768
## 156 0.9573217 0.3428280 1.521672 3.800210
## 157 1.2080605 0.4343764 1.767960 4.713438
## 158 1.1433944 0.4120049 1.700695 4.046218
## 159 1.0993195 0.4084227 1.690396 4.379899
## 160 1.1174376 0.3921670 1.645189 5.104476
## 161 1.1019272 0.4009981 1.669444 4.033430
## 162 0.9943400 0.3344865 1.502599 5.926942
## 163 1.0646499 0.4009879 1.669415 4.014640
## 164 1.6364990 0.5320156 2.136824 8.614307
## 165 1.2202461 0.4317769 1.759872 5.345689
## 166 1.3055935 0.4590037 1.848441 5.647828
## 167 1.2551904 0.4250527 1.739290 6.075704
## 168 1.3908337 0.4708680 1.889888 6.342263
## 169 1.5319849 0.5338560 2.145260 6.915739
## 170 0.9827985 0.3637872 1.571801 3.675202
## 171 0.9091475 0.3495779 1.537463 2.750911
## 172 1.3284004 0.4747466 1.903843 4.849896
## 173 1.0280144 0.3657520 1.576670 4.653569
## 174 1.0155201 0.3795146 1.611642 3.632950
## 175 0.8256294 0.2896172 1.407692 3.708700
## 176 1.1407526 0.4219639 1.729996 4.551192
## 177 1.1095773 0.4203375 1.725142 3.574193
## 178 0.9370976 0.3414050 1.518384 3.328464
## 179 1.1957853 0.4325555 1.762287 4.513385
## 180 0.9132408 0.3431800 1.522487 3.010270
## 181 0.9268789 0.3372680 1.508906 3.314740
## 182 0.9070407 0.3009006 1.430412 5.730437
## 183 0.9184634 0.3442333 1.524933 3.296916
## 184 1.0505849 0.3724143 1.593408 4.218990
## 185 1.1130730 0.3960015 1.655633 4.513385
## 186 1.1475092 0.3812779 1.616235 5.923958
## 187 0.8861070 0.3234457 1.478078 3.292541
## 188 1.1679846 0.4113292 1.698742 4.471297
## 189 1.3269686 0.4541780 1.832099 6.113701
## 190 1.4025824 0.4803133 1.924236 6.406272
## 191 0.7316835 0.2576901 1.347146 3.014282
## 192 1.0732068 0.3719623 1.592261 5.165024
## 193 1.0880649 0.3935743 1.649007 4.453883
## 194 0.8289934 0.2681985 1.366491 5.647828
## 195 0.8676827 0.3192152 1.468893 2.957055
## 196 1.1044350 0.3765746 1.604041 6.182347
## 197 1.0755235 0.3795178 1.611650 4.772041
## 198 1.0144104 0.3771101 1.605420 3.494964
## 199 1.1081660 0.3910828 1.642259 4.741473
## 200 1.0762302 0.3889234 1.636456 4.409155
## 201 1.0511711 0.3824131 1.619205 4.409155
## 202 0.9282779 0.3371236 1.508577 3.815290
## 203 1.2321592 0.4337904 1.766130 4.655622
## 204 0.7472004 0.2852947 1.399178 2.103496
## 205 0.8967415 0.3188013 1.468000 3.815290
## 206 0.9696155 0.3529198 1.545403 3.778652
## 207 0.4879819 0.1674397 1.201114 2.415700
## 208 1.2601278 0.4239074 1.735832 6.323830
## 209 1.2607309 0.4304125 1.755657 5.950260
## 210 1.0734488 0.3842678 1.624083 4.993876
library("rstatix")
## Warning: package 'rstatix' was built under R version 4.4.2
##
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
##
## filter
Ohio.env.Div %>%
group_by(Habitat) %>%
get_summary_stats(shannon, type = "mean_sd")
## # A tibble: 3 × 5
## Habitat variable n mean sd
## <chr> <fct> <dbl> <dbl> <dbl>
## 1 dry-mesic shannon 60 1.67 0.527
## 2 dry-oak shannon 95 1.46 0.507
## 3 wet-mesic shannon 55 1.61 0.452
Ohio.env.Div %>%
group_by(Habitat) %>%
get_summary_stats(simpson, type = "mean_sd")
## # A tibble: 3 × 5
## Habitat variable n mean sd
## <chr> <fct> <dbl> <dbl> <dbl>
## 1 dry-mesic simpson 60 0.606 0.18
## 2 dry-oak simpson 95 0.537 0.185
## 3 wet-mesic simpson 55 0.58 0.157
Ohio.env.Div %>%
group_by(Habitat) %>%
get_summary_stats(fish.alp, type = "mean_sd")
## # A tibble: 3 × 5
## Habitat variable n mean sd
## <chr> <fct> <dbl> <dbl> <dbl>
## 1 dry-mesic fish.alp 60 5.77 1.90
## 2 dry-oak fish.alp 95 5.22 1.84
## 3 wet-mesic fish.alp 55 5.76 1.87
Ohio.env.Div %>%
group_by(Habitat) %>%
get_summary_stats(inv.simpson, type = "mean_sd")
## # A tibble: 3 × 5
## Habitat variable n mean sd
## <chr> <fct> <dbl> <dbl> <dbl>
## 1 dry-mesic inv.simpson 60 3.66 2.81
## 2 dry-oak inv.simpson 95 2.98 2.44
## 3 wet-mesic inv.simpson 55 3.04 2.15
library("ggplot2")
## Warning: package 'ggplot2' was built under R version 4.4.2
Shanon.habitat<-ggplot(Ohio.env.Div, aes(x = Habitat, y = shannon, fill = Habitat)) +
geom_boxplot() +
stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")
Simp.habitat<-ggplot(Ohio.env.Div, aes(x = Habitat, y = simpson, fill = Habitat)) +
geom_boxplot() +
stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")
InvSimp.habitat<-ggplot(Ohio.env.Div, aes(x = Habitat, y = inv.simpson, fill = Habitat)) +
geom_boxplot() +
stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")
Fish.habitat<-ggplot(Ohio.env.Div, aes(x = Habitat, y = fish.alp, fill = Habitat)) +
geom_boxplot() +
stat_summary(fun = mean, geom = "point", shape =21, size = 3, colour = "black", fill="yellow")
library("gridExtra")
## Warning: package 'gridExtra' was built under R version 4.4.2
grid.arrange(Shanon.habitat, Simp.habitat, InvSimp.habitat, Fish.habitat,
nrow=2, ncol=2)
grid.arrange(Shanon.habitat, Simp.habitat, InvSimp.habitat, Fish.habitat, nrow=2, ncol=2)
shannon_aov <- aov(Ohio.env.Div$shannon ~ Ohio.env.Div$Habitat)
summary(shannon_aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## Ohio.env.Div$Habitat 2 1.66 0.8308 3.335 0.0375 *
## Residuals 207 51.57 0.2491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
simpson_aov <- aov(Ohio.env.Div$simpson ~ Ohio.env.Div$Habitat)
summary(simpson_aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## Ohio.env.Div$Habitat 2 0.189 0.09426 3.015 0.0512 .
## Residuals 207 6.471 0.03126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
inv.simpson_aov <- aov(Ohio.env.Div$inv.simpson ~ Ohio.env.Div$Habitat)
summary(inv.simpson_aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## Ohio.env.Div$Habitat 2 18.2 9.109 1.481 0.23
## Residuals 207 1273.6 6.153
fish.alp_aov <- aov(Ohio.env.Div$fish.alp ~ Ohio.env.Div$Habitat)
summary(fish.alp_aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## Ohio.env.Div$Habitat 2 15.4 7.687 2.206 0.113
## Residuals 207 721.4 3.485
distance_matrix<-vegdist(Ohio.env.Div[,10:10], method="bray", binary=FALSE)
adonis2(distance_matrix ~ Habitat, data=Ohio.env.Div)
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = distance_matrix ~ Habitat, data = Ohio.env.Div)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 0.1221 0.02272 2.4058 0.063 .
## Residual 207 5.2533 0.97728
## Total 209 5.3754 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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$shannon ~ Ohio.env.Div$Habitat)
##
## $`Ohio.env.Div$Habitat`
## diff lwr upr p adj
## wet-mesic-dry-oak 0.14147731 -0.058161073 0.3411157 0.2180524
## dry-mesic-dry-oak 0.20188576 0.007587024 0.3961845 0.0396316
## dry-mesic-wet-mesic 0.06040845 -0.159546381 0.2803633 0.7935133
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$simpson ~ Ohio.env.Div$Habitat)
##
## $`Ohio.env.Div$Habitat`
## diff lwr upr p adj
## wet-mesic-dry-oak 0.04373077 -0.0269916799 0.1144532 0.3125264
## dry-mesic-dry-oak 0.06936909 0.0005382234 0.1382000 0.0477573
## dry-mesic-wet-mesic 0.02563832 -0.0522812927 0.1035579 0.7177126
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$inv.simpson ~ Ohio.env.Div$Habitat)
##
## $`Ohio.env.Div$Habitat`
## diff lwr upr p adj
## wet-mesic-dry-oak 0.06042921 -0.9317123 1.052571 0.9886676
## dry-mesic-dry-oak 0.67188001 -0.2937251 1.637485 0.2301703
## dry-mesic-wet-mesic 0.61145080 -0.4816572 1.704559 0.3853924
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$fish.alp ~ Ohio.env.Div$Habitat)
##
## $`Ohio.env.Div$Habitat`
## diff lwr upr p adj
## wet-mesic-dry-oak 0.538910024 -0.2077869 1.2856069 0.2061985
## dry-mesic-dry-oak 0.547843742 -0.1788816 1.2745691 0.1789658
## dry-mesic-wet-mesic 0.008933717 -0.8137517 0.8316192 0.9996378
shannon_Tukey_plot <- as.data.frame(shannon_Tukey$`Ohio.env.Div$Habitat`)
shannon_Tukey_plot$comparison <- rownames(shannon_Tukey_plot)
shannon_Tukey_plot
## diff lwr upr p adj
## wet-mesic-dry-oak 0.14147731 -0.058161073 0.3411157 0.21805244
## dry-mesic-dry-oak 0.20188576 0.007587024 0.3961845 0.03963162
## dry-mesic-wet-mesic 0.06040845 -0.159546381 0.2803633 0.79351326
## comparison
## wet-mesic-dry-oak wet-mesic-dry-oak
## dry-mesic-dry-oak dry-mesic-dry-oak
## dry-mesic-wet-mesic dry-mesic-wet-mesic
simpson_Tukey_plot <- as.data.frame(simpson_Tukey$`Ohio.env.Div$Habitat`)
simpson_Tukey_plot$comparison <- rownames(simpson_Tukey_plot)
simpson_Tukey_plot
## diff lwr upr p adj
## wet-mesic-dry-oak 0.04373077 -0.0269916799 0.1144532 0.31252636
## dry-mesic-dry-oak 0.06936909 0.0005382234 0.1382000 0.04775727
## dry-mesic-wet-mesic 0.02563832 -0.0522812927 0.1035579 0.71771258
## comparison
## wet-mesic-dry-oak wet-mesic-dry-oak
## dry-mesic-dry-oak dry-mesic-dry-oak
## dry-mesic-wet-mesic dry-mesic-wet-mesic
inv.simpson_Tukey_plot <- as.data.frame(inv.simpson_Tukey$`Ohio.env.Div$Habitat`)
inv.simpson_Tukey_plot$comparison <- rownames(inv.simpson_Tukey_plot)
inv.simpson_Tukey_plot
## diff lwr upr p adj
## wet-mesic-dry-oak 0.06042921 -0.9317123 1.052571 0.9886676
## dry-mesic-dry-oak 0.67188001 -0.2937251 1.637485 0.2301703
## dry-mesic-wet-mesic 0.61145080 -0.4816572 1.704559 0.3853924
## comparison
## wet-mesic-dry-oak wet-mesic-dry-oak
## dry-mesic-dry-oak dry-mesic-dry-oak
## dry-mesic-wet-mesic dry-mesic-wet-mesic
fish.alp_Tukey_plot <- as.data.frame(fish.alp_Tukey$`Ohio.env.Div$Habitat`)
fish.alp_Tukey_plot$comparison <- rownames(fish.alp_Tukey_plot)
fish.alp_Tukey_plot
## diff lwr upr p adj
## wet-mesic-dry-oak 0.538910024 -0.2077869 1.2856069 0.2061985
## dry-mesic-dry-oak 0.547843742 -0.1788816 1.2745691 0.1789658
## dry-mesic-wet-mesic 0.008933717 -0.8137517 0.8316192 0.9996378
## comparison
## wet-mesic-dry-oak wet-mesic-dry-oak
## dry-mesic-dry-oak dry-mesic-dry-oak
## dry-mesic-wet-mesic dry-mesic-wet-mesic
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 = "Habitat 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 = "Habitat 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 = "Habitat 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 = "Habitat 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.habitat,nrow=1, ncol=2)
grid.arrange(simpson_Tukey_plot_result_meandiff,Simp.habitat,nrow=1, ncol=2)
grid.arrange(inv.simpson_Tukey_plot_result_meandiff,InvSimp.habitat,nrow=1, ncol=2)
grid.arrange(fish.alp_Tukey_plot_result_meandiff,Fish.habitat,nrow=1, ncol=2)
