HREC Veg Community

Overview

Data Transformation

## Yo. 91 species (94.7916666666667% of total) don't make the cut.

HREC Relative Abundance

Ends at PASM; more species than the proportion filter

Structure Ordi Setup

StSp.MDSe <- metaMDS(StrSpeHREC, k=3, trymax=50, distance="euclidean")

## Wisconsin double standardization
## Run 0 stress 0.04709878 
## Run 1 stress 0.04705197 
## ... New best solution
## ... Procrustes: rmse 0.005707212  max resid 0.03255237 
## Run 2 stress 0.04726615 
## ... Procrustes: rmse 0.002897145  max resid 0.01774475 
## Run 3 stress 0.04705185 
## ... New best solution
## ... Procrustes: rmse 0.0001171594  max resid 0.0008180187 
## ... Similar to previous best
## Run 4 stress 0.04771956 
## Run 5 stress 0.07093041 
## Run 6 stress 0.04709875 
## ... Procrustes: rmse 0.005841537  max resid 0.03316322 
## Run 7 stress 0.04709936 
## ... Procrustes: rmse 0.005903207  max resid 0.03326123 
## Run 8 stress 0.04705174 
## ... New best solution
## ... Procrustes: rmse 5.663672e-05  max resid 0.000339995 
## ... Similar to previous best
## Run 9 stress 0.04771885 
## Run 10 stress 0.0477194 
## Run 11 stress 0.04709879 
## ... Procrustes: rmse 0.005812296  max resid 0.03288403 
## Run 12 stress 0.04705203 
## ... Procrustes: rmse 0.0001052325  max resid 0.000637019 
## ... Similar to previous best
## Run 13 stress 0.04695249 
## ... New best solution
## ... Procrustes: rmse 0.004099961  max resid 0.03189478 
## Run 14 stress 0.0477194 
## Run 15 stress 0.04709916 
## ... Procrustes: rmse 0.003587646  max resid 0.02255251 
## Run 16 stress 0.04695265 
## ... Procrustes: rmse 6.40907e-05  max resid 0.000500904 
## ... Similar to previous best
## Run 17 stress 0.04695266 
## ... Procrustes: rmse 6.835673e-05  max resid 0.0005469457 
## ... Similar to previous best
## Run 18 stress 0.04709869 
## ... Procrustes: rmse 0.003539348  max resid 0.02245626 
## Run 19 stress 0.04709895 
## ... Procrustes: rmse 0.003523929  max resid 0.0224036 
## Run 20 stress 0.04709871 
## ... Procrustes: rmse 0.003571174  max resid 0.02258866 
## *** Solution reached

StSp.MDSe #0.04695249

## 
## Call:
## metaMDS(comm = StrSpeHREC, distance = "euclidean", k = 3, trymax = 50) 
## 
## global Multidimensional Scaling using monoMDS
## 
## Data:     wisconsin(StrSpeHREC) 
## Distance: euclidean 
## 
## Dimensions: 3 
## Stress:     0.04695249 
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation 
## Species: expanded scores based on 'wisconsin(StrSpeHREC)'

#StSp.MDSc <- metaMDS(StrSpeHREC, k=3, trymax=50, distance="canberra") 
#StSp.MDSc #0.08123532

#StSp.MDSb <- metaMDS(StrSpeHREC, k=3, trymax=50, distance="bray") 
#StSp.MDSb #0.06333051

StSp.cape <-  capscale(StrSpeHREC ~ 1, metaMDSdist = "true", dist="euclidean")

## Wisconsin double standardization

summary(StSp.cape) #0.5772, 0.7787, 0.9193

## 
## Call:
## capscale(formula = StrSpeHREC ~ 1, distance = "euclidean", metaMDSdist = "true") 
## 
## Partitioning of squared Euclidean distance:
##               Inertia Proportion
## Total           3.578          1
## Unconstrained   3.578          1
## 
## Eigenvalues, and their contribution to the squared Euclidean distance 
## 
## Importance of components:
##                         MDS1   MDS2   MDS3    MDS4    MDS5    MDS6
## Eigenvalue            2.0651 0.7209 0.5032 0.17374 0.07216 0.04275
## Proportion Explained  0.5772 0.2015 0.1406 0.04856 0.02017 0.01195
## Cumulative Proportion 0.5772 0.7787 0.9193 0.96788 0.98805 1.00000
## 
## 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:  4.293758 
## 
## 
## Species scores
## 
##             MDS1    MDS2     MDS3     MDS4     MDS5     MDS6
## BGCover  -1.8858 -0.3917 -1.10656  0.03948 -0.04529 -0.02165
## GCover   -1.8738  0.6556  0.93236  0.14335  0.13690 -0.02512
## LitCover  1.3588  0.5252 -0.26281  0.71463 -0.05378 -0.03152
## LitMean   0.4253  0.8360  0.01222 -0.46178 -0.39175  0.01510
## MaxDead   0.9914  0.3761 -0.35089 -0.35391  0.43125  0.09285
## MaxLive   0.2926 -1.0821  0.44210  0.06060 -0.09205  0.30835
## VOR_Mean  0.6916 -0.9190  0.33356 -0.14238  0.01473 -0.33802
## 
## 
## Site scores (weighted sums of species scores)
## 
##         MDS1      MDS2     MDS3      MDS4      MDS5      MDS6
## 1  -0.857335  0.666057  0.74758 -0.089129 -0.294833 -0.150751
## 2   0.044742  0.546772 -0.01177 -0.050910 -0.115995  0.347521
## 3   0.023869  0.560305  0.14427  0.314837 -0.183274 -0.312453
## 4   0.210366  0.638966 -0.04201  0.343882 -0.380207 -0.349125
## 5  -0.613784  0.446971  0.21835 -0.133080  0.334566 -0.087516
## 6  -0.019021  0.544552  0.29080  0.214010  0.446564 -0.108505
## 7   0.139730  0.418818  0.03411  0.291317  0.050833 -0.305535
## 8   0.004813  0.499316  0.09701 -0.008279  0.352505 -0.582154
## 9  -0.696471  0.073637 -0.23223 -0.111851 -0.640787 -0.187345
## 10  0.239744  0.360552 -0.12921  0.038991 -0.916953  0.030340
## 11  0.323629  0.466135 -0.22433  0.501851 -0.813275 -0.087006
## 12  0.208697  0.373445  0.02544  0.672422 -0.095266  0.226549
## 13 -0.144504  0.569166  0.49748  0.805081 -0.103998  0.174467
## 14  0.387046  0.371559 -0.17570  0.253356 -1.056364  0.132643
## 15  0.339542  0.259067 -0.06938  0.472990  0.061512  0.296905
## 16  0.128158  0.479637  0.03514  0.122081 -0.274585 -0.012525
## 17 -0.562624  0.090069 -0.02158  0.259387 -0.286135  0.276082
## 18  0.312904  0.045052 -0.15247  0.254676 -0.175486 -0.167718
## 19  0.229073  0.384679 -0.18944  0.524018  0.457125  0.279091
## 20  0.132525 -0.071673 -0.12333 -0.355449 -0.058329 -0.001844
## 21 -0.563006  0.609907  0.64547 -0.103879 -0.093850 -0.245777
## 22  0.123129  0.619501  0.12681  0.515536 -0.673136  0.449622
## 23  0.164790  0.443163  0.14081  0.277648 -0.041325  0.192653
## 24  0.330595  0.280841 -0.42372  0.283165  0.541541  0.428035
## 25 -0.355097 -0.288719  0.03055 -0.055167  0.050089  0.018462
## 26 -0.518116 -0.283678 -0.26238 -0.085043 -0.254035  0.524518
## 27 -0.074173 -0.137545 -0.27856  0.213966 -0.204849  0.330243
## 28 -0.055823  0.095604 -0.16660  0.268754 -0.439527  0.330120
## 29 -0.404211 -0.304525 -0.21933  0.010188 -0.381381 -0.079945
## 30 -0.690113 -0.315122 -0.82834  0.127386 -0.401256 -0.131003
## 31 -0.316638  0.148148  0.73887 -0.233418 -0.002449  0.066443
## 32 -0.060226 -0.223839  0.47090 -0.322711  0.064261  0.003956
## 33 -0.300713 -0.621058 -1.03240 -0.084263 -0.109327 -0.167043
## 34 -0.329926 -0.493898  0.26033 -0.373845 -0.145890 -0.514052
## 35  0.088824  0.123545  0.40765 -0.601355 -0.840055  0.522034
## 36 -0.132277 -0.090339  0.02251  0.003634  0.281615  0.403555
## 37 -0.175786 -0.165440  0.45869 -0.438192  0.095418  0.144013
## 38 -0.716989  0.044846  0.05959  0.099147  0.291130 -0.161593
## 39  0.226020  0.074789  0.08046  0.073193 -0.466681  0.116790
## 40  0.235362  0.050464  0.12524  0.039581 -0.962623  0.131114
## 41 -0.292516 -0.525942 -0.39477 -0.017941 -0.193465  1.217067
## 42 -0.762682 -0.163486  0.21152  0.142859  0.035677  0.298194
## 43  0.261975 -0.557953 -0.05442 -0.288544 -0.085238 -0.631051
## 44  0.329876 -0.615905 -0.25066 -0.245846 -0.358809 -0.740361
## 45 -0.404876 -0.368567  0.30115 -0.059382  0.241140  0.834433
## 46 -0.822377 -0.524926 -1.12261 -0.066622 -0.362921 -0.038631
## 47 -0.237609 -0.061324  0.15320 -0.052025 -0.315949  0.416612
## 48  0.034606 -0.084765  0.38902 -0.162748 -0.133656 -0.304772
## 49  0.620635 -0.747383 -0.05658 -0.063601 -0.078832 -0.520362
## 50  0.206378 -0.236917  0.20796  0.530177 -0.381728 -0.385703
## 51 -0.210188 -0.264648  0.54760  0.206628  0.773005 -0.389455
## 52  0.583329 -0.095263 -0.11178  0.950924 -0.711456 -0.583770
## 53  0.173119 -0.135002 -0.05073  0.512343  0.135519 -0.276658
## 54  0.466713 -0.615952 -0.05750  0.386595 -0.039866 -0.222113
## 55 -0.427468 -0.283000  0.53848  0.077807  0.932025 -0.901045
## 56  0.368362 -0.270925  0.02952  0.217431  0.072192  0.253539
## 57  0.588520 -0.831627 -0.03821 -0.181570  0.430411  0.078495
## 58  0.258997 -0.409031  0.44902 -0.092630  0.537613 -0.529809
## 59 -0.330666 -0.233437  0.58404  0.180436  0.348232  0.718131
## 60  0.197895 -0.443350  0.61431  0.012053  0.316921  0.076502
## 61  0.130639 -0.294004  0.02870  0.234979  0.487484  0.267173
## 62  0.408732 -0.529486  0.15266  0.338226  0.389113  0.537535
## 63 -0.209310 -0.711191  0.66098 -0.078096 -0.019565  1.270961
## 64  0.461812 -0.003395  0.14074  0.680080 -0.250848 -0.112028
## 65  0.598261 -0.873493 -0.02452 -0.325718  0.355371  0.114720
## 66 -0.063149  0.085809  0.11801  0.805942  0.370331  0.119670
## 67  0.139987 -0.531265  0.69011  0.306321  0.050037 -0.838995
## 68  0.597166 -1.312024  0.36480 -0.978800 -0.571432 -1.150494
## 69 -0.017584 -0.209761  0.78043  0.791208  0.363616 -0.446294
## 70  0.100480 -0.583466 -0.37632  0.493133  0.171136  0.449945
## 71 -0.282191 -0.875042  0.66320 -0.284424 -0.223984  0.910153
## 72  0.230082 -0.535499  0.23328  0.312443  0.069375  0.027520
## 73  0.461475  0.230864 -0.35825 -0.332819  0.019669 -0.145961
## 74 -0.078616  0.090143 -0.50861 -0.360403 -0.391203 -0.328453
## 75 -0.169158  0.477735  0.41495 -1.149025 -0.286980 -0.357862
## 76 -0.967376 -0.412377 -1.19365 -0.101721 -0.447719 -0.609174
## 77  0.013856  0.497306  0.55247 -0.982139  0.079813 -0.258723
## 78  0.110917  0.384246  0.17439 -0.884956  0.342500 -0.334057
## 79  0.700656  0.293481 -0.47700  0.820327  0.199963 -0.369767
## 80 -1.377842  0.218189 -0.75419  0.319681  0.509934 -1.042736
## 81  0.255039  0.442686  0.23454 -0.940101 -0.099033 -0.352308
## 82  0.352340  0.098885 -0.29098 -0.117752  0.984836  0.305753
## 83  0.810052  0.281401 -0.65189  0.406845  0.990935 -0.017614
## 84 -0.982482  0.313291  0.30278  0.479907  0.835243 -0.218491
## 85  0.299200  0.902782 -0.17384 -0.959397 -0.136326 -0.037251
## 86  0.771651  0.155574 -0.49846 -0.557881  0.417347  0.280716
## 87  0.264165  0.529355 -0.35812 -0.862772  0.318155  0.187220
## 88 -0.107259  0.469957  0.50082  0.140426  0.033340 -0.470656
## 89 -0.268888  0.016291 -1.25330 -0.409236  0.720729  0.230009
## 90 -0.043960  0.483583  0.22373 -0.666315 -0.151310  0.486530
## 91  0.706178  0.132215 -0.43102 -0.559393  0.290529  0.389555
## 92 -0.242862 -0.313848  0.23082 -0.196852 -0.656130  0.746803
## 93  0.077248  0.516650 -0.37834 -0.617587  1.011405  0.315492
## 94  0.419782  0.361460 -0.19233 -0.410144  0.464320  0.411929
## 95  0.804513  0.379614 -0.58798 -0.176106 -0.274178 -0.136453
## 96 -0.812300  0.003012 -0.92248 -0.098750  0.257421  0.033116

#StSp.capc <-  capscale(StrSpeHREC ~ 1, metaMDSdist = "true", dist="canberra")
#summary(StSp.capc) #0.3781, 0.5531, 0.6419


#StSp.capb <-  capscale(StrSpeHREC ~ 1, metaMDSdist = "true", dist="bray")
#summary(StSp.capb) #0.4977, 0.6981, 0.77056

#E > B > C

Structure Species Ordi Plot

Structure Env Fit

StSp.fit <- envfit(StSp.MDSe ~ TSF + Management, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                      strata=EnvPatchHREC2$YearLoc)
StSp.fit

## 
## ***FACTORS:
## 
## Centroids:
##                    NMDS1   NMDS2   NMDS3
## TSF2yr3yr         0.0803 -0.0040  0.0075
## TSF1yr2yr         0.0173 -0.0309  0.0142
## TSF3yr4yr         0.1736  0.0727  0.0532
## TSFRB            -0.1822 -0.0164 -0.0123
## TSFUnburned       0.0571  0.0157 -0.0102
## ManagementCattle  0.0040 -0.0147  0.0042
## ManagementSheep  -0.0040  0.0147 -0.0042
## 
## Goodness of fit:
##                r2 Pr(>r)   
## TSF        0.3879  0.002 **
## Management 0.0073  1.000   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

StSp.M <- envfit(StSp.MDSe ~ Management, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                   strata=EnvPatchHREC2$YearLoc)
StSp.M

## 
## ***FACTORS:
## 
## Centroids:
##                    NMDS1   NMDS2   NMDS3
## ManagementCattle  0.0040 -0.0147  0.0042
## ManagementSheep  -0.0040  0.0147 -0.0042
## 
## Goodness of fit:
##                r2 Pr(>r)
## Management 0.0073      1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

StSp.TSF <- envfit(StSp.MDSe ~ TSF, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                      strata=EnvPatchHREC2$YearLoc)

StSp.TSF

## 
## ***FACTORS:
## 
## Centroids:
##               NMDS1   NMDS2   NMDS3
## TSF2yr3yr    0.0803 -0.0040  0.0075
## TSF1yr2yr    0.0173 -0.0309  0.0142
## TSF3yr4yr    0.1736  0.0727  0.0532
## TSFRB       -0.1822 -0.0164 -0.0123
## TSFUnburned  0.0571  0.0157 -0.0102
## 
## Goodness of fit:
##         r2 Pr(>r)   
## TSF 0.3879  0.002 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

pairwise.factorfit(StSp.MDSe, fac=EnvPatchHREC2$TSF, perm = 499, strata=EnvPatchHREC2$YearLoc)

## 
##  Pairwise comparisons using factor fitting to an ordination 
## 
## data:  StSp.MDSe by EnvPatchHREC2$TSF
## 999 permutations 
## 
##          2yr3yr 1yr2yr 3yr4yr RB    
## 1yr2yr   0.2000 -      -      -     
## 3yr4yr   0.1038 0.0080 -      -     
## RB       0.0025 0.0025 0.0025 -     
## Unburned 0.5750 0.0586 0.0117 0.0025
## 
## P value adjustment method: fdr

pairwise.factorfit(StSp.MDSe, fac=EnvPatchHREC2$Management, perm = 499, strata=EnvPatchHREC2$YearLoc)

## 
##  Pairwise comparisons using factor fitting to an ordination 
## 
## data:  StSp.MDSe by EnvPatchHREC2$Management
## 999 permutations 
## 
##       Cattle
## Sheep 0.43  
## 
## P value adjustment method: fdr

Structure fit Management

Veg Structrue Fit TSF

HREC Species Ordi Setup

HSP10.MDSe <- metaMDS(SpHRECProp10, k=3, trymax=50, distance="euclidean")

## Wisconsin double standardization
## Run 0 stress 0.09874177 
## Run 1 stress 0.1009084 
## Run 2 stress 0.1013264 
## Run 3 stress 0.1220569 
## Run 4 stress 0.1109768 
## Run 5 stress 0.1013264 
## Run 6 stress 0.1102391 
## Run 7 stress 0.1206641 
## Run 8 stress 0.1117762 
## Run 9 stress 0.1144179 
## Run 10 stress 0.1152592 
## Run 11 stress 0.1031501 
## Run 12 stress 0.1142247 
## Run 13 stress 0.1013264 
## Run 14 stress 0.1126032 
## Run 15 stress 0.1206735 
## Run 16 stress 0.09811849 
## ... New best solution
## ... Procrustes: rmse 0.01398754  max resid 0.08536636 
## Run 17 stress 0.1227299 
## Run 18 stress 0.1184068 
## Run 19 stress 0.1008262 
## Run 20 stress 0.1013264 
## Run 21 stress 0.1156634 
## Run 22 stress 0.1124027 
## Run 23 stress 0.1135355 
## Run 24 stress 0.0987417 
## Run 25 stress 0.1225732 
## Run 26 stress 0.1000241 
## Run 27 stress 0.116061 
## Run 28 stress 0.1167743 
## Run 29 stress 0.1101064 
## Run 30 stress 0.1007496 
## Run 31 stress 0.1098418 
## Run 32 stress 0.1214391 
## Run 33 stress 0.1024203 
## Run 34 stress 0.1191736 
## Run 35 stress 0.09885749 
## Run 36 stress 0.1055897 
## Run 37 stress 0.1156633 
## Run 38 stress 0.1218162 
## Run 39 stress 0.1196952 
## Run 40 stress 0.1156866 
## Run 41 stress 0.1060681 
## Run 42 stress 0.1111502 
## Run 43 stress 0.1206568 
## Run 44 stress 0.1013264 
## Run 45 stress 0.112403 
## Run 46 stress 0.1102393 
## Run 47 stress 0.1009084 
## Run 48 stress 0.1031501 
## Run 49 stress 0.1009084 
## Run 50 stress 0.1200164 
## *** No convergence -- monoMDS stopping criteria:
##      2: no. of iterations >= maxit
##     46: stress ratio > sratmax
##      2: scale factor of the gradient < sfgrmin

HSP10.MDSe  #Stress  0.09811858 for k=3

## 
## Call:
## metaMDS(comm = SpHRECProp10, distance = "euclidean", k = 3, trymax = 50) 
## 
## global Multidimensional Scaling using monoMDS
## 
## Data:     wisconsin(SpHRECProp10) 
## Distance: euclidean 
## 
## Dimensions: 3 
## Stress:     0.09811849 
## Stress type 1, weak ties
## No convergent solutions - best solution after 50 tries
## Scaling: centring, PC rotation 
## Species: expanded scores based on 'wisconsin(SpHRECProp10)'

HREC Species Capscale results

HSP10.cape <-  capscale(SpHRECProp10 ~ 1, metaMDSdist = "true", dist="euclidean")

## Wisconsin double standardization

summary(HSP10.cape)  #0.7315 through third axis

## 
## Call:
## capscale(formula = SpHRECProp10 ~ 1, distance = "euclidean",      metaMDSdist = "true") 
## 
## Partitioning of squared Euclidean distance:
##               Inertia Proportion
## Total           10.23          1
## Unconstrained   10.23          1
## 
## Eigenvalues, and their contribution to the squared Euclidean distance 
## 
## Importance of components:
##                         MDS1   MDS2   MDS3   MDS4   MDS5    MDS6    MDS7
## Eigenvalue            3.2375 3.0753 1.1724 0.8257 0.7551 0.50848 0.39737
## Proportion Explained  0.3164 0.3006 0.1146 0.0807 0.0738 0.04969 0.03884
## Cumulative Proportion 0.3164 0.6170 0.7315 0.8122 0.8860 0.93574 0.97457
##                          MDS8
## Eigenvalue            0.26018
## Proportion Explained  0.02543
## Cumulative Proportion 1.00000
## 
## 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:  5.583712 
## 
## 
## Species scores
## 
##         MDS1     MDS2      MDS3    MDS4     MDS5     MDS6
## AGCR -0.2174 -0.08773  1.687616 -0.2528  0.13113 -0.12634
## BRIN  1.1490  1.18725 -0.425820 -0.6319 -0.60663 -0.16078
## COAR  0.2019  0.01250  0.002302 -0.1678 -0.39216  0.84846
## DISP  0.3709  0.70460 -0.240579 -0.1530  0.43495 -0.71223
## ELIN -1.3622 -2.01199 -0.557198 -0.5892 -0.02917 -0.18114
## ELTR  0.3631  0.24263 -0.391268  0.3054  1.11707  0.36730
## MEOF  0.2927 -0.05968  0.046901 -0.2165  0.12531  0.24116
## MESA -2.1206  1.29970 -0.138092  0.6808 -0.34667 -0.04792
## POPR  1.3225 -1.28727  0.016138  1.0249 -0.43384 -0.22850
## 
## 
## Site scores (weighted sums of species scores)
## 
##         MDS1      MDS2      MDS3     MDS4      MDS5     MDS6
## 1  -0.256746 -0.011889  1.634126 -0.39755  0.302945 -0.30500
## 2  -0.216636  0.287018  1.182467  0.29874 -0.094356 -0.26896
## 3  -0.262326  0.399943  1.579682  0.36130  0.013957 -0.22642
## 4  -0.695140  0.412315  1.024096  0.84460 -0.054211 -0.15413
## 5  -0.450821 -0.509301  0.553319  0.05130  0.163414 -0.38922
## 6  -0.412576 -0.295461 -0.193025 -0.58334 -0.144624 -0.38534
## 7  -0.657384 -0.355952  0.389718 -0.21622  0.162157 -0.32754
## 8  -0.348435 -0.709555 -0.209488  0.04682 -0.072332 -0.47010
## 9  -0.120507  0.866394 -0.436353 -0.06583  0.330488 -0.52252
## 10 -0.080973  1.127130 -0.495279 -0.17344  0.743662 -0.23296
## 11 -0.426446  1.143930 -0.310031  0.07097  0.221455 -0.64926
## 12 -0.433198  0.311891 -0.118612  1.15921 -0.553418 -0.28558
## 13 -0.706426  0.763825 -0.055405  0.47625 -0.303979 -0.34009
## 14 -0.468366  0.880599 -0.465867 -0.20078  0.229820 -0.91224
## 15 -0.875725  0.002173 -0.577450 -0.51811 -0.109764 -0.30229
## 16 -0.225055  1.059359  0.305021 -0.33355  0.540238 -1.00667
## 17 -0.683179  1.072119 -0.347286 -0.05158 -0.722653 -0.07556
## 18 -0.039353 -1.374738 -0.463602  0.61405  0.464452 -0.39467
## 19 -0.268628  1.266928 -0.485073 -0.14285 -0.205266 -0.09973
## 20  0.115836 -0.945031 -0.420988  1.01305  1.156611  0.12988
## 21 -0.767988  0.304968 -0.373430  0.60827 -0.454709 -0.20532
## 22 -0.479488  0.608800 -0.383847  0.24971 -0.144722  0.04882
## 23 -0.266543  0.602955 -0.448697  0.16353 -0.388838 -0.07750
## 24 -0.552785  0.944877 -0.319142  0.67294 -0.273627 -0.39455
## 25  0.014512  0.177677  1.667375 -0.92678  0.138128  0.45024
## 26  0.072038 -0.176239  1.686902  0.41195  0.116973  0.35428
## 27  0.074706  0.280388  0.762865 -0.28449 -0.346048  0.56513
## 28  0.049866  0.014152  1.574939  0.48936  0.004669  0.11859
## 29 -0.382497 -0.582029  0.752999 -0.70757  0.325083 -0.36087
## 30 -0.334188 -0.801557  0.089966 -0.24511  0.098225 -0.30920
## 31 -0.005479 -0.595586 -0.456668 -0.59412 -0.303411 -0.27195
## 32 -0.396048 -0.631278 -0.297064 -1.01415  0.004099 -0.33605
## 33  0.463435  0.659156 -0.372894 -0.13197  1.046486  0.30761
## 34  0.501459  0.560376 -0.150984 -0.24240 -0.372044  0.05920
## 35  0.774035  0.200023 -0.205007  0.37640 -0.486889 -0.49848
## 36 -0.057369  0.369989 -0.096965  0.96188 -0.704415 -0.01673
## 37  0.339626 -0.368059  0.205667  0.71590 -0.485458 -0.03104
## 38  0.636104  0.473542 -0.041243 -0.01265  0.531367 -1.71978
## 39  0.593321  0.152637 -0.453516 -0.17358  0.357489 -0.27137
## 40  0.149880 -0.403401 -0.171716  0.02054 -0.401556  0.37865
## 41  0.590783  0.254932 -0.165169 -0.49363 -0.942335  1.83048
## 42  0.722761  0.566837 -0.415165 -0.39248 -0.215807  0.36975
## 43 -1.723760  0.222717 -0.475905  0.43536  0.027984  0.20337
## 44 -1.651710  0.143628 -0.465135  0.52264 -0.012896  0.13867
## 45  0.048009 -0.662767 -0.253883  0.23140 -0.368674  0.80987
## 46  0.433908  0.146436 -0.455009  0.97212  2.604588  0.94529
## 47  0.315518 -0.560803 -0.292899  0.59948 -0.552544 -0.19107
## 48  0.358411 -0.160851 -0.310041  0.28346 -0.576874  0.17654
## 49 -0.266812  0.127590  0.587427 -0.69509  0.395827  0.70208
## 50 -0.093103 -0.033688  1.090096 -0.10737  0.369891  0.42970
## 51 -0.119125 -0.008514  0.547773 -0.41376 -0.095368  0.43887
## 52 -0.154375  0.335680  1.382905  0.07834  0.055271  0.26684
## 53 -0.144094 -0.742749  0.966801 -0.35485  0.429571 -0.32187
## 54 -0.062251 -0.687115  0.045125 -0.38959  0.223426 -0.05534
## 55 -0.026841 -0.597943 -0.227881 -1.14737  0.097842 -0.12085
## 56  0.126272 -0.594875 -0.168097 -0.05478 -0.306529 -0.45982
## 57  0.479186  0.724312 -0.084442 -0.68880  0.952365  0.25123
## 58  0.731714  0.483006 -0.116952 -0.85769 -0.344344  0.20892
## 59  0.730240  0.284640 -0.076815 -0.16324  0.035712 -0.54046
## 60 -0.127681  0.238497 -0.257530  0.58577 -0.644235  0.09908
## 61  0.616710  0.166265  0.298450 -0.38488 -0.601382 -0.36580
## 62  0.681226  0.534974  0.001133 -0.44607  0.679381 -1.69475
## 63  0.621046  0.213182 -0.321833 -0.30611  0.460139 -0.41850
## 64  0.312329 -0.617232 -0.400776  0.02556 -0.496595 -0.22348
## 65  0.524070  0.164993 -0.127141 -0.35051 -0.670308  1.51445
## 66  0.817306  0.441979 -0.273290 -0.41517 -0.503415  0.34354
## 67 -1.398443 -0.374897 -0.568695 -0.46996  0.160679  0.67427
## 68 -1.353339 -0.182887 -0.500271 -0.36191  0.166361  0.69611
## 69 -0.121942 -0.988702 -0.406308 -0.21837  0.014269  0.24717
## 70  0.570452  0.138393 -0.397870  0.47836  2.231779  0.98809
## 71  0.409574 -0.330454 -0.310911 -0.02669  0.033940  0.23533
## 72  0.286476 -0.774897 -0.348603  0.18648 -0.334158 -0.19219
## 73  0.037531 -0.214126  0.575822  0.50025 -0.091660  0.09069
## 74 -0.139243  0.194937  0.013562 -0.05929 -0.426484  0.33318
## 75  0.217672 -0.148379 -0.211652 -0.04472 -0.577738  0.74066
## 76  0.087809  0.181017  0.490001  0.67631 -0.260089  0.65748
## 77 -0.127209 -0.794342 -0.364965 -0.74161  0.036060 -0.30893
## 78 -0.438094 -0.904967 -0.513876 -1.44522  0.197306 -0.38067
## 79 -0.367555 -0.756751  0.729413 -0.87250  0.468175 -0.35084
## 80  0.428424 -0.573689 -0.106223  0.41458 -0.474562 -0.56850
## 81  0.568733  0.441217 -0.407827 -0.85471 -0.602559  0.36431
## 82  0.784684  0.184479 -0.167272  0.19746 -0.241385 -0.59883
## 83  0.524879  0.603286 -0.293288 -0.95819  0.398245  0.24536
## 84 -0.352503  0.230897 -0.170381  1.04699 -0.550603 -0.16953
## 85  1.016028 -0.094763  0.250405  0.81930 -0.796822 -0.39081
## 86  0.615377  0.086309 -0.361276 -0.23732  0.334873 -0.52019
## 87  0.753645 -0.066361  0.275960  0.13803 -0.595784  0.19619
## 88  0.409763 -0.474022 -0.149401 -0.18172 -0.455704 -0.21207
## 89  0.943054 -0.156034 -0.045865  1.07290 -0.208382  0.34887
## 90 -0.968226  0.044477 -0.385546  0.37739  0.407647  1.04698
## 91  0.531029  0.298943 -0.148096 -0.75105 -0.580762  1.38152
## 92 -1.154916 -0.208186 -0.412933  0.05385 -0.032778  0.79733
## 93  0.705636 -0.186608 -0.220861  1.08296  1.461269  0.78517
## 94  0.451357 -0.443191 -0.330671  0.23235  0.314644  0.06766
## 95  0.007832 -1.104820 -0.513923 -0.08064 -0.100881 -0.54680
## 96  0.417262 -1.192099 -0.089712  1.36325 -0.224985 -0.56503

HREC Species Ordi Plot

HREC Species Env Fit

This is where things get frustrating!

Using Year+Pasture shows TSF is not significant and VOR almost is depending on the iteration.

SpHREC.fit <- envfit(HSP10.MDSe ~ TSF + Management + BGCover + GCover + LitCover + LitMean + 
                        MaxDead + MaxLive + VOR_Mean, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                      strata=EnvPatchHREC2$YearLoc)
SpHREC.fit

## 
## ***VECTORS
## 
##             NMDS1    NMDS2    NMDS3     r2 Pr(>r)  
## BGCover   0.54611 -0.30014  0.78210 0.0533  0.188  
## GCover   -0.41249 -0.73745  0.53481 0.0215  0.574  
## LitCover -0.46636  0.76059  0.45168 0.0048  0.842  
## LitMean  -0.11714 -0.67266  0.73062 0.0018  0.930  
## MaxDead   0.63809 -0.41944  0.64568 0.0530  0.554  
## MaxLive  -0.18139 -0.31987  0.92994 0.0528  0.342  
## VOR_Mean -0.62449 -0.49040  0.60788 0.0612  0.094 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499
## 
## ***FACTORS:
## 
## Centroids:
##                    NMDS1   NMDS2   NMDS3
## TSF2yr3yr         0.1043 -0.0652 -0.0328
## TSF1yr2yr         0.0747 -0.0211 -0.0101
## TSF3yr4yr         0.0768 -0.0671 -0.0430
## TSFRB            -0.0122  0.0066  0.0166
## TSFUnburned      -0.0767  0.0390  0.0121
## ManagementCattle -0.0083  0.0758 -0.0385
## ManagementSheep   0.0083 -0.0758  0.0385
## 
## Goodness of fit:
##                r2 Pr(>r)
## TSF        0.0813  0.132
## Management 0.0862  1.000
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

SpHREC.V <- envfit(HSP10.MDSe ~ VOR_Mean, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                   strata=EnvPatchHREC2$YearLoc)
SpHREC.V

## 
## ***VECTORS
## 
##             NMDS1    NMDS2    NMDS3     r2 Pr(>r)
## VOR_Mean -0.62449 -0.49040  0.60788 0.0612  0.112
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

SpHREC.TSF <- envfit(HSP10.MDSe ~ TSF, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                      strata=EnvPatchHREC2$YearLoc)

HREC Species Fit VOR

HREC Species Fit TSF

HREC Species Fit Management

HREC FG Ordi Setup

FGHREC.MDSe <- metaMDS(FGFineHREC, k=3, trymax=50, distance="euclidean")

## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.06718037 
## Run 1 stress 0.06718037 
## ... Procrustes: rmse 2.998206e-05  max resid 0.0001540773 
## ... Similar to previous best
## Run 2 stress 0.0671804 
## ... Procrustes: rmse 1.243658e-05  max resid 6.458118e-05 
## ... Similar to previous best
## Run 3 stress 0.06718037 
## ... New best solution
## ... Procrustes: rmse 1.941414e-05  max resid 0.0001071447 
## ... Similar to previous best
## Run 4 stress 0.06718039 
## ... Procrustes: rmse 1.203618e-05  max resid 6.088047e-05 
## ... Similar to previous best
## Run 5 stress 0.06718038 
## ... Procrustes: rmse 1.466033e-05  max resid 7.964794e-05 
## ... Similar to previous best
## Run 6 stress 0.06718046 
## ... Procrustes: rmse 6.00107e-05  max resid 0.0003723789 
## ... Similar to previous best
## Run 7 stress 0.06718037 
## ... Procrustes: rmse 2.071349e-05  max resid 0.0001187834 
## ... Similar to previous best
## Run 8 stress 0.06718038 
## ... Procrustes: rmse 2.204872e-05  max resid 0.0001552741 
## ... Similar to previous best
## Run 9 stress 0.06718038 
## ... Procrustes: rmse 1.74935e-05  max resid 0.0001198375 
## ... Similar to previous best
## Run 10 stress 0.0671804 
## ... Procrustes: rmse 3.89994e-05  max resid 0.000203137 
## ... Similar to previous best
## Run 11 stress 0.06718037 
## ... Procrustes: rmse 9.480631e-06  max resid 6.826528e-05 
## ... Similar to previous best
## Run 12 stress 0.0671804 
## ... Procrustes: rmse 3.347634e-05  max resid 0.0001738259 
## ... Similar to previous best
## Run 13 stress 0.06718038 
## ... Procrustes: rmse 2.458315e-05  max resid 0.0001326934 
## ... Similar to previous best
## Run 14 stress 0.06718043 
## ... Procrustes: rmse 4.774093e-05  max resid 0.0003369434 
## ... Similar to previous best
## Run 15 stress 0.06718037 
## ... New best solution
## ... Procrustes: rmse 1.006937e-05  max resid 5.474446e-05 
## ... Similar to previous best
## Run 16 stress 0.06718037 
## ... Procrustes: rmse 8.341003e-06  max resid 4.469793e-05 
## ... Similar to previous best
## Run 17 stress 0.06718038 
## ... Procrustes: rmse 1.913665e-05  max resid 0.0001156102 
## ... Similar to previous best
## Run 18 stress 0.06718038 
## ... Procrustes: rmse 2.062709e-05  max resid 0.0001376011 
## ... Similar to previous best
## Run 19 stress 0.06718042 
## ... Procrustes: rmse 4.620799e-05  max resid 0.0002531826 
## ... Similar to previous best
## Run 20 stress 0.06718038 
## ... Procrustes: rmse 1.105055e-05  max resid 5.091e-05 
## ... Similar to previous best
## *** Solution reached

FGHREC.MDSe #Stress 0.06718037

## 
## Call:
## metaMDS(comm = FGFineHREC, distance = "euclidean", k = 3, trymax = 50) 
## 
## global Multidimensional Scaling using monoMDS
## 
## Data:     wisconsin(sqrt(FGFineHREC)) 
## Distance: euclidean 
## 
## Dimensions: 3 
## Stress:     0.06718037 
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation 
## Species: expanded scores based on 'wisconsin(sqrt(FGFineHREC))'

HREC FG Ordi capscale results

FGHREC.cape <-  capscale(FGFineHREC ~ 1, metaMDSdist = "true", dist="euclidean")

## Square root transformation
## Wisconsin double standardization

summary(FGHREC.cape)  #through third axis: 0.8501

## 
## Call:
## capscale(formula = FGFineHREC ~ 1, distance = "euclidean", metaMDSdist = "true") 
## 
## Partitioning of squared Euclidean distance:
##               Inertia Proportion
## Total           6.019          1
## Unconstrained   6.019          1
## 
## Eigenvalues, and their contribution to the squared Euclidean distance 
## 
## Importance of components:
##                         MDS1   MDS2   MDS3    MDS4    MDS5    MDS6
## Eigenvalue            3.0661 1.1770 0.8735 0.46913 0.24285 0.19024
## Proportion Explained  0.5094 0.1956 0.1451 0.07794 0.04035 0.03161
## Cumulative Proportion 0.5094 0.7050 0.8501 0.92804 0.96839 1.00000
## 
## 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:  4.890002 
## 
## 
## Species scores
## 
##            MDS1    MDS2     MDS3     MDS4     MDS5     MDS6
## IntC3   -2.4731 -0.2543  1.06134  0.06392  0.11493 -0.02362
## IntForb -0.1379 -1.5656 -0.74876 -0.43517 -0.04260  0.22391
## IntLeg  -1.3494  1.2416 -1.10903 -0.15768 -0.05811 -0.07125
## NatC3    1.3249  0.4974  0.71298 -0.86517 -0.31638 -0.07917
## NatC4    1.1877  0.3491  0.08691  0.09796  0.78965  0.21589
## NatForb  0.6490  0.1926  0.13472  0.80202 -0.47173  0.42022
## NatLeg   0.7988 -0.4608 -0.13816  0.49411 -0.01576 -0.68597
## 
## 
## Site scores (weighted sums of species scores)
## 
##         MDS1       MDS2     MDS3      MDS4     MDS5       MDS6
## 1  -0.341993  0.6051149  0.39013  0.757366 -0.94508  0.5187434
## 2  -0.536279  0.7095063 -0.12759  0.426088 -0.48025  0.0773518
## 3   0.067280  0.5670771  0.02267 -0.068251 -0.78091 -1.0197337
## 4  -0.276454  1.0236701 -0.34805  0.264361 -0.71830  0.1022614
## 5  -0.635617 -0.0559180 -0.11060 -0.079964  0.06351  0.0953846
## 6  -0.581047  0.6396176  0.56463 -0.198788 -0.21643 -0.5234182
## 7  -0.759011  0.7399395  0.13194  0.168823  0.80558 -0.4695825
## 8  -0.984300  0.5168476  0.58367  0.223929  0.41745 -0.6244371
## 9   0.491600  0.3580297 -0.08599  0.127921  0.21994  0.2544129
## 10  0.630378  0.1749447 -0.16617 -0.085462  0.02348  0.0701837
## 11  0.520042  0.2968650 -0.37345  0.440425  0.23432 -0.3815938
## 12 -0.503027  0.9698960 -0.38633  0.456131  0.16799  0.0000225
## 13 -0.117834  0.8303065 -0.54944  0.395875  0.78345 -0.2340117
## 14  0.297058  0.7448917 -0.19601  0.446339  0.92435  0.4614374
## 15 -0.384212  0.8268156 -0.02028  0.314440  0.34272  0.0546839
## 16  0.576865  0.6942891  0.08626 -0.583725  0.55364  0.2333310
## 17  0.124296  0.4889522 -0.41342  0.458770 -0.85902  0.6701000
## 18 -0.215750 -0.1180199  1.60924 -0.187161 -0.45086  0.2142310
## 19  0.542917  0.5318792 -0.01450 -0.203891 -0.27540  0.6298422
## 20  0.297101  0.2850012  1.10806 -0.558823 -0.52990  0.3757562
## 21 -0.771941  1.0571836 -0.49256  0.129801 -0.02256 -0.5810300
## 22 -0.188023  0.8717125 -0.08145 -0.400368 -0.65932 -0.2265570
## 23 -0.003138  0.7850481  0.04156  0.330633 -0.50139  0.0750763
## 24  0.402863  0.8588293 -0.14585  0.946180 -0.31975  1.4452557
## 25 -0.611582 -0.6192263 -0.18497 -0.528708  0.31500  0.1877130
## 26  0.200324 -0.2603956 -0.38964 -0.017394  0.66516  0.0729258
## 27 -0.498730 -0.3648123 -0.63806 -0.495860  0.05633  0.2690918
## 28  0.053993  0.0611570 -0.25737 -0.402260 -0.82240 -0.4420439
## 29 -0.422921 -0.0683632  0.22375  0.188431  0.86319  0.4590286
## 30 -0.804446 -0.1635934  0.19700 -0.255499  0.44082 -0.2259464
## 31 -0.296735 -0.3103547  0.61369 -0.752940 -0.10242 -0.7368265
## 32 -0.772690 -0.2127296  0.35141 -0.082892  0.33106 -0.0602091
## 33  0.655068  0.0004515  0.14158 -0.283542  0.47782 -0.1899863
## 34  0.685720 -0.5069318 -0.37133  0.378176  0.91469 -0.3417527
## 35  0.609553 -0.3154842 -0.08661  0.696437  0.40488  0.1776708
## 36 -0.046863  0.2795777 -0.65703  0.345588 -0.73283 -0.7464413
## 37 -0.482409 -0.3445529 -0.29600 -0.133146 -0.11497  0.4707992
## 38  0.808637  0.4239693  0.53606 -0.429577  1.15975  0.0576825
## 39  0.639728 -0.2106303  0.42037 -0.195377  0.37069 -1.0770764
## 40 -0.217463 -0.6226966 -0.26131 -0.160637  0.82720  0.0049138
## 41  0.104539 -1.1178552 -0.35646 -0.231464 -0.49913 -0.0337727
## 42  0.634693 -0.1398666  0.29618 -0.422882 -0.40983  0.0795968
## 43 -0.405579  0.5455805 -0.95021 -0.381250  0.25604 -0.0931178
## 44 -0.011095  0.4196978 -0.43011 -0.941113 -0.10516 -0.0489303
## 45 -0.205934 -0.8683528 -0.35487  0.004607 -0.07562 -0.3859660
## 46  0.763106  0.2386042  0.42869 -1.507209 -0.07024 -0.4969199
## 47 -0.175080 -0.8479240 -0.39163  0.051425 -0.04252 -0.7162420
## 48 -0.120080 -0.3619792 -0.09188 -0.328495 -0.36633 -0.5684727
## 49 -0.559791  0.3102937 -0.73591 -0.453724 -0.01545 -0.2037724
## 50  0.069029  0.2028950 -0.42695 -0.250188  0.33647  0.2153155
## 51 -0.450493 -0.3518767 -0.55318 -0.302315 -0.10272  0.4425461
## 52  0.172260  0.1397124 -0.51620  0.176599  0.40132 -0.4573540
## 53 -0.340574 -0.4507948  0.20746  0.198308 -0.27270  0.0861674
## 54 -0.368670 -0.2858370 -0.09453  0.331286 -0.04473 -0.7985738
## 55 -0.614625 -0.6617656  0.29348 -0.551757  0.31261  0.0998312
## 56 -0.295979 -0.2585083  0.45387 -0.755590 -0.31969  0.0354145
## 57  0.762509 -0.0924416  0.07438  0.429343  0.02837 -0.4568527
## 58  0.621492 -0.4370494 -0.24850  0.867218  0.39534 -0.6434456
## 59  0.773856 -0.2490458 -0.06295  0.858356 -0.08177 -0.0615950
## 60  0.040191 -0.0388397 -0.60291  0.511713 -0.85010 -0.3787544
## 61  0.001430 -0.3669964  0.26509  0.595872  0.03053  0.8530154
## 62  0.852609  0.1517898  0.50791 -0.178671  0.73662  0.8124049
## 63  0.722137  0.0358966  0.29243  0.409910  0.01884 -0.1003637
## 64 -0.395115 -0.8685471  0.22932  0.133332 -0.25394  1.0137572
## 65  0.337436 -0.6687207 -0.35666 -0.113710  0.08144 -0.1186864
## 66  0.559140 -0.4593730 -0.15335  0.173727 -0.77538  0.4263524
## 67 -0.378557 -0.2036229 -0.69881 -0.614667 -0.16424  0.2324834
## 68 -0.030379  0.1109524 -0.48745 -0.606261  0.55965  0.3734686
## 69 -0.052172 -0.8512435 -0.21996  0.477022 -0.44359 -0.1822968
## 70  0.783550  0.0232023  0.34895 -0.637837 -0.20497 -0.3246039
## 71  0.307471 -0.3032227  0.21305  0.150177 -0.29248 -0.4645646
## 72 -0.019452 -0.7497475  0.12557  0.213942 -0.47353 -0.4366402
## 73  0.142729  0.0813114 -0.34989 -0.262625  1.19568  0.8001565
## 74 -0.544781  0.0545525 -0.67244 -0.305511 -0.02785  0.0797486
## 75 -0.493600 -0.5981283 -0.46853 -0.510164  0.10284  0.3862695
## 76  0.100635 -0.0531337 -0.86255 -0.199301  0.22974 -0.1618693
## 77 -0.768989  0.0267100  0.75860  0.480134  0.05759  0.1130958
## 78 -1.075928 -0.0522426  1.17099  0.117472  0.73537 -0.5511507
## 79 -0.658898  0.1423695  0.78106  0.674970  0.17232  0.2967840
## 80 -0.068476 -0.2997077  0.39073 -0.362665  0.78981  0.5982514
## 81  0.640014 -0.3646344 -0.04117  0.477906  0.23063 -0.0353483
## 82  0.669428 -0.0914799  0.01635  1.101283  0.41440 -0.5837211
## 83  0.561039  0.1799911  0.38281 -0.049724  0.09129  0.1284834
## 84  0.007799  0.2658708 -0.75243  0.725035 -0.39688  0.1474089
## 85  0.693022  0.0341143  0.74864 -0.136552 -0.87883  0.1981773
## 86  0.610852  0.2207671  0.56764  0.006362  0.47211 -0.2292890
## 87 -0.264749 -0.5657137  0.20621  0.291604 -0.62163  1.0892554
## 88 -0.541137 -0.7491189  0.81214  0.268203  0.33799  0.7296627
## 89  0.732792 -0.3039675 -0.04354  0.127339 -0.49306  0.3423739
## 90 -0.118637  0.0855776 -0.54018 -1.172021 -0.54488 -0.0837805
## 91 -0.113812 -0.5667304 -0.46206  0.037576 -0.55556  1.2157959
## 92  0.095953 -0.1046620 -0.61113 -0.356795  0.14265  0.4516980
## 93  0.639481  0.6154209  0.72799 -1.201755 -0.49576 -0.3596943
## 94  0.104587  0.2577686  0.73117 -0.373982 -0.65614  0.3818273
## 95 -0.666490 -0.0367072  1.26587  1.109518 -0.06526 -0.3886937
## 96  0.084332 -0.8911059 -0.12815  0.816541 -0.36094 -1.3621231

HREC FG Plot

Functional Group Fit

MaxDead and LitCover are the only two even close to significant

FGHREC.fit1 <- envfit(FGHREC.MDSe ~ TSF + Management + BGCover + GCover + LitCover + LitMean + 
                        MaxDead + MaxLive + VOR_Mean, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                      strata=EnvPatchHREC2$YearLoc)
FGHREC.fit1

## 
## ***VECTORS
## 
##             NMDS1    NMDS2    NMDS3     r2 Pr(>r)  
## BGCover   0.61016 -0.72926 -0.30965 0.0276  0.528  
## GCover   -0.96480 -0.02107 -0.26214 0.0385  0.686  
## LitCover  0.00501  0.71259 -0.70156 0.0875  0.074 .
## LitMean  -0.18004  0.59739 -0.78148 0.0980  0.140  
## MaxDead   0.10794  0.23776 -0.96531 0.0950  0.044 *
## MaxLive   0.20292 -0.92070  0.33338 0.1217  0.994  
## VOR_Mean -0.05782 -0.94687  0.31637 0.0657  0.966  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499
## 
## ***FACTORS:
## 
## Centroids:
##                    NMDS1   NMDS2   NMDS3
## TSF2yr3yr         0.0650 -0.0342 -0.0210
## TSF1yr2yr         0.0234 -0.0397  0.0007
## TSF3yr4yr        -0.1017 -0.0501 -0.0551
## TSFRB             0.0178 -0.0059  0.0133
## TSFUnburned      -0.0283  0.0435  0.0070
## ManagementCattle  0.0408 -0.0008  0.0462
## ManagementSheep  -0.0408  0.0008 -0.0462
## 
## Goodness of fit:
##                r2 Pr(>r)
## TSF        0.0643  0.182
## Management 0.0745  1.000
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

FGHREC.v <- envfit(FGHREC.MDSe ~ LitCover + MaxDead, data = EnvPatchHREC2, choices = c(1:3), perm=499, 
                   strata=EnvPatchHREC2$YearLoc)