SI and NIR SRM
Jonathan, Megan, and Devan
January 29, 2021
HRECSICalc <- HRECSICalc %>%
mutate(TDNg= 98.625-(1.048*ADF)) %>%
mutate(TDNm= 92.62-(0.9093*ADF)) %>%
mutate(TDNc=82.14-(0.577*ADF))
NIRpatches <- HRECSICalc %>%
gather(Moisture,Powell:CP, TDNg, TDNm, TDNc, key="species", value="cover") %>%
mutate(cover=as.numeric(cover)) %>%
group_by(Year, TSF, Treatment, Location, Patch, Month, ESD, species) %>%
dplyr::summarise(Cover=round(mean(cover),5)) %>%
ungroup() %>%
spread(species,Cover)
#Moisture and Biomass as env
NIREnv1 <- NIRpatches %>% drop_na() %>%
select(Year, Location,Treatment,ESD,TSF, Month, Moisture, Powell, KgHa)
#Makes a Year*Location column to use as a strata, should also include month?
NIREnv_2 <-
NIREnv1 %>%
unite("YearLoc", c("Year", "Location"), remove = F ) %>%
as_tibble()
NIRsp1 <- NIRpatches %>% drop_na() %>% select(ADF,NDF, ADL, CP)Capscale without TDN
The biggest difference between metaMDS and capscale is that the species scores are scaled up by 6.153314. The rest all seems the same mostly!
NIR.cap <- capscale(NIRsp1 ~ 1, metaMDSdist = "true", dist="euclidean")## Square root transformation
## Wisconsin double standardizationsummary(NIR.cap)##
## Call:
## capscale(formula = NIRsp1 ~ 1, distance = "euclidean", metaMDSdist = "true")
##
## Partitioning of squared Euclidean distance:
## Inertia Proportion
## Total 1.671 1
## Unconstrained 1.671 1
##
## Eigenvalues, and their contribution to the squared Euclidean distance
##
## Importance of components:
## MDS1 MDS2 MDS3
## Eigenvalue 1.1887 0.4688 0.013360
## Proportion Explained 0.7114 0.2806 0.007996
## Cumulative Proportion 0.7114 0.9920 1.000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 6.153314
##
##
## Species scores
##
## MDS1 MDS2 MDS3
## ADF -0.6232 -1.583 0.38891
## NDF -0.3503 -1.618 -0.38865
## ADL -3.1155 2.033 -0.01489
## CP 4.0890 1.169 0.01464
##
##
## Site scores (weighted sums of species scores)
##
## MDS1 MDS2 MDS3
## 1 -2.236e-01 -1.246e-01 -0.4164073
## 2 -7.706e-02 3.339e-02 -0.4198260
## 3 -1.037e-01 1.129e-01 -0.1947988
## 4 -4.608e-02 1.750e-01 -0.2072061
## 5 -1.271e-01 8.721e-02 -0.0353294
## 6 -4.774e-02 1.531e-01 -0.0174974
## 7 5.224e-03 3.412e-01 0.1126262
## 8 -9.771e-02 5.144e-02 -0.1191821
## 9 -3.196e-02 1.224e-01 -0.2943249
## 10 1.226e-01 5.154e-01 0.0411917
## 11 -6.237e-02 6.642e-02 -0.3558116
## 12 -9.035e-02 3.395e-01 0.3194418
## 13 -3.828e-02 4.846e-01 -0.0365944
## 14 -1.271e-01 1.231e-01 -0.4234410
## 15 7.094e-02 7.187e-01 0.7474770
## 16 -4.411e-02 1.986e-01 -0.1427354
## 17 -1.795e-01 -1.711e-01 -0.3610807
## 18 -1.886e-01 -3.283e-03 -0.3352291
## 19 2.127e-03 3.495e-02 -0.4118075
## 20 -1.363e-01 1.165e-01 -0.1992737
## 21 -1.988e-01 2.212e-02 -0.1633965
## 22 -1.446e-01 5.396e-02 -0.1754413
## 23 -9.130e-02 9.632e-02 -0.5602382
## 24 -1.363e-01 1.798e-01 -0.1828769
## 25 -2.394e-01 9.708e-02 -0.3001162
## 26 -1.088e-01 1.399e-01 0.0268144
## 27 -1.076e-01 1.012e-01 -0.1657984
## 28 -1.116e-01 9.044e-02 -0.3355178
## 29 -7.328e-02 7.379e-02 -0.0878305
## 30 -2.349e-01 3.943e-02 -0.0642004
## 31 -1.976e-01 -1.103e-02 -0.4412891
## 32 -2.138e-01 4.702e-02 0.0942609
## 33 -2.007e-01 9.974e-02 0.0134121
## 34 -1.981e-01 -1.320e-02 -0.0284164
## 35 -1.502e-01 1.117e-01 0.0475654
## 36 -1.494e-01 8.378e-02 -0.1322544
## 37 -7.903e-02 1.204e-01 -0.1670475
## 38 -1.096e-01 5.332e-02 -0.1292983
## 39 -9.604e-02 2.316e-01 0.1831487
## 40 -1.539e-02 3.613e-01 0.1586437
## 41 -1.750e-01 4.658e-02 -0.1684654
## 42 -1.460e-01 1.827e-01 -0.0320104
## 43 -1.911e-01 6.121e-03 -0.2466706
## 44 9.532e-02 3.709e-01 0.0861443
## 45 -1.134e-01 9.305e-02 0.3349756
## 46 -8.916e-02 1.358e-01 -0.0718015
## 47 -4.132e-02 2.063e-01 -0.1087750
## 48 -1.008e-01 1.749e-01 0.0242919
## 49 -1.811e-02 1.970e-01 -0.3459455
## 50 4.808e-02 4.002e-01 -0.1286775
## 51 -1.370e-02 2.489e-01 -0.1417083
## 52 -1.118e-01 1.966e-01 0.3404955
## 53 8.931e-02 4.644e-01 0.2156648
## 54 2.538e-03 2.590e-01 0.1493365
## 55 -1.011e-01 2.386e-01 -0.0917466
## 56 2.366e-02 1.446e-01 -0.3969852
## 57 -1.496e-01 -1.107e-02 0.0733509
## 58 -1.258e-01 1.509e-01 -0.2154179
## 59 -6.293e-02 9.132e-02 -0.2215800
## 60 -6.630e-02 1.136e-01 0.0915918
## 61 3.420e-02 3.081e-01 0.1041611
## 62 -5.834e-02 2.439e-01 0.0010802
## 63 -1.700e-01 3.804e-02 0.1899089
## 64 -1.756e-01 -2.378e-02 -0.0608770
## 65 -1.065e-01 -1.337e-02 -0.1506471
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## 84 -1.639e-01 4.239e-01 0.5151997
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## 90 -3.143e-02 3.053e-01 -0.0093692
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## 92 5.176e-03 4.066e-01 0.2333684
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## 682 3.742e-06 -2.631e-02 -0.0624567
## 683 -2.809e-02 8.039e-02 0.0683077
## 684 -4.576e-02 1.884e-02 -0.2229406
## 685 -1.696e-01 -1.015e-01 -0.1398993
## 686 -1.622e-01 6.543e-02 -0.3199119
## 687 -2.021e-01 -8.137e-02 -0.0997160
## 688 -1.273e-01 1.106e-01 -0.4012582
## 689 -5.685e-02 -1.387e-03 0.4057624
## 690 1.589e-01 2.496e-01 0.4289850
## 691 -9.710e-03 8.376e-02 0.4151987
## 692 1.574e-02 1.470e-01 0.3872198
## 693 6.138e-03 2.147e-01 0.3742108
## 694 -5.672e-02 1.475e-01 0.4529406
## 695 -1.383e-01 2.082e-01 0.1127352
## 696 -1.115e-01 1.572e-01 0.2299767
## 697 -1.261e-01 1.708e-01 0.1758073
## 698 -1.463e-01 1.337e-01 0.1757171
## 699 -2.258e-02 2.613e-01 0.0910656
## 700 -2.071e-01 1.328e-01 0.3517424
## 701 7.272e-02 5.044e-02 0.0304441
## 702 -1.020e-01 -8.511e-02 0.3296237
## 703 -1.988e-01 -2.373e-01 0.3736350
## 704 -4.964e-02 -1.008e-01 0.3218096
## 705 -9.042e-02 -1.297e-01 0.2364181
## 706 -1.303e-01 -1.845e-01 0.3334305
## 707 -2.084e-01 -1.441e-01 0.2492092
## 708 -2.319e-01 -1.128e-01 0.2807571
## 709 -2.080e-01 -1.754e-01 0.2048817
## 710 -2.673e-01 -1.056e-01 0.1905918
## 711 -3.188e-01 -1.134e-01 0.0558696
## 712 -3.162e-01 -6.490e-02 0.1011874
## 713 -1.998e-01 3.467e-02 -0.1813168
## 714 -2.650e-01 5.617e-03 -0.1800651
## 715 -2.928e-01 -8.556e-02 -0.0759179
## 716 -4.383e-02 2.005e-02 0.2432546
## 717 7.722e-02 -1.844e-02 0.1233344
## 718 3.745e-02 2.604e-02 0.1552497
## 719 8.571e-02 -3.724e-02 -0.1122630
## 720 3.757e-02 -2.409e-02 -0.1163167
## 721 -1.899e-01 -7.814e-02 -0.1654233
## 722 -1.764e-01 1.159e-02 0.0342613
## 723 -2.206e-01 -5.415e-02 -0.1576989
## 724 -2.912e-01 -1.251e-02 -0.0335796
## 725 -2.240e-01 -2.659e-02 -0.0570848
## 726 3.994e-02 -1.300e-01 0.1949461
## 727 3.389e-02 -1.089e-01 0.1514222
## 728 9.606e-02 -5.035e-02 -0.0080775
## 729 1.424e-01 -1.221e-01 -0.0450665
## 730 -7.730e-02 -1.137e-01 0.0284055
## 731 -1.154e-01 -7.622e-02 -0.0178827
## 732 -2.244e-01 -5.750e-02 -0.0539349
## 733 -2.426e-01 -1.132e-01 -0.1094226
## 734 -2.332e-01 -4.362e-02 -0.1175519
## 735 -1.786e-01 -2.452e-02 -0.2547438
## 736 1.445e-01 2.412e-01 0.0968203
## 737 1.723e-01 2.713e-01 -0.3886514
## 738 1.599e-01 2.569e-01 0.5509878
## 739 8.144e-03 1.919e-01 0.1294478
## 740 1.749e-02 2.831e-01 -0.1085918
## 741 1.282e-01 2.702e-01 0.2644550
## 742 -2.112e-01 8.027e-02 -0.1516690
## 743 -1.608e-01 2.710e-02 -0.3846495
## 744 -1.961e-01 -8.407e-02 0.0723022
## 745 -6.564e-02 1.591e-01 0.0240451
## 746 -2.043e-01 9.544e-02 -0.3259766
## 747 -2.445e-01 6.575e-02 0.1357035
## 748 -4.292e-02 2.048e-02 0.2095170
## 749 5.240e-02 -9.258e-02 0.0528884
## 750 2.923e-02 -7.760e-02 0.0221350
## 751 -1.751e-01 -6.258e-02 0.0834047
## 752 -7.482e-02 7.826e-03 0.1719807
## 753 -8.973e-02 -7.655e-02 0.2885069
## 754 -2.605e-01 -6.971e-02 0.0014228
## 755 -1.978e-01 -1.043e-01 0.0268763
## 756 -3.075e-01 -7.028e-02 0.1361462
## 757 -2.609e-01 6.759e-03 0.1179335
## 758 -2.760e-01 -1.168e-01 0.0425619
## 759 -3.363e-01 -5.073e-02 0.0711857
## 760 2.482e-02 -1.124e-01 -0.0094781
## 761 -2.115e-02 -1.318e-01 0.1018335
## 762 -7.193e-03 -1.162e-01 -0.0287932
## 763 -1.432e-01 -4.658e-03 0.1437112
## 764 -1.445e-01 -9.118e-02 0.1624263
## 765 -1.867e-01 -1.391e-01 0.1362930
## 766 -1.852e-01 -1.776e-02 0.0143498
## 767 -2.232e-01 -7.483e-03 0.0790373
## 768 -2.233e-01 4.771e-02 0.3018547
## 769 -2.603e-02 1.103e-01 -0.2095628
## 770 -2.796e-01 3.326e-02 0.0269251
## 771 -4.481e-01 -6.220e-03 0.0085403
## 772 -2.378e-02 -7.567e-02 0.3794473
## 773 -3.302e-02 3.793e-02 0.0657645
## 774 -7.451e-03 -1.066e-01 0.2398447
## 775 -1.901e-01 -1.906e-01 0.3154031
## 776 -1.791e-01 -1.420e-01 0.1786819
## 777 -2.909e-01 -5.375e-02 0.1992307
## 778 -2.446e-01 -6.519e-02 -0.0671201
## 779 -2.480e-01 2.668e-02 -0.0302842
## 780 -2.646e-01 -2.063e-02 -0.2380728
## 781 -2.903e-01 8.383e-02 -0.3103680
## 782 -1.296e-01 -1.808e-02 0.2150584
## 783 -4.517e-02 -1.062e-01 0.1663041
## 784 -4.055e-02 -7.301e-02 0.0088024
## 785 -6.736e-02 -8.856e-02 0.2278167
## 786 -9.207e-02 -1.143e-01 0.0399388
## 787 -1.433e-01 -1.187e-01 0.0068884
## 788 -2.181e-01 -1.001e-01 0.0194728
## 789 -2.062e-01 -5.731e-02 0.0786075
## 790 -2.781e-01 -1.082e-02 -0.0246109
## 791 -2.380e-01 1.620e-02 -0.0230208
## 792 7.616e-03 -1.352e-01 0.1328720
## 793 8.302e-02 -3.152e-02 0.1089179
## 794 5.382e-02 -7.887e-02 -0.0111082
## 795 -2.617e-02 -1.061e-01 0.1269857
## 796 -1.725e-01 -2.008e-01 -0.0098904
## 797 -2.150e-01 -1.556e-01 0.0134056
## 798 -2.429e-01 -1.143e-01 -0.0488507
## 799 -2.428e-01 -9.157e-02 -0.0848932
## 800 -2.689e-01 -1.138e-01 -0.1571121
## 801 -2.945e-01 -1.001e-01 -0.1034686
## 802 9.996e-02 -6.329e-02 0.1578653
## 803 3.326e-02 1.204e-02 0.1190869
## 804 3.034e-02 4.928e-02 -0.1031833
## 805 1.307e-01 3.505e-01 0.4621512
## 806 -1.999e-01 6.163e-03 0.0320845
## 807 -2.118e-01 -1.448e-03 0.0280560
## 808 -2.749e-01 -2.342e-03 -0.0787239
## 809 -2.535e-01 1.054e-01 -0.0289333
## 810 1.733e-01 1.737e-01 0.1831189
## 811 -9.762e-02 -1.070e-01 0.4502234
## 812 1.274e-01 1.208e-01 0.0222265
## 813 -1.017e-01 6.325e-02 -0.1857095
## 814 -1.606e-01 -1.715e-01 0.1661144
## 815 -4.704e-02 -2.168e-02 -0.0779299
## 816 -2.521e-01 -3.396e-02 -0.1926390
## 817 -2.548e-01 -1.027e-01 0.1452615
## 818 -2.490e-01 -5.117e-02 -0.2222808
## 819 -3.390e-01 -5.368e-02 -0.0110731
## 820 -3.406e-01 -1.650e-02 0.1681660
## 821 3.849e-02 1.345e-01 -0.0557924
## 822 -6.521e-02 -4.791e-02 0.2183143
## 823 5.004e-02 -8.692e-02 0.0449448
## 824 -1.657e-01 -2.147e-02 0.1622535
## 825 -1.348e-01 -1.482e-01 0.0340025
## 826 -2.590e-01 1.989e-02 0.0424058
## 827 -3.000e-01 -9.226e-02 -0.0039884
## 828 -2.815e-01 2.851e-02 0.1068237
## 829 -2.674e-01 -3.089e-02 -0.0800109
## 830 -5.195e-02 -1.667e-01 0.3809179
## 831 -3.072e-02 -1.450e-02 0.4048209
## 832 1.852e-03 -1.054e-01 0.3548416
## 833 2.712e-02 -6.871e-02 0.2750121
## 834 -1.898e-01 -1.326e-01 0.1617377
## 835 -2.338e-01 -1.276e-01 0.1115851
## 836 -2.094e-01 -8.149e-02 0.0162721
## 837 -2.603e-01 -4.816e-02 -0.2198608
## 838 -2.573e-01 -1.317e-02 -0.2489923
## 839 -2.000e-01 7.539e-02 -0.4099305
## 840 3.984e-02 -6.223e-03 0.3320262
## 841 -7.650e-02 -1.149e-01 0.3949405
## 842 8.137e-02 -1.081e-01 0.2537980
## 843 -3.783e-02 -1.477e-01 0.2582920
## 844 -2.266e-02 -4.518e-02 -0.2104499
## 845 -4.494e-02 -7.958e-02 -0.0353597
## 846 -2.314e-01 -7.667e-02 0.0093186
## 847 -2.543e-01 -8.993e-02 -0.1302235
## 848 -2.707e-01 -1.308e-01 0.1224205
## 849 -2.833e-01 -8.295e-02 0.0080091
## 850 -5.136e-02 -1.596e-01 0.3400068
## 851 -3.414e-02 -8.951e-02 0.3425047
## 852 4.915e-02 -1.249e-01 -0.0314761
## 853 1.126e-01 -5.189e-02 -0.1641926
## 854 -2.026e-01 -1.968e-01 0.1829542
## 855 -8.863e-02 -9.487e-02 -0.1681757
## 856 -3.206e-01 -1.376e-01 -0.0570196
## 857 -2.519e-01 -4.691e-02 -0.2082107
## 858 -3.184e-01 -6.450e-02 -0.1483042
## 859 -2.402e-01 -2.463e-02 -0.1983089plot(NIR.cap, main="Capscale Euclidean")
NIRcapE <- envfit(NIR.cap ~ Treatment + TSF + ESD + Month + Moisture + Powell + KgHa, data = NIREnv_2, choices = c(1:2), perm=499, strata=NIREnv_2$YearLoc)
NIRcapE##
## ***VECTORS
##
## MDS1 MDS2 r2 Pr(>r)
## Moisture 0.96831 -0.24976 0.6242 0.002 **
## Powell 0.88479 0.46600 0.1247 0.002 **
## KgHa -0.90474 -0.42597 0.1997 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499
##
## ***FACTORS:
##
## Centroids:
## MDS1 MDS2
## TreatmentCattle 0.0224 0.0541
## TreatmentSheep -0.0263 -0.0636
## TSFRB 0.1118 0.0316
## TSF1to2 -0.0050 -0.0606
## TSF2to3 -0.0727 -0.1028
## TSF3plus -0.1280 -0.0439
## TSFNYB -0.0248 0.0628
## ESDClayey 0.0055 0.0239
## ESDLoamy 0.0152 -0.0203
## ESDSaline Lowland -0.0221 -0.0123
## ESDSandy 0.0102 0.0060
## ESDThin Claypan -0.0179 -0.0825
## MonthMay 0.0773 -0.0022
## MonthJune 0.2544 -0.0574
## MonthJuly 0.0102 -0.0343
## MonthAugust -0.0933 0.0059
## MonthSeptember -0.1189 0.0721
##
## Goodness of fit:
## r2 Pr(>r)
## Treatment 0.0457 1.000
## TSF 0.1033 0.002 **
## ESD 0.0064 0.518
## Month 0.2389 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499#Vectors
NIRcapV <- envfit(NIR.cap ~ Moisture + Powell + KgHa, data = NIREnv_2,
choices = c(1:2), perm=499, strata=NIREnv_2$YearLoc)
plot(NIR.cap, main="Vector fitting")
text(NIR.cap, display = "species", col="red")
plot(NIRcapV)
NIRcapTSF <- envfit(NIR.cap ~ TSF, data = NIREnv_2,
choices = c(1:2), perm=499, strata=NIREnv_2$YearLoc)
plot(NIR.cap, main="TSF", display="sites")
ordispider(NIR.cap, NIREnv_2$TSF, label=TRUE)
pairwise.factorfit(NIR.cap, fac=NIREnv_2$TSF, perm = 499, strata=NIREnv_2$YearLoc)##
## Pairwise comparisons using factor fitting to an ordination
##
## data: NIR.cap by NIREnv_2$TSF
## 999 permutations
##
## RB 1to2 2to3 3plus
## 1to2 0.0012 - - -
## 2to3 0.0012 0.0033 - -
## 3plus 0.0012 0.0012 0.0150 -
## NYB 0.0012 0.0012 0.0012 0.0012
##
## P value adjustment method: fdrNIRcapM <- envfit(NIR.cap ~ Month, data = NIREnv_2,
choices = c(1:3), perm=499, strata=NIREnv_2$YearLoc)
plot(NIR.cap, main="Month", display="sites")
ordispider(NIR.cap, NIREnv_2$Month, label=TRUE)
pairwise.factorfit(NIR.cap, fac=NIREnv_2$Month, perm = 499, strata=NIREnv_2$YearLoc)##
## Pairwise comparisons using factor fitting to an ordination
##
## data: NIR.cap by NIREnv_2$Month
## 999 permutations
##
## May June July August
## June 0.0044 - - -
## July 0.2130 0.0012 - -
## August 0.0012 0.0012 0.0012 -
## September 0.0012 0.0012 0.0012 0.0012
##
## P value adjustment method: fdrNIR ordination with TDNg included
#NIR ordination with TDNg included
NIRsp2 <- NIRpatches %>% drop_na() %>% dplyr::select(ADF,NDF, ADL, CP, TDNg)
NIR.cap3 <- capscale(NIRsp2 ~ 1, metaMDSdist = "true", dist="euclidean")## Square root transformation
## Wisconsin double standardizationsummary(NIR.cap3)##
## Call:
## capscale(formula = NIRsp2 ~ 1, distance = "euclidean", metaMDSdist = "true")
##
## Partitioning of squared Euclidean distance:
## Inertia Proportion
## Total 1.083 1
## Unconstrained 1.083 1
##
## Eigenvalues, and their contribution to the squared Euclidean distance
##
## Importance of components:
## MDS1 MDS2 MDS3 MDS4
## Eigenvalue 0.7559 0.2865 0.03419 0.006261
## Proportion Explained 0.6981 0.2646 0.03157 0.005782
## Cumulative Proportion 0.6981 0.9626 0.99422 1.000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 5.520938
##
##
## Species scores
##
## MDS1 MDS2 MDS3 MDS4
## ADF -0.7794 -1.1946 -0.4618 0.256367
## NDF -0.5284 -1.2355 -0.1306 -0.319680
## ADL -2.9510 1.7570 0.0869 -0.004229
## CP 3.2725 1.2847 -0.2935 -0.021085
## TDNg 0.9864 -0.6115 0.7990 0.088627
##
##
## Site scores (weighted sums of species scores)
##
## MDS1 MDS2 MDS3 MDS4
## 1 -1.732e-01 -0.1425131 3.753e-01 -2.067e-01
## 2 -5.130e-02 0.0070811 3.265e-01 -2.423e-01
## 3 -8.408e-02 0.0839100 2.276e-01 -7.330e-02
## 4 -3.923e-02 0.1470537 1.573e-01 -1.307e-01
## 5 -1.118e-01 0.0681421 9.982e-02 1.595e-02
## 6 -4.019e-02 0.1276691 1.311e-01 5.383e-02
## 7 6.270e-03 0.2923356 2.191e-01 2.388e-01
## 8 -5.543e-02 0.0067015 5.226e-01 1.917e-01
## 9 -1.178e-02 0.0853734 3.558e-01 -9.501e-02
## 10 1.050e-01 0.4537795 2.231e-01 1.629e-01
## 11 -3.189e-02 0.0289377 4.351e-01 -1.080e-01
## 12 -1.068e-01 0.3213342 -1.904e-01 2.087e-01
## 13 -4.979e-02 0.4370806 7.988e-02 -1.116e-02
## 14 -9.736e-02 0.0833015 3.748e-01 -2.225e-01
## 15 2.350e-02 0.6829507 -2.747e-01 6.100e-01
## 16 -1.390e-02 0.1396915 5.254e-01 1.646e-01
## 17 -1.285e-01 -0.1848789 4.063e-01 -1.251e-01
## 18 -1.457e-01 -0.0338698 3.879e-01 -1.184e-01
## 19 1.639e-02 0.0144891 2.710e-01 -2.653e-01
## 20 -1.040e-01 0.0757162 3.717e-01 1.031e-02
## 21 -1.684e-01 0.0009345 1.974e-01 -5.858e-02
## 22 -1.125e-01 0.0230797 3.134e-01 1.489e-03
## 23 -5.404e-02 0.0493010 5.335e-01 -2.613e-01
## 24 -1.237e-01 0.1515544 1.274e-01 -1.273e-01
## 25 -2.081e-01 0.0668158 2.284e-01 -1.887e-01
## 26 -1.006e-01 0.1201572 4.768e-02 4.810e-02
## 27 -9.287e-02 0.0794480 1.391e-01 -9.736e-02
## 28 -9.134e-02 0.0643793 2.264e-01 -2.214e-01
## 29 -6.328e-02 0.0585740 8.730e-02 -4.480e-02
## 30 -2.181e-01 0.0311566 -3.069e-02 -9.663e-02
## 31 -1.651e-01 -0.0299317 2.364e-01 -3.254e-01
## 32 -2.028e-01 0.0425948 -1.010e-01 2.946e-02
## 33 -1.810e-01 0.0789522 7.838e-02 5.153e-02
## 34 -1.775e-01 -0.0198908 2.189e-02 -2.284e-02
## 35 -1.451e-01 0.1016237 -6.676e-02 -4.328e-05
## 36 -1.439e-01 0.0767959 -6.062e-02 -1.865e-01
## 37 -8.410e-02 0.1158086 -1.063e-01 -2.509e-01
## 38 -1.008e-01 0.0446103 1.290e-02 -1.350e-01
## 39 -1.091e-01 0.2232575 -2.066e-01 5.654e-02
## 40 -2.556e-02 0.3262980 2.481e-02 1.681e-01
## 41 -1.560e-01 0.0320987 8.127e-02 -1.362e-01
## 42 -1.307e-01 0.1519398 1.402e-01 3.978e-02
## 43 -1.639e-01 -0.0101330 1.591e-01 -1.692e-01
## 44 7.204e-02 0.3408186 -1.593e-02 6.898e-02
## 45 -1.231e-01 0.0993606 -2.867e-01 1.749e-01
## 46 -8.025e-02 0.1143488 9.031e-02 -2.918e-02
## 47 -3.962e-02 0.1797504 9.614e-02 -6.599e-02
## 48 -9.747e-02 0.1553697 8.530e-03 2.030e-02
## 49 -1.634e-02 0.1695592 1.478e-01 -2.828e-01
## 50 2.809e-02 0.3666205 1.239e-02 -1.442e-01
## 51 -1.823e-02 0.2216372 6.995e-02 -1.179e-01
## 52 -1.407e-01 0.2114604 -4.955e-01 5.268e-02
## 53 6.097e-02 0.4297082 -5.800e-02 1.773e-01
## 54 -1.542e-02 0.2457539 -1.418e-01 6.114e-02
## 55 -1.064e-01 0.2205430 -6.362e-02 -1.502e-01
## 56 3.748e-02 0.1063620 3.707e-01 -1.914e-01
## 57 -1.380e-01 -0.0116318 -5.923e-02 3.669e-02
## 58 -1.288e-01 0.1429287 -1.027e-01 -3.035e-01
## 59 -7.451e-02 0.0966840 -1.990e-01 -3.648e-01
## 60 -7.716e-02 0.1157343 -2.104e-01 -3.851e-02
## 61 1.288e-02 0.2898892 -1.152e-01 2.829e-02
## 62 -6.971e-02 0.2292876 -1.102e-01 -7.866e-02
## 63 -1.743e-01 0.0481904 -2.891e-01 1.887e-02
## 64 -1.474e-01 -0.0377476 1.545e-01 2.569e-02
## 65 -8.428e-02 -0.0269705 1.726e-01 -5.554e-02
## 66 -1.464e-01 0.0262289 1.901e-01 6.916e-02
## 67 -2.307e-01 0.0459336 9.878e-02 -1.297e-01
## 68 -2.245e-01 -0.0541455 -9.495e-02 -1.041e-01
## 69 -2.073e-01 0.0174155 3.735e-02 -1.045e-01
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## 846 -2.037e-01 -0.0800866 3.846e-02 2.879e-02
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## 849 -2.570e-01 -0.0822176 -4.050e-02 -2.234e-02
## 850 -4.252e-02 -0.1422658 -1.027e-01 2.992e-01
## 851 -3.542e-02 -0.0726925 -1.880e-01 2.490e-01
## 852 3.423e-02 -0.0943456 -2.723e-01 -1.945e-01
## 853 9.975e-02 -0.0383194 -7.544e-02 -2.139e-01
## 854 -1.850e-01 -0.1760137 -1.719e-01 8.987e-02
## 855 -7.148e-02 -0.0929654 6.890e-02 -1.341e-01
## 856 -2.751e-01 -0.1450476 1.480e-01 2.567e-02
## 857 -2.126e-01 -0.0642492 2.171e-01 -9.249e-02
## 858 -2.784e-01 -0.0772961 1.374e-01 -8.050e-02
## 859 -2.096e-01 -0.0374713 1.248e-01 -1.400e-01plot(NIR.cap3)
text(NIR.cap3, display = "species", col="red")
NIRcap3E <- envfit(NIR.cap3 ~ Treatment + TSF + ESD + Month + Moisture + Powell + KgHa, data = NIREnv_2,
choices = c(1:2), perm=499, strata=NIREnv_2$YearLoc)
NIRcap3E##
## ***VECTORS
##
## MDS1 MDS2 r2 Pr(>r)
## Moisture 0.97713 -0.21264 0.6275 0.002 **
## Powell 0.87469 0.48467 0.1255 0.002 **
## KgHa -0.91156 -0.41117 0.2068 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499
##
## ***FACTORS:
##
## Centroids:
## MDS1 MDS2
## TreatmentCattle 0.0178 0.0499
## TreatmentSheep -0.0210 -0.0586
## TSFRB 0.1021 0.0286
## TSF1to2 -0.0029 -0.0538
## TSF2to3 -0.0621 -0.0945
## TSF3plus -0.1117 -0.0463
## TSFNYB -0.0264 0.0581
## ESDClayey 0.0033 0.0228
## ESDLoamy 0.0156 -0.0193
## ESDSaline Lowland -0.0181 -0.0132
## ESDSandy 0.0085 0.0060
## ESDThin Claypan -0.0240 -0.0637
## MonthMay 0.0668 0.0008
## MonthJune 0.2281 -0.0408
## MonthJuly 0.0124 -0.0319
## MonthAugust -0.0810 -0.0009
## MonthSeptember -0.1122 0.0635
##
## Goodness of fit:
## r2 Pr(>r)
## Treatment 0.0465 1.000
## TSF 0.1063 0.002 **
## ESD 0.0063 0.490
## Month 0.2385 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499NIRcap3V <- envfit(NIR.cap3 ~ Moisture + Powell + KgHa, data = NIREnv_2,
choices = c(1:2), perm=499, strata=NIREnv_2$YearLoc)
plot(NIR.cap3, main="Euclidean")
text(NIR.cap3, display = "species", col="red")
plot(NIRcap3V)
NIRcap3TSF <- envfit(NIR.cap3 ~ TSF, data = NIREnv_2,
choices = c(1:2), perm=499, strata=NIREnv_2$YearLoc)
plot(NIR.cap3, main="Euclidean", display="sites")
#text(NIR.cap, display = "species", col="red")
ordispider(NIR.cap3, NIREnv_2$TSF, label=TRUE)
pairwise.factorfit(NIR.cap3, fac=NIREnv_2$TSF, perm = 499, strata=NIREnv_2$YearLoc)##
## Pairwise comparisons using factor fitting to an ordination
##
## data: NIR.cap3 by NIREnv_2$TSF
## 999 permutations
##
## RB 1to2 2to3 3plus
## 1to2 0.0012 - - -
## 2to3 0.0012 0.0044 - -
## 3plus 0.0012 0.0012 0.0150 -
## NYB 0.0012 0.0012 0.0012 0.0012
##
## P value adjustment method: fdrNIRcap3M <- envfit(NIR.cap3 ~ Month, data = NIREnv_2,
choices = c(1:3), perm=499, strata=NIREnv_2$YearLoc)
plot(NIR.cap3, main="Euclidean", display="sites")
#text(NIR.cap, display = "species", col="red")
ordispider(NIR.cap3, NIREnv_2$Month, label=TRUE)
pairwise.factorfit(NIR.cap3, fac=NIREnv_2$Month, perm = 499, strata=NIREnv_2$YearLoc)##
## Pairwise comparisons using factor fitting to an ordination
##
## data: NIR.cap3 by NIREnv_2$Month
## 999 permutations
##
## May June July August
## June 0.0033 - - -
## July 0.2250 0.0012 - -
## August 0.0012 0.0012 0.0012 -
## September 0.0012 0.0012 0.0012 0.0012
##
## P value adjustment method: fdr