dat_try2019=
read.csv("cover19.csv")%>%
mutate(DM_Cover=as.numeric(DM_Cover)) %>%
unite("code",c(Plot,Subplot), sep="_") %>%
as_tibble() %>%
select(code,Species,DM_Cover) %>%
pivot_wider(names_from = "Species", values_from = "DM_Cover", values_fill=0)
trt_try=
read.csv("trt.csv") %>%
unite("code",c(Plot,Subplot), sep="_") %>%
rename(herb=Herbivory.Tr, fire= Fire.Energy.Tr)
trt2019=
dat_try2019 %>%
inner_join(trt_try) %>%
select(code, herb, fire)
dat2019ord=
read.csv("cover19.csv")%>%
mutate(DM_Cover=as.numeric(DM_Cover)) %>%
unite("code",c(Plot,Subplot), sep="_") %>%
as_tibble() %>%
select(code,Species,DM_Cover) %>%
pivot_wider(names_from = "Species", values_from = "DM_Cover", values_fill=0) %>%
select(-code)
set.seed(101)
dat19_NMDS=metaMDS(dat2019ord,k=4,trymax =50)
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.08286534
## Run 1 stress 0.08294884
## ... Procrustes: rmse 0.009565545 max resid 0.0230346
## Run 2 stress 0.08276713
## ... New best solution
## ... Procrustes: rmse 0.008812452 max resid 0.02523425
## Run 3 stress 0.08279477
## ... Procrustes: rmse 0.004345743 max resid 0.01846326
## Run 4 stress 0.08276648
## ... New best solution
## ... Procrustes: rmse 0.003135422 max resid 0.01012392
## Run 5 stress 0.08282027
## ... Procrustes: rmse 0.004655002 max resid 0.01350922
## Run 6 stress 0.0827977
## ... Procrustes: rmse 0.006585951 max resid 0.01403624
## Run 7 stress 0.08283218
## ... Procrustes: rmse 0.006080729 max resid 0.02513525
## Run 8 stress 0.0827878
## ... Procrustes: rmse 0.005399961 max resid 0.01453105
## Run 9 stress 0.08277089
## ... Procrustes: rmse 0.002549902 max resid 0.01237868
## Run 10 stress 0.08282023
## ... Procrustes: rmse 0.004102648 max resid 0.01080994
## Run 11 stress 0.08283244
## ... Procrustes: rmse 0.006253655 max resid 0.02412396
## Run 12 stress 0.08277987
## ... Procrustes: rmse 0.00342222 max resid 0.01650102
## Run 13 stress 0.08276703
## ... Procrustes: rmse 0.003532207 max resid 0.01658631
## Run 14 stress 0.08279689
## ... Procrustes: rmse 0.003873965 max resid 0.01271632
## Run 15 stress 0.08285625
## ... Procrustes: rmse 0.006990764 max resid 0.0250627
## Run 16 stress 0.08296518
## ... Procrustes: rmse 0.008161255 max resid 0.02014471
## Run 17 stress 0.08276953
## ... Procrustes: rmse 0.00256115 max resid 0.01324126
## Run 18 stress 0.08283339
## ... Procrustes: rmse 0.00616181 max resid 0.02546232
## Run 19 stress 0.08284755
## ... Procrustes: rmse 0.005575326 max resid 0.02071755
## Run 20 stress 0.08282547
## ... Procrustes: rmse 0.005399401 max resid 0.0151742
## Run 21 stress 0.08279737
## ... Procrustes: rmse 0.004042822 max resid 0.01835939
## Run 22 stress 0.08293974
## ... Procrustes: rmse 0.005629742 max resid 0.02423497
## Run 23 stress 0.08279408
## ... Procrustes: rmse 0.006255998 max resid 0.01770602
## Run 24 stress 0.08283588
## ... Procrustes: rmse 0.006619006 max resid 0.02107885
## Run 25 stress 0.08277327
## ... Procrustes: rmse 0.003068834 max resid 0.01313849
## Run 26 stress 0.08292902
## ... Procrustes: rmse 0.007779289 max resid 0.02681255
## Run 27 stress 0.08283633
## ... Procrustes: rmse 0.006969838 max resid 0.02236944
## Run 28 stress 0.08276216
## ... New best solution
## ... Procrustes: rmse 0.00306087 max resid 0.01522741
## Run 29 stress 0.08284364
## ... Procrustes: rmse 0.006558538 max resid 0.02378783
## Run 30 stress 0.08295326
## ... Procrustes: rmse 0.008551753 max resid 0.02482498
## Run 31 stress 0.0827622
## ... Procrustes: rmse 0.002530703 max resid 0.00822494
## ... Similar to previous best
## *** Solution reached
dat19_NMDS
##
## Call:
## metaMDS(comm = dat2019ord, k = 4, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(dat2019ord))
## Distance: bray
##
## Dimensions: 4
## Stress: 0.08276216
## Stress type 1, weak ties
## Two convergent solutions found after 31 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(dat2019ord))'
plot(dat19_NMDS)
species.scores <- as.data.frame(scores(dat19_NMDS, "species"))
species.scores
## NMDS1 NMDS2 NMDS3
## Glandularia bipinnatifida -0.0429834560 0.0467468669 -3.132455e-02
## Nassella leucotricha -0.0427092951 0.0320159571 2.463339e-02
## Astragalus nuttallianus -0.0139963048 0.0051679423 -1.000227e-02
## Evax verna -0.0167845785 -0.0220651442 1.405196e-02
## Forb A -0.0830781707 -0.0001954664 -2.853750e-02
## Forb B -0.0566243416 -0.0587096565 3.471469e-02
## Heterotheca canescens -0.0566243416 -0.0587096565 3.471469e-02
## Limnodea arkansana -0.0552627868 -0.0383967378 1.920410e-02
## Medicago minima -0.0739582223 0.0063354194 -6.690599e-03
## Not Filaree -0.0375721410 -0.0380647333 -1.721804e-02
## Senecio douglasii -0.0230156661 -0.0189292947 2.562987e-02
## Trilobe -0.0668990696 0.0064820463 1.174582e-02
## Erodium cicutarium -0.0760753164 0.0177883468 -6.713124e-02
## Ratibida columnifera -0.0667856751 0.0000737225 -4.279569e-03
## Hilaria belangeri -0.0162848948 -0.0590181841 -2.675151e-02
## Lepidium virginicum -0.0390169044 -0.0054828449 -2.510070e-03
## Phyllanthus polygonoides -0.0454825688 -0.0220583981 7.131828e-02
## Solanum elaeagnifolium -0.1058352560 0.0299135219 -5.385749e-02
## Plantago rhodosperma -0.0280130458 -0.0224505195 -8.354210e-04
## Aphanostephus skirrhobasis -0.0169876445 -0.0192143261 2.221077e-02
## Bubble Plant -0.0461710081 0.0123160599 7.673427e-02
## Englemannia peristenia -0.0312595237 -0.0285984654 -5.356448e-03
## Cirsium texanum -0.0757845078 0.0033306417 -4.116186e-03
## Salvia farinacea -0.0743990982 0.0598330058 1.879490e-02
## Vicia ludoviciana -0.1174370921 0.0550689467 4.567116e-02
## Verbena halei -0.0508436165 -0.0270064273 -3.217313e-02
## Panicum hallii -0.0308667838 -0.0182804311 -5.143976e-02
## Erigeron strigosus -0.0521771063 -0.0360709453 -3.136861e-02
## Glandularia pumilia -0.0464178009 0.0061788931 8.401442e-02
## Vulpia octoflora var. octoflora -0.1126102782 -0.0924783867 4.481357e-02
## Forb C -0.0714685876 -0.0174554652 4.685656e-03
## Thelesperma filifolium -0.0302873136 -0.0553674205 4.485641e-02
## Allium drummondii 0.0281852466 -0.0135476241 1.899163e-02
## Scutellaria drummondii -0.0219596585 -0.0161736753 2.718809e-02
## Tragia ramosa 0.0025665817 -0.0033919249 -2.815604e-02
## Heartshaped leaf forb -0.1105828477 -0.0049644313 5.528349e-02
## Croton dioicus -0.0119434601 -0.0470204167 -6.164192e-03
## Erioneuron pilosum -0.0191631177 -0.0572778357 -5.917303e-02
## Bouteloua curtipendula 0.0088324340 -0.0609783262 -1.429742e-02
## Oxalis stricta 4.3059379153 0.0010046737 6.562854e-05
## Aristida purpurea -0.0008093649 -0.0572489656 2.753347e-02
## Aristolochia coryi -0.0719995931 0.0821624431 7.444738e-03
## Forb D -0.1195872111 -0.0258635657 -2.419329e-02
## Solanum triquetrum -0.1089153790 0.0445476588 3.103858e-02
## Berberis trifoliolata 6.9881100294 0.0011099999 -6.847767e-04
## Vine A 6.9881100294 0.0011099999 -6.847767e-04
## Thymophylla pentachaeta -0.1370007395 0.0165692686 4.175763e-03
## Sphaeralcea angustifolia -0.1405160648 -0.0640078482 5.735894e-02
## Tridens muticus -0.0835330743 -0.0358116635 1.039234e-01
## Opuntia engelmannii -0.0702855610 -0.0554242784 -5.192171e-03
## Daisy A -0.0692213197 -0.0873208932 5.920541e-04
## Gaillardia pinnatifida -0.0586087767 -0.0871671060 6.513062e-02
## Lupinus texensis -0.0618322394 -0.0522142657 1.413371e-02
## Desmanthus velutinus -0.0610898077 -0.1596035811 -1.483386e-02
## Rhynchosida physocalyx -0.0436038039 -0.0655398579 -5.068411e-02
## Hookleaf Unknown -0.1482563794 -0.0829072030 -3.204047e-02
## Sida abutifolia -0.0120177451 0.0369011753 -3.683321e-02
## Tridens muticus var. muticus -0.0777888573 -0.0181699788 3.098096e-02
## Asclepias asperula -0.0811443200 -0.1009852581 5.826205e-02
## Hordeum pusillum 0.0321704283 -0.0246018861 6.232066e-02
## NMDS4
## Glandularia bipinnatifida -0.0180989164
## Nassella leucotricha 0.0069044206
## Astragalus nuttallianus -0.0306597970
## Evax verna 0.0258798400
## Forb A -0.0068668552
## Forb B -0.1027707257
## Heterotheca canescens -0.1027707257
## Limnodea arkansana -0.0438193348
## Medicago minima -0.0507783638
## Not Filaree -0.0251510031
## Senecio douglasii -0.0702465012
## Trilobe -0.0273915228
## Erodium cicutarium 0.0181751571
## Ratibida columnifera -0.0454756304
## Hilaria belangeri -0.0047577109
## Lepidium virginicum 0.0217987717
## Phyllanthus polygonoides 0.0726814588
## Solanum elaeagnifolium 0.0371196317
## Plantago rhodosperma 0.0000263745
## Aphanostephus skirrhobasis -0.0018711446
## Bubble Plant -0.0084261209
## Englemannia peristenia 0.0465228205
## Cirsium texanum 0.0076225811
## Salvia farinacea -0.0326083686
## Vicia ludoviciana -0.0416142371
## Verbena halei 0.0486982158
## Panicum hallii 0.0004790966
## Erigeron strigosus -0.0071770958
## Glandularia pumilia -0.0024610247
## Vulpia octoflora var. octoflora -0.0069920819
## Forb C -0.0158741798
## Thelesperma filifolium 0.0475184643
## Allium drummondii 0.0036207282
## Scutellaria drummondii 0.0483591795
## Tragia ramosa 0.0209880012
## Heartshaped leaf forb 0.0357646496
## Croton dioicus 0.0713948948
## Erioneuron pilosum 0.0241861133
## Bouteloua curtipendula 0.0206296626
## Oxalis stricta 0.0009328266
## Aristida purpurea -0.0157317604
## Aristolochia coryi 0.0599646408
## Forb D 0.0380459886
## Solanum triquetrum 0.0575139064
## Berberis trifoliolata 0.0011082179
## Vine A 0.0011082179
## Thymophylla pentachaeta 0.0260549780
## Sphaeralcea angustifolia -0.0419555232
## Tridens muticus -0.0116144991
## Opuntia engelmannii -0.0080281546
## Daisy A -0.0122580002
## Gaillardia pinnatifida -0.0822239133
## Lupinus texensis 0.0294482885
## Desmanthus velutinus -0.0114075405
## Rhynchosida physocalyx 0.0505198364
## Hookleaf Unknown -0.0048257719
## Sida abutifolia 0.0214677378
## Tridens muticus var. muticus 0.0973194718
## Asclepias asperula -0.0088999286
## Hordeum pusillum 0.0245991375
dat_try2019=
read.csv("cover19.csv")%>%
mutate(DM_Cover=as.numeric(DM_Cover)) %>%
unite("code",c(Plot,Subplot), sep="_") %>%
as_tibble() %>%
select(code,Species,DM_Cover) %>%
pivot_wider(names_from = "Species", values_from = "DM_Cover", values_fill=0) %>%
select (-"Berberis trifoliolata", -"Vine A")
trt_try=
read.csv("trt.csv") %>%
unite("code",c(Plot,Subplot), sep="_") %>%
rename(herb=Herbivory.Tr, fire= Fire.Energy.Tr)
trt2019=
dat_try2019 %>%
inner_join(trt_try) %>%
select(code, herb, fire)
dat2019ord=
read.csv("cover19.csv")%>%
mutate(DM_Cover=as.numeric(DM_Cover)) %>%
unite("code",c(Plot,Subplot), sep="_") %>%
as_tibble() %>%
select(code,Species,DM_Cover) %>%
pivot_wider(names_from = "Species", values_from = "DM_Cover", values_fill=0) %>%
select (-"Oxalis stricta",-"Berberis trifoliolata", -"Vine A") %>%
select(-code)
set.seed(107)
dat19_NMDS=metaMDS(dat2019ord,k=4,trymax =50)
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 9.535341e-05
## Run 1 stress 9.6944e-05
## ... Procrustes: rmse 5.006636e-05 max resid 0.0001864695
## ... Similar to previous best
## Run 2 stress 9.425894e-05
## ... New best solution
## ... Procrustes: rmse 5.579475e-05 max resid 0.0001579969
## ... Similar to previous best
## Run 3 stress 9.563024e-05
## ... Procrustes: rmse 5.165879e-05 max resid 0.00021183
## ... Similar to previous best
## Run 4 stress 9.139673e-05
## ... New best solution
## ... Procrustes: rmse 6.821211e-05 max resid 0.0002597575
## ... Similar to previous best
## Run 5 stress 9.810925e-05
## ... Procrustes: rmse 6.258374e-05 max resid 0.0003013533
## ... Similar to previous best
## Run 6 stress 9.771247e-05
## ... Procrustes: rmse 5.060915e-05 max resid 0.0001735178
## ... Similar to previous best
## Run 7 stress 9.448358e-05
## ... Procrustes: rmse 6.414953e-05 max resid 0.0002350907
## ... Similar to previous best
## Run 8 stress 9.360409e-05
## ... Procrustes: rmse 6.439393e-05 max resid 0.0002363052
## ... Similar to previous best
## Run 9 stress 8.96182e-05
## ... New best solution
## ... Procrustes: rmse 5.1271e-05 max resid 0.00021465
## ... Similar to previous best
## Run 10 stress 9.244088e-05
## ... Procrustes: rmse 4.396544e-05 max resid 0.0001188084
## ... Similar to previous best
## Run 11 stress 9.762485e-05
## ... Procrustes: rmse 5.589524e-05 max resid 0.0001884853
## ... Similar to previous best
## Run 12 stress 9.615139e-05
## ... Procrustes: rmse 4.688005e-05 max resid 0.000189407
## ... Similar to previous best
## Run 13 stress 9.433328e-05
## ... Procrustes: rmse 5.952162e-05 max resid 0.0002410461
## ... Similar to previous best
## Run 14 stress 9.885821e-05
## ... Procrustes: rmse 5.238678e-05 max resid 0.0001152497
## ... Similar to previous best
## Run 15 stress 9.47201e-05
## ... Procrustes: rmse 6.928104e-05 max resid 0.0003121808
## ... Similar to previous best
## Run 16 stress 9.957897e-05
## ... Procrustes: rmse 6.09325e-05 max resid 0.0002633361
## ... Similar to previous best
## Run 17 stress 9.798843e-05
## ... Procrustes: rmse 5.763908e-05 max resid 0.0001936714
## ... Similar to previous best
## Run 18 stress 8.937595e-05
## ... New best solution
## ... Procrustes: rmse 4.779514e-05 max resid 0.0002390866
## ... Similar to previous best
## Run 19 stress 9.289449e-05
## ... Procrustes: rmse 5.218835e-05 max resid 0.000154491
## ... Similar to previous best
## Run 20 stress 9.817146e-05
## ... Procrustes: rmse 6.56489e-05 max resid 0.0002214528
## ... Similar to previous best
## *** Solution reached
dat19_NMDS
##
## Call:
## metaMDS(comm = dat2019ord, k = 4, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(dat2019ord))
## Distance: bray
##
## Dimensions: 4
## Stress: 8.937595e-05
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(dat2019ord))'
plot(dat19_NMDS)
site.scores <- as.data.frame(scores(dat19_NMDS, "site"))
site.scores
## NMDS1 NMDS2 NMDS3 NMDS4
## 1 -117.7073 6.556532e-01 -1.361906e-02 -0.0523849292
## 2 -117.6709 9.856176e-01 -5.337472e-01 0.5168959843
## 3 -117.9580 1.896046e-01 -6.353395e-01 -0.2930927518
## 4 -117.8962 -5.084163e-01 3.136437e-01 0.7234411258
## 5 -118.1302 6.088370e-01 -1.997626e-01 0.8756484871
## 6 -117.7541 6.175670e-01 -4.630523e-01 0.3498528348
## 7 -117.9262 2.771208e-02 -3.576759e-01 0.0145436685
## 8 -118.0720 3.650050e-01 1.679943e-01 0.0551645224
## 9 -118.1656 7.103654e-01 2.423595e-01 -0.4143273599
## 10 -117.4032 -2.823815e-01 3.759963e-01 -0.9792952571
## 11 -117.8800 -1.360676e-01 -5.280182e-01 0.7167688300
## 12 -118.2522 3.045068e-01 2.140272e-01 0.8127857009
## 13 -117.5477 -1.141210e-01 6.198264e-01 -0.1114002420
## 14 -117.6709 9.856056e-01 -5.337297e-01 0.5169143791
## 15 -117.6267 -5.932115e-02 -2.606129e-01 -0.0610135702
## 16 -117.9337 -1.086650e-01 -5.939083e-01 -0.0417607256
## 17 -117.8806 2.832537e-01 2.972037e-01 0.3794964587
## 18 -118.0717 6.744728e-01 3.004219e-01 0.0318983385
## 19 -118.7601 2.427477e-01 1.000176e-01 -0.3522485224
## 20 -118.1112 2.965700e-01 2.542873e-01 0.9211522895
## 21 -117.6282 -3.949443e-01 5.722493e-02 0.2916645618
## 22 -118.0751 3.971005e-01 6.882970e-01 0.2099046785
## 23 -118.5639 1.686779e-01 6.652202e-01 0.7682263530
## 24 -118.0355 4.739921e-01 2.441574e-01 0.3887232466
## 25 -117.5233 2.965115e-01 6.033362e-01 -0.5092640762
## 26 -118.6393 -1.888228e+00 1.621254e+00 0.1999957317
## 27 -118.2930 -5.359474e-01 3.706293e-01 -0.6628904144
## 28 -118.7295 3.260209e-01 2.105900e-01 0.6267190918
## 29 -117.8281 3.273445e-02 -1.037634e+00 0.1712177654
## 30 -118.4594 1.057517e-01 -2.231097e-01 -0.5571679087
## 31 -117.7829 1.074510e-01 3.089605e-01 -0.0595204361
## 32 -118.1227 -1.321693e-02 -4.207964e-01 0.7081187523
## 33 -117.6369 -2.489404e-01 4.221246e-01 -0.6159078306
## 34 -118.3669 4.299969e-01 -2.402841e-01 -0.1266357258
## 35 -118.0803 -3.915009e-01 -5.237731e-01 -0.3424753084
## 36 -117.9685 4.389050e-01 3.808000e-01 -0.6054329421
## 37 -117.8015 -1.765282e-01 -3.320743e-01 -0.1974519942
## 38 -118.4286 -1.247441e-01 1.137552e-01 0.1014257113
## 39 -119.0607 9.784350e-01 -7.039962e-01 0.4405162175
## 40 -118.6515 1.996468e-01 7.523292e-01 -0.1481950276
## 41 -119.0496 -4.588911e-01 1.580383e-01 0.1150319967
## 42 -118.1632 -6.849038e-01 2.770679e-02 -0.4838552714
## 43 -118.2410 2.848245e-01 -8.986359e-02 -0.2090161810
## 44 -118.6554 -7.766769e-02 -5.290826e-02 -0.4089217463
## 45 -117.7465 2.657202e-01 -2.635336e-01 0.0300102065
## 46 -118.6979 4.921207e-02 -5.125958e-01 -0.1734214025
## 47 -117.5637 2.779636e-01 1.023492e-01 -0.4793681414
## 48 -117.8884 -7.176946e-02 6.113807e-02 0.3445765397
## 49 -117.6089 3.479466e-01 -5.193101e-02 -0.2868498149
## 50 -117.6576 5.490088e-01 1.522202e-01 -0.1754953134
## 51 -118.3661 -2.483516e-01 -7.786385e-01 0.0446232510
## 52 -118.3611 -3.437406e-01 -4.505305e-01 -0.6004718509
## 53 -117.7650 -3.598582e-01 -4.609705e-01 -0.1926077813
## 54 -117.9922 5.412454e-01 7.317756e-03 0.4211858831
## 55 -118.1905 1.300945e-01 -2.386525e-01 0.0318742175
## 56 -118.0414 6.102588e-01 -1.810224e-01 0.0118610662
## 57 -117.4622 1.963832e-02 3.007748e-01 0.2217360112
## 58 -117.1471 -1.566044e-01 -1.061683e+00 0.1616290328
## 59 -117.6735 -6.498212e-01 -2.200926e-01 0.2256966624
## 60 -117.6250 3.486193e-01 -7.968581e-02 -0.2008042198
## 61 -117.9003 1.019214e-02 -5.273067e-02 0.3344563952
## 62 -117.8020 7.598315e-02 4.154988e-01 0.1756607126
## 63 -118.3835 -4.075936e-01 -1.885326e-01 -0.2609802450
## 64 -117.8380 -9.778153e-02 -5.377643e-01 0.4039079365
## 65 -117.7327 4.457429e-01 -1.510657e-01 -0.5058625801
## 66 -117.8607 4.025070e-01 9.091101e-02 -0.4095293616
## 67 -119.1027 1.332685e+00 -2.675279e-01 -0.4254457617
## 68 -118.4075 1.338223e-01 2.665421e-02 0.9024670976
## 69 -118.6375 1.604336e-01 -2.727241e-01 -0.2649502776
## 70 -119.1187 -2.324387e-01 -1.810601e-01 0.2776798472
## 71 -118.6102 -1.500348e-01 -3.235656e-01 -0.0237713631
## 72 -117.8811 8.534490e-02 1.590390e-01 -0.6377083558
## 73 -117.9766 -4.317579e-02 -5.601367e-01 0.1646001390
## 74 -118.7819 -1.419604e-01 -5.157908e-01 -0.0879797494
## 75 -118.0800 -5.541574e-01 1.284923e-01 -0.1578270128
## 76 -117.6834 5.896370e-01 1.413943e-01 -0.3323317016
## 77 -117.8400 -3.732795e-01 3.074376e-01 0.3899956522
## 78 -117.8073 -6.039397e-01 -2.320453e-01 0.6769609238
## 79 -117.7073 6.556532e-01 -1.361906e-02 -0.0523849293
## 80 16653.6641 2.982353e-05 7.392663e-05 0.0003053001
## 81 -119.5140 5.228969e-01 -2.831994e-01 0.5938645559
## 82 -118.7066 4.903959e-01 -5.317008e-01 0.1846560381
## 83 -119.2418 8.964844e-01 -3.561249e-01 0.4005897024
## 84 -117.9897 -5.602141e-01 -4.552393e-01 -0.1087335906
## 85 -118.5500 -7.748859e-01 -5.594665e-01 -0.4357165175
## 86 -119.5506 2.223241e-01 5.097199e-01 1.0059858219
## 87 -117.9101 6.973262e-01 1.333978e-02 0.1492800945
## 88 -118.6236 -7.217921e-01 9.516854e-01 0.8330948895
## 89 -119.3365 9.137531e-01 1.842011e-01 0.5145455941
## 90 -117.8218 -4.303430e-01 9.561849e-01 0.1150012432
## 91 -117.9461 5.450363e-01 7.554290e-01 -0.4242765593
## 92 -117.9300 -1.751647e-01 -2.353156e-03 0.7194088185
## 93 -117.7737 2.352938e-01 -3.212329e-01 0.1923251854
## 94 -117.5066 3.051612e-01 2.587562e-02 -0.4924389134
## 95 -118.4620 -3.557378e-01 1.443771e-01 0.2449764133
## 96 -118.4070 7.449834e-01 2.651688e-01 -0.2708248594
## 97 -118.4583 1.725770e+00 1.800519e+00 -0.1707233792
## 98 -118.3056 -2.891438e-03 8.415205e-01 0.2215820183
## 99 -117.7332 -6.789769e-01 7.609322e-01 0.2655609166
## 100 -117.6482 1.946343e-01 -2.208238e-01 -0.0297230621
## 101 -118.4539 -7.218802e-01 -1.060260e-01 0.2279363047
## 102 -117.7054 -2.440296e-02 -6.689125e-02 0.2288079460
## 103 -118.3102 2.083139e-01 8.231624e-02 -0.5216568871
## 104 -117.5478 -1.120652e+00 4.586660e-01 0.9383011058
## 105 -117.7287 -5.258753e-01 3.535247e-01 -0.4421201552
## 106 -118.4802 -3.885885e-01 2.162247e-01 -0.1210103934
## 107 -118.2925 -1.635082e+00 1.469285e-01 -0.1490104344
## 108 -118.5199 -7.721624e-01 -1.835730e-01 -0.1971450077
## 109 -118.0212 -1.941893e-01 7.237381e-01 -0.2903452415
## 110 -118.4624 -2.620899e-01 5.933232e-02 -0.2445598645
## 111 -118.3277 -7.573656e-01 -4.993072e-01 -0.2170377149
## 112 -118.4026 -8.085312e-02 -4.991090e-01 0.0831681632
## 113 -118.0439 -6.566452e-01 -1.337878e-01 -0.7727172415
## 114 -117.7491 -1.323894e+00 -5.222171e-01 0.2337140086
## 115 -118.9907 -6.730617e-01 1.755067e-01 1.1683206628
## 116 -117.4316 5.732916e-01 -3.632194e-01 0.3856126057
## 117 -118.0018 -8.158552e-01 -5.005808e-01 0.2238314633
## 118 -118.3641 -6.953844e-01 -4.999198e-01 -0.1046579128
## 119 -118.6211 7.668745e-02 1.180844e-01 -0.2980289761
## 120 -117.8051 5.963994e-01 4.517388e-01 -0.5130261230
## 121 -118.7923 1.764175e-03 4.035534e-01 -0.4424525135
## 122 -118.8095 -3.789165e-01 -4.250229e-01 -0.2790915387
## 123 -118.2591 -5.447218e-01 -8.961302e-02 -0.0308351825
## 124 -117.9083 9.043778e-02 5.111276e-01 -0.3433992113
## 125 -118.3503 -4.332527e-02 -1.731016e-01 -0.5177990430
## 126 -118.1244 1.892130e-01 5.692033e-01 -0.1615467944
## 127 -117.8138 -7.001100e-01 -3.267167e-01 -0.2973495154
## 128 -118.8581 -7.647986e-01 -3.817533e-01 0.0345231670
## 129 -118.3111 -9.143623e-01 6.456220e-01 0.2880352724
## 130 -117.7995 -2.303651e-01 -1.050504e-01 -0.4453445580
## 131 -117.6941 1.518348e-01 -1.373308e-01 -0.1634702244
## 132 -117.8629 5.001015e-01 -8.274931e-03 -0.1589480884
## 133 -117.5859 3.174546e-01 -2.316479e-01 0.1129138876
## 134 -117.9049 2.472909e-01 3.350946e-01 -0.6528113777
## 135 -117.4329 -6.874340e-01 -2.264201e-01 -0.5446657179
## 136 -117.3888 -5.444808e-01 -2.975244e-01 -0.2502560399
## 137 -118.1526 1.264421e-02 -3.073114e-01 -0.8326349922
## 138 -117.7706 2.299801e-01 -1.610736e-01 -0.1083993816
## 139 -117.7128 6.443032e-01 9.591906e-03 -0.0989521945
## 140 -117.9278 5.063407e-01 1.388843e-01 -0.3451959372
## 141 -117.8534 -5.366321e-01 5.260391e-01 -0.0534146649
## 142 -117.5333 2.727053e-01 -2.222150e-01 -0.0910015870
dat_try2019=
read.csv("cover19.csv")%>%
mutate(DM_Cover=as.numeric(DM_Cover)) %>%
unite("code",c(Plot,Subplot), sep="_") %>%
as_tibble() %>%
select(code,Species,DM_Cover) %>%
pivot_wider(names_from = "Species", values_from = "DM_Cover", values_fill=0) %>%
select (-"Berberis trifoliolata", -"Vine A") %>%
filter(code !="41_3")
trt_try=
read.csv("trt.csv") %>%
unite("code",c(Plot,Subplot), sep="_") %>%
rename(herb=Herbivory.Tr, fire= Fire.Energy.Tr)
trt2019=
dat_try2019 %>%
inner_join(trt_try) %>%
select(code, herb, fire)
dat2019ord=
read.csv("cover19.csv")%>%
mutate(DM_Cover=as.numeric(DM_Cover)) %>%
unite("code",c(Plot,Subplot), sep="_") %>%
as_tibble() %>%
select(code,Species,DM_Cover) %>%
pivot_wider(names_from = "Species", values_from = "DM_Cover", values_fill=0) %>%
select (-"Oxalis stricta",-"Berberis trifoliolata", -"Vine A") %>%
filter(code !="41_3")%>%
select(-code)
set.seed(101)
dat19_NMDS=metaMDS(dat2019ord,k=4,trymax =1000)
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1301534
## Run 1 stress 0.1300628
## ... New best solution
## ... Procrustes: rmse 0.01661139 max resid 0.1074907
## Run 2 stress 0.1300739
## ... Procrustes: rmse 0.009724374 max resid 0.0568385
## Run 3 stress 0.1343117
## Run 4 stress 0.1305389
## ... Procrustes: rmse 0.01829743 max resid 0.07550609
## Run 5 stress 0.1308982
## Run 6 stress 0.1301583
## ... Procrustes: rmse 0.01335899 max resid 0.08099544
## Run 7 stress 0.1300954
## ... Procrustes: rmse 0.003342052 max resid 0.02742332
## Run 8 stress 0.13008
## ... Procrustes: rmse 0.0137003 max resid 0.0698675
## Run 9 stress 0.1310645
## Run 10 stress 0.1328464
## Run 11 stress 0.13005
## ... New best solution
## ... Procrustes: rmse 0.001849439 max resid 0.01147041
## Run 12 stress 0.1301131
## ... Procrustes: rmse 0.00640712 max resid 0.03489886
## Run 13 stress 0.1312104
## Run 14 stress 0.1300683
## ... Procrustes: rmse 0.002835654 max resid 0.01250772
## Run 15 stress 0.1301051
## ... Procrustes: rmse 0.009172482 max resid 0.06723056
## Run 16 stress 0.1300595
## ... Procrustes: rmse 0.001450472 max resid 0.009475022
## ... Similar to previous best
## Run 17 stress 0.1324438
## Run 18 stress 0.1319723
## Run 19 stress 0.1300665
## ... Procrustes: rmse 0.009345564 max resid 0.0561176
## Run 20 stress 0.130069
## ... Procrustes: rmse 0.01099618 max resid 0.05606138
## *** Solution reached
dat19_NMDS
##
## Call:
## metaMDS(comm = dat2019ord, k = 4, trymax = 1000)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(dat2019ord))
## Distance: bray
##
## Dimensions: 4
## Stress: 0.13005
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(dat2019ord))'
plot(dat19_NMDS)
