Overview

I summarized plant species, structural environmental variables, and plant functional group data to the patches within each pasture for each year. Despite some seperation between speceis and functional groups, there is not a lot of explanation found in our environmental variables.

Summarizing by the patch instead of each transect helped with convergence issues for the species ordination, but does not really change much for the functional group ordination.

Data Transformation

#Veg species ordination summarized by patch

EnvPatch <-
  HRECVeg %>%
  gather(VOR_Mean:MaxDead,LitMean:LitCover, key="species", value="cover") %>%
  #filter(Use=="Heterogeneous" & Management != "PB") %>% 
  mutate(cover=as.numeric(cover)) %>%
  group_by(Year, Pasture, TSF, Management, Patch, species) %>%
  dplyr::summarise(Cover=round(mean(cover),5)) %>%
  ungroup() %>%
  spread(species,Cover)
#96 observations of 12 variables

EnvPatch2 <- EnvPatch %>%
  unite("YearLoc", c("Year", "Pasture"), remove = F ) %>%
  as_tibble()

FGPatch <-
  HRECVeg %>%
  gather(NatForb:Shrub, key="species", value="cover") %>%
  #filter(Use=="Heterogeneous" & Management != "PB") %>% 
  mutate(cover=as.numeric(cover)) %>%
  group_by(Year, Pasture, TSF, Management, Patch, species) %>%
  dplyr::summarise(Cover=round(mean(cover),5)) %>%
  ungroup() %>%
  spread(species,Cover)

FGFine <- FGPatch  %>% select(IntC3:IntLeg, NatC3:NatShrub)
FGMedium <- FGPatch  %>% select(C3,C4, Forb:Shrub)

SpeciesPatch <-
  HRECVeg %>%
  gather(ACMI:VIAM, key="species", value="cover") %>%
  #filter(Use=="Heterogeneous" & Management != "PB") %>% 
  mutate(cover=as.numeric(cover)) %>%
  group_by(Year, Pasture, TSF, Management, Patch, species) %>%
  dplyr::summarise(Cover=round(mean(cover),5)) %>%
  ungroup() %>%
  spread(species,Cover)
#96 observations of 105 variables

#relative abundance and filtering
SpeciesFull <- SpeciesPatch %>% select(ACMI:VIAM)
sum(SpeciesFull)
## [1] 6389.924
RelAbund<- round(colSums(SpeciesFull)/sum(SpeciesFull),5)*100
RAD <- as.data.frame(RelAbund)
RAD <- tibble::rownames_to_column(RAD, "Species")
sum(RAD$RelAbund)
## [1] 99.998
#RAD01 <- filter(RAD, RelAbund >= 0.01) #68 species
#sum(RAD01$RelAbund)

RAD05 <- filter(RAD, RelAbund >= 0.05) #42 species
sum(RAD05$RelAbund)
## [1] 99.359
PS05 <- SpeciesPatch %>% 
  select(ACMI, AGCR, AMPS, ARFR, ARLU, ASSY, BOGR, BRIN, BUNCH, CIAR, CIUN, COAR, DESO, DISP, ELIN, ELRE, ELTR, ERAS, GLLE, GRSQ, HEMA, 
         HEPA, HOJU, IVAX, LEDE, LOUN, MEOF, MESA, NAVI, PASM, POPR, PSAR, RUOC, SEDGE, SOAR, SPHE, SPPE, SYER, TAOF, THAR, TYLA, VIAM)

Relative Abundance

Intermediate wheatgrass (ELIN), Kentucky bluegrass (POPR), smooth brome (BRIN), alfalfa (MESA), crested wheatgrass (AGCR) make up >80% of the plant community across years at HREC. Inland saltgrass (DISP) is our most abundant native species on transects, and it is a warm season grass!

I think this is why people try to transform their plant data to downweight super abundant species?

Species Ordi Setup

#Euclidean
PS.MDSe <- metaMDS(PS05, k=3, trymax=50, distance="euclidean") 
## Wisconsin double standardization
## Run 0 stress 0.13202 
## Run 1 stress 0.1328803 
## Run 2 stress 0.1329638 
## Run 3 stress 0.1455798 
## Run 4 stress 0.1328656 
## Run 5 stress 0.1328831 
## Run 6 stress 0.1320368 
## ... Procrustes: rmse 0.004813867  max resid 0.03918211 
## Run 7 stress 0.1320227 
## ... Procrustes: rmse 0.000493184  max resid 0.002831365 
## ... Similar to previous best
## Run 8 stress 0.14571 
## Run 9 stress 0.1329082 
## Run 10 stress 0.1320388 
## ... Procrustes: rmse 0.002047648  max resid 0.01257797 
## Run 11 stress 0.1473344 
## Run 12 stress 0.1328599 
## Run 13 stress 0.1328596 
## Run 14 stress 0.1328889 
## Run 15 stress 0.1328593 
## Run 16 stress 0.1328956 
## Run 17 stress 0.145635 
## Run 18 stress 0.1333782 
## Run 19 stress 0.1320164 
## ... New best solution
## ... Procrustes: rmse 0.002597186  max resid 0.01417382 
## Run 20 stress 0.1328584 
## Run 21 stress 0.1329112 
## Run 22 stress 0.1328607 
## Run 23 stress 0.1320145 
## ... New best solution
## ... Procrustes: rmse 0.0009550429  max resid 0.006558012 
## ... Similar to previous best
## *** Solution reached
PS.MDSe #Stress 0.1320143
## 
## Call:
## metaMDS(comm = PS05, distance = "euclidean", k = 3, trymax = 50) 
## 
## global Multidimensional Scaling using monoMDS
## 
## Data:     wisconsin(PS05) 
## Distance: euclidean 
## 
## Dimensions: 3 
## Stress:     0.1320145 
## Stress type 1, weak ties
## Two convergent solutions found after 23 tries
## Scaling: centring, PC rotation 
## Species: expanded scores based on 'wisconsin(PS05)'
PS.cape <-  capscale(PS05 ~ 1, metaMDSdist = "true", dist="euclidean")
## Wisconsin double standardization
summary(PS.cape)  #through third axis: 0.49461; use euclidean
## 
## Call:
## capscale(formula = PS05 ~ 1, distance = "euclidean", metaMDSdist = "true") 
## 
## Partitioning of squared Euclidean distance:
##               Inertia Proportion
## Total           8.231          1
## Unconstrained   8.231          1
## 
## Eigenvalues, and their contribution to the squared Euclidean distance 
## 
## Importance of components:
##                         MDS1   MDS2    MDS3   MDS4    MDS5    MDS6    MDS7
## Eigenvalue            1.9891 1.2730 0.80888 0.6058 0.43279 0.38528 0.28294
## Proportion Explained  0.2417 0.1547 0.09827 0.0736 0.05258 0.04681 0.03438
## Cumulative Proportion 0.2417 0.3963 0.49461 0.5682 0.62079 0.66760 0.70198
##                          MDS8   MDS9   MDS10   MDS11   MDS12   MDS13   MDS14
## Eigenvalue            0.26579 0.2535 0.23250 0.17023 0.15044 0.14007 0.13634
## Proportion Explained  0.03229 0.0308 0.02825 0.02068 0.01828 0.01702 0.01656
## Cumulative Proportion 0.73427 0.7651 0.79332 0.81400 0.83228 0.84930 0.86586
##                        MDS15   MDS16   MDS17   MDS18   MDS19   MDS20    MDS21
## Eigenvalue            0.1251 0.10659 0.09528 0.08953 0.08404 0.07325 0.063735
## Proportion Explained  0.0152 0.01295 0.01158 0.01088 0.01021 0.00890 0.007743
## Cumulative Proportion 0.8811 0.89401 0.90559 0.91647 0.92668 0.93558 0.943320
##                          MDS22    MDS23    MDS24   MDS25    MDS26    MDS27
## Eigenvalue            0.055670 0.052719 0.048926 0.04387 0.038015 0.031628
## Proportion Explained  0.006764 0.006405 0.005944 0.00533 0.004619 0.003843
## Cumulative Proportion 0.950084 0.956489 0.962433 0.96776 0.972382 0.976225
##                          MDS28    MDS29    MDS30    MDS31    MDS32    MDS33
## Eigenvalue            0.028659 0.025307 0.022463 0.020933 0.020118 0.016937
## Proportion Explained  0.003482 0.003075 0.002729 0.002543 0.002444 0.002058
## Cumulative Proportion 0.979706 0.982781 0.985510 0.988053 0.990498 0.992556
##                          MDS34    MDS35    MDS36    MDS37     MDS38     MDS39
## Eigenvalue            0.013857 0.011695 0.011161 0.008806 0.0058424 0.0052835
## Proportion Explained  0.001683 0.001421 0.001356 0.001070 0.0007098 0.0006419
## Cumulative Proportion 0.994239 0.995660 0.997016 0.998086 0.9987956 0.9994375
##                           MDS40     MDS41
## Eigenvalue            0.0032539 0.0013758
## Proportion Explained  0.0003953 0.0001672
## Cumulative Proportion 0.9998328 1.0000000
## 
## 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:  5.288007 
## 
## 
## Species scores
## 
##           MDS1       MDS2      MDS3       MDS4      MDS5       MDS6
## ACMI   0.12644 -0.0682646  0.003117  0.1134500 -0.064662 -0.0370309
## AGCR  -0.38467  0.3520831  0.991555 -0.9936746  0.029225  0.0479233
## AMPS   0.15915 -0.1114155 -0.006072  0.0444722  0.077674 -0.1223066
## ARFR   0.15891 -0.0232596 -0.019883  0.0193909  0.035016  0.0079395
## ARLU   0.26411 -0.0266081 -0.002343 -0.0302531 -0.239009  0.0024125
## ASSY   0.18408 -0.0394445 -0.081952 -0.0542122 -0.026740 -0.0596683
## BOGR   0.06985  0.0150526 -0.036369  0.0199861 -0.101919  0.0747819
## BRIN  -0.15105  0.0435066  0.705271  0.6296708 -0.228407 -0.6708321
## BUNCH  0.03114  0.0284917 -0.121779 -0.0390006  0.009261  0.0893624
## CIAR   0.05941  0.0110498 -0.104533 -0.0931664  0.146411 -0.0029377
## CIUN   0.03652  0.1607450  0.122424 -0.0589331 -0.048959 -0.0018704
## COAR  -0.01162 -0.1171744  0.190781  0.0949442  0.478595 -0.3325773
## DESO  -0.02484  0.0084502 -0.150720 -0.0423971  0.123724 -0.0886967
## DISP   0.28989 -0.0007556 -0.045051  0.0411204 -0.308482  0.0166295
## ELIN  -2.12588  0.1587268 -0.626879 -0.1636673 -0.309034 -0.1847789
## ELRE   0.02006  0.2326015 -0.514967 -0.1266967  0.347234 -0.0418806
## ELTR   0.16729 -0.2071671 -0.107340  0.1711176 -0.189095  0.3829351
## ERAS   0.05802  0.0523034 -0.021207  0.0581710 -0.068599  0.0632220
## GLLE   0.12771 -0.0627331 -0.059524 -0.0418309 -0.022600 -0.0454268
## GRSQ   0.14467 -0.0732837 -0.027061  0.0004904  0.032030 -0.0403419
## HEMA   0.11342 -0.0443905 -0.057818 -0.0600386  0.022132 -0.0243095
## HEPA   0.05655  0.0075101 -0.027024 -0.0149337 -0.019472  0.0073997
## HOJU   0.38740 -0.0583795 -0.017237 -0.0828067 -0.292606  0.1056127
## IVAX   0.19062 -0.0356724 -0.043126  0.0201226 -0.219728  0.0301051
## LEDE   0.04677 -0.0362996 -0.087791  0.0532297  0.187843  0.0202382
## LOUN   0.06810 -0.0874974 -0.038087  0.0676352  0.126566 -0.0593465
## MEOF  -0.05655 -0.1355871  0.057304 -0.0772354  0.028454 -0.1035977
## MESA  -0.12419  1.7794953  0.112843  0.4642593  0.163183  0.2648716
## NAVI  -0.10634 -0.0107588  0.029799 -0.0582801 -0.155811 -0.1417225
## PASM   0.25325 -0.1351863 -0.002284 -0.0045167 -0.339485  0.0216695
## POPR  -1.11888 -0.7862413  0.601820  0.5025336  0.208207  0.5527162
## PSAR   0.11016 -0.0890219  0.042420 -0.1028372  0.169479  0.2038352
## RUOC   0.08893  0.0021333  0.019544 -0.0848334 -0.013651  0.0551014
## SEDGE  0.10775 -0.0124286 -0.031599 -0.0504675 -0.027289 -0.0158125
## SOAR   0.15944  0.0341379 -0.159465 -0.0827863  0.056362 -0.0235792
## SPHE   0.26111 -0.1018917 -0.050636  0.0807331 -0.130843  0.1410647
## SPPE   0.13236 -0.0191687 -0.047301 -0.1351729  0.070195 -0.0008228
## SYER   0.14898 -0.0774394 -0.047143  0.0041029  0.008555 -0.0376441
## TAOF  -0.06818 -0.3552390 -0.215866 -0.0096230  0.514258 -0.1426986
## THAR   0.03641 -0.0508835 -0.053167 -0.0116396 -0.013854  0.0428898
## TYLA   0.04676 -0.0107399 -0.020570 -0.0198954 -0.011093  0.0077053
## VIAM   0.06694 -0.1093553 -0.052085  0.0534682 -0.003062  0.0394662
## 
## 
## Site scores (weighted sums of species scores)
## 
##        MDS1      MDS2       MDS3      MDS4     MDS5       MDS6
## 1  -0.15159  0.458397  1.0376504 -1.275761 -0.20759  0.0898347
## 2  -0.09811  0.687661  1.2642355 -0.377423  0.12435  0.3839851
## 3   0.24123  0.553989  0.9895951 -0.740891 -0.20676  0.3790377
## 4   0.16574  0.928786  0.5566126 -0.366808  0.11483  0.7983300
## 5  -0.65022  0.287706  0.1056744 -0.590429 -0.02650  0.5169005
## 6  -0.52060  0.298742 -0.0856328 -0.028574 -0.74152 -0.6264917
## 7  -0.76657  0.670850  0.0437947 -0.470465 -0.37880  0.2567427
## 8  -1.11353  0.087073 -0.1690959  0.221404 -0.42472  0.4238102
## 9   0.67111  0.126255 -0.2107580 -0.007277 -0.37491 -0.0433253
## 10  0.74477  0.006885 -0.4308218 -0.331223 -0.16594  0.1628879
## 11  0.71267  0.214288 -0.3055547 -0.066129 -0.34500  0.0679113
## 12 -0.21803  0.922201  0.4987497  1.198830  0.26333  0.8628096
## 13  0.11214  1.395868  0.3692280  0.880001 -0.05411  0.1160795
## 14  0.43615  0.801724 -0.0630379  0.579181 -0.80704 -0.3248379
## 15 -0.54768  1.039056 -0.4653226  0.441744 -0.71783 -0.5087258
## 16  0.82916  0.336429  0.0863337 -0.217432 -1.23313  0.0250003
## 17  0.44647  0.849968  0.1288913  0.401270 -0.07347 -0.4347603
## 18 -1.28559 -0.750302 -0.5533766  0.159249 -0.38611  1.4072169
## 19  0.79743  0.206182 -0.2173242  0.065067 -0.84327  0.0834914
## 20 -0.21126 -0.492810 -0.4871208 -0.001099 -0.42617  1.3661650
## 21 -0.30478  1.218969  0.0219175  1.033877 -0.14206  0.3601239
## 22 -0.03121  1.088340  0.3177822  1.184174 -0.24184 -0.1461526
## 23  0.13616  0.663681  0.3034256  1.009479 -0.36410 -0.2278251
## 24  0.61033  0.486767 -0.1541323  0.436219 -0.45234  0.5360519
## 25 -0.06219  0.282649  1.4060591 -1.357889  0.20442 -0.8261243
## 26  0.04097 -0.068239  0.9448534 -1.040823  0.46461  0.7152944
## 27 -0.04399  0.242462  1.1467146 -0.021090  0.41579 -0.6536352
## 28  0.07296  0.087392  1.0703225 -0.803003  0.25120  0.6515779
## 29 -0.69872  0.107486  0.1467432 -1.010668 -0.24337 -0.1182188
## 30 -0.98071 -0.023859 -0.2615577 -0.359913 -0.15467  0.2000893
## 31 -0.38536 -0.244115 -0.2248944 -0.027843 -0.54812 -0.7467465
## 32 -0.84928  0.096942 -0.5170714 -0.212588 -0.39491 -0.7288076
## 33  0.69748 -0.112490 -0.4131802 -0.156721 -0.05501 -0.1342000
## 34  0.70744 -0.174982 -0.4900288 -0.530062  0.27019 -0.2514902
## 35  0.61866 -0.341836 -0.2483573 -0.139839  0.25680 -0.0195974
## 36  0.27860  0.174908  0.1618751  0.614287  0.57936  0.2236110
## 37 -0.33691 -0.409895  0.5582544  0.484846  0.48269  0.5421018
## 38  0.69730 -0.348722  0.0351722 -0.034769 -1.25428  0.1933544
## 39  0.39354 -0.461837  0.0009796  0.264222 -0.75658 -0.1667649
## 40 -0.41707 -0.337397  0.1324703  0.410443  0.25350 -0.1263260
## 41  0.29155 -0.481303  0.3908298  0.613172  1.05210 -1.3224060
## 42  0.69089 -0.302312 -0.1494571 -0.042721 -0.36357 -0.2924761
## 43 -0.23868  1.748975 -1.1538868  0.126892  0.38740  0.4062767
## 44  0.18724  0.937988 -1.2658876 -0.385081  0.64291  0.3527838
## 45 -0.34036 -0.359746 -0.4236622  0.160917  0.91541  0.0217029
## 46  0.41330 -0.548485 -0.2128335  0.389032 -0.29104  1.2750354
## 47 -0.26217 -0.612240 -0.1016482  0.430202  0.88896  0.1170542
## 48 -0.33931 -0.346500  0.4932568  0.992881  0.21564 -0.1427589
## 49 -0.09061  0.437552  0.3428641 -0.553253  0.01415 -0.5974028
## 50  0.09115  0.077072  0.4382180 -0.833798  0.24737  0.2054264
## 51 -0.16330  0.123289  0.3385511 -0.295866  0.52160 -0.5293901
## 52  0.28204  0.294908  0.7372853 -0.733026  0.53672  0.3269978
## 53 -0.55793 -0.274617  0.1401189 -1.061478  0.23451  0.2026847
## 54 -0.55173 -0.342057 -0.2091544 -0.264791 -0.02228 -0.0212220
## 55 -0.59861 -0.430781 -0.3487061 -0.201876  0.07493 -0.9901971
## 56 -0.33583 -0.339292 -0.0419307 -0.019136 -0.27492 -0.3409974
## 57  0.78151 -0.202906 -0.4709810 -0.419154 -0.10505 -0.1260308
## 58  0.69982 -0.234586 -0.4838223 -0.435432  0.15874 -0.3432935
## 59  0.74668 -0.339013 -0.3689683 -0.387860  0.02387 -0.0185165
## 60  0.19888  0.114159 -0.1445568  0.467104  0.72114 -0.1106267
## 61  0.08459 -0.339845  0.6667101  0.313635  0.06681 -0.7234842
## 62  0.75088 -0.349164 -0.0874204 -0.263883 -0.99625  0.0008369
## 63  0.61603 -0.410889 -0.1619159  0.003039 -0.79190 -0.1547065
## 64 -0.42002 -0.551907 -0.1039204  0.398592  0.33489 -0.2666892
## 65  0.40660 -0.431987  0.0401524  0.271926  0.72386 -0.6502251
## 66  0.63757 -0.434129 -0.3117394 -0.165722  0.50039 -0.4434773
## 67 -0.08376  0.589348 -1.3672226 -0.496727  0.81292 -0.3482097
## 68  0.05414  0.665999 -1.2713647 -0.503868  0.68068 -0.1489830
## 69 -0.27170 -0.532677 -0.6979583 -0.160770  0.80467 -0.1894052
## 70  0.53057 -0.566291 -0.2816691  0.175795 -0.24320  0.7120866
## 71 -0.00148 -0.503491 -0.0988437  0.307524  0.14317 -0.1210372
## 72 -0.33977 -0.669316 -0.2297912  0.263964  0.55316  0.0080775
## 73 -0.07314 -0.055474  0.3536795 -0.255289  0.35116  0.4827306
## 74 -0.25250  0.455637  0.5800062  0.683744  0.11415 -0.4550252
## 75 -0.42575 -0.241571  0.5298223  0.895638  0.33514 -0.5373145
## 76  0.13481  0.084828  0.6538414  0.216897  0.71214  0.4995876
## 77 -1.10001 -0.285113 -0.2761483  0.052865 -0.74737 -0.4454416
## 78 -1.35290 -0.044195 -0.7364622 -0.307632 -1.18174 -1.0561565
## 79 -1.03055  0.042372  0.1410260 -1.267775 -0.63116 -0.1149520
## 80 -0.36596 -0.619150  0.2116055  0.408965  0.16309  0.2357191
## 81  0.64615 -0.215063 -0.2839807 -0.271083 -0.10440 -0.5037618
## 82  0.61487 -0.374499 -0.1515904 -0.120859 -0.10739  0.0188625
## 83  0.63996 -0.225582  0.0074461  0.012350 -0.59662 -0.4492852
## 84  0.07482  0.355451  0.0834080  0.658211  0.48220  0.4699095
## 85  0.53839 -0.492956  0.1773421 -0.045449 -0.49991  0.4062411
## 86  0.40916 -0.486831  0.0436257  0.241769 -0.99517 -0.0806322
## 87 -0.05442 -0.593066  0.8751137  0.526201  0.32038 -0.2934964
## 88 -0.58486 -0.535630  0.3471671  0.494696 -0.04932 -0.3302537
## 89  0.56453 -0.583302 -0.1980090 -0.085404  0.44861  0.1093732
## 90  0.07119  0.731578 -1.1413701 -0.171485  0.94445  0.1797912
## 91  0.32621 -0.402307  0.3604030  0.485236  0.53874 -1.3460555
## 92  0.08013  0.377028 -0.9832655 -0.366747  0.93350 -0.0792979
## 93  0.28482 -0.771061  0.1348122  0.500173 -0.42297  1.4100533
## 94 -0.38613 -0.671123  0.2104271  0.628513 -0.37396  0.3361879
## 95 -1.19204 -0.592609 -0.3775737  0.319189 -0.49126  0.2985595
## 96 -0.17186 -0.798289 -0.1920398 -0.138531  1.02898  1.2188521

Euclidean has the best stress values and proportion explained through third axis. I’m using the metaMDS for initial plotting to avoid the scaling issues with capscale

Species Ordi Plot

Species Env Fit

Patch.fit1 <- envfit(PS.MDSe ~ TSF + Management + BGCover + GCover + LitCover + LitMean + 
                     MaxDead + MaxLive + VOR_Mean, data = EnvPatch2, choices = c(1:3), perm=499, 
                   strata=EnvPatch2$YearLoc)
Patch.fit1 #BGCov meets 0.05 criteria; VOR_Mean has 0.072
## 
## ***VECTORS
## 
##             NMDS1    NMDS2    NMDS3     r2 Pr(>r)  
## BGCover   0.26083 -0.95599 -0.13437 0.0743  0.032 *
## GCover   -0.99080  0.07846  0.11029 0.0176  0.824  
## LitCover -0.23600  0.96749  0.09095 0.0066  0.662  
## LitMean  -0.71749  0.29932 -0.62898 0.0131  0.758  
## MaxDead  -0.10183 -0.95399  0.28200 0.0293  0.818  
## MaxLive   0.07754 -0.14079  0.98700 0.1857  0.490  
## VOR_Mean -0.12322  0.01477  0.99227 0.1663  0.050 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499
## 
## ***FACTORS:
## 
## Centroids:
##                    NMDS1   NMDS2   NMDS3
## TSF2yr3yr         0.0177 -0.0578 -0.0003
## TSF1yr2yr         0.0051 -0.0333 -0.0035
## TSF3yr4yr        -0.0787 -0.0591 -0.0572
## TSFRB             0.0173 -0.0028 -0.0001
## TSFUnburned      -0.0069  0.0477  0.0115
## ManagementCattle  0.0507  0.0261 -0.0014
## ManagementSheep  -0.0507 -0.0261  0.0014
## 
## Goodness of fit:
##                r2 Pr(>r)
## TSF        0.0633  0.222
## Management 0.0830  1.000
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499
#whittle down to signficant variable (BGCover) and friend who is depending on the run (VOR)
Patch.fit2 <- envfit(PS.MDSe ~ BGCover + VOR_Mean, data = EnvPatch2, choices = c(1:3), perm=499, 
                   strata=EnvPatch2$YearLoc)
Patch.fit2
## 
## ***VECTORS
## 
##             NMDS1    NMDS2    NMDS3     r2 Pr(>r)  
## BGCover   0.26083 -0.95599 -0.13437 0.0743  0.048 *
## VOR_Mean -0.12322  0.01477  0.99227 0.1663  0.080 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

Species Fit Plot

So…not a whole lot happening here. I do think it is interesting that the very abundant species are essentially located in the middle of the first axis (MESA, POPR, BRIN, AGCR) or left of it (ELIN).

The large cluster of species opposite of VOR makes it seem like TSF should be significant. The r2 (0.0632) and Pr (0.236) are not great.

Species TSF Spider

Functional Group Ordi Setup

#Euclidean
FG.MDSe <- metaMDS(FGFine, k=3, trymax=50, distance="euclidean") 
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.07572115 
## Run 1 stress 0.07572108 
## ... New best solution
## ... Procrustes: rmse 8.12194e-06  max resid 4.671346e-05 
## ... Similar to previous best
## Run 2 stress 0.07571917 
## ... New best solution
## ... Procrustes: rmse 0.0005329933  max resid 0.003192418 
## ... Similar to previous best
## Run 3 stress 0.07572037 
## ... Procrustes: rmse 0.0004653725  max resid 0.002890482 
## ... Similar to previous best
## Run 4 stress 0.07572208 
## ... Procrustes: rmse 0.0002748559  max resid 0.002098554 
## ... Similar to previous best
## Run 5 stress 0.07571878 
## ... New best solution
## ... Procrustes: rmse 0.0001540209  max resid 0.001034023 
## ... Similar to previous best
## Run 6 stress 0.07572016 
## ... Procrustes: rmse 0.0002990332  max resid 0.001762028 
## ... Similar to previous best
## Run 7 stress 0.09971441 
## Run 8 stress 0.07572034 
## ... Procrustes: rmse 0.0003163903  max resid 0.00180625 
## ... Similar to previous best
## Run 9 stress 0.07571901 
## ... Procrustes: rmse 0.0001027765  max resid 0.0006800022 
## ... Similar to previous best
## Run 10 stress 0.075722 
## ... Procrustes: rmse 0.0004466495  max resid 0.002653793 
## ... Similar to previous best
## Run 11 stress 0.07571899 
## ... Procrustes: rmse 0.000115765  max resid 0.0008104507 
## ... Similar to previous best
## Run 12 stress 0.09971441 
## Run 13 stress 0.07572002 
## ... Procrustes: rmse 0.0002991935  max resid 0.002080397 
## ... Similar to previous best
## Run 14 stress 0.07572023 
## ... Procrustes: rmse 0.0003245865  max resid 0.002234769 
## ... Similar to previous best
## Run 15 stress 0.07571921 
## ... Procrustes: rmse 0.0001648208  max resid 0.001315295 
## ... Similar to previous best
## Run 16 stress 0.07571911 
## ... Procrustes: rmse 0.0001413546  max resid 0.0009322189 
## ... Similar to previous best
## Run 17 stress 0.101092 
## Run 18 stress 0.07572032 
## ... Procrustes: rmse 0.0003134508  max resid 0.001816179 
## ... Similar to previous best
## Run 19 stress 0.1010923 
## Run 20 stress 0.07572018 
## ... Procrustes: rmse 0.0002999798  max resid 0.001854704 
## ... Similar to previous best
## *** Solution reached
FG.MDSe #Stress 0.07571887
## 
## Call:
## metaMDS(comm = FGFine, distance = "euclidean", k = 3, trymax = 50) 
## 
## global Multidimensional Scaling using monoMDS
## 
## Data:     wisconsin(sqrt(FGFine)) 
## Distance: euclidean 
## 
## Dimensions: 3 
## Stress:     0.07571878 
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation 
## Species: expanded scores based on 'wisconsin(sqrt(FGFine))'
FG.cape <-  capscale(FGFine ~ 1, metaMDSdist = "true", dist="euclidean")
## Square root transformation
## Wisconsin double standardization
summary(FG.cape)  #through third axis: 0.8068; use euclidean
## 
## Call:
## capscale(formula = FGFine ~ 1, distance = "euclidean", metaMDSdist = "true") 
## 
## Partitioning of squared Euclidean distance:
##               Inertia Proportion
## Total            6.31          1
## Unconstrained    6.31          1
## 
## Eigenvalues, and their contribution to the squared Euclidean distance 
## 
## Importance of components:
##                         MDS1   MDS2   MDS3    MDS4    MDS5    MDS6    MDS7
## Eigenvalue            3.0698 1.1432 0.8782 0.50264 0.31509 0.22516 0.17613
## Proportion Explained  0.4865 0.1812 0.1392 0.07966 0.04993 0.03568 0.02791
## Cumulative Proportion 0.4865 0.6676 0.8068 0.88647 0.93640 0.97209 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:  4.948125 
## 
## 
## Species scores
## 
##             MDS1     MDS2    MDS3     MDS4     MDS5     MDS6
## IntC3    -2.5271 -0.29574 -0.9972  0.15169 -0.04934 -0.10608
## IntForb  -0.2570 -1.49254  0.8368 -0.36618 -0.18512  0.05774
## IntLeg   -1.2822  1.20961  0.9193 -0.44439  0.24483  0.04102
## NatC3     1.2442  0.22121 -0.8424 -0.84671 -0.20415  0.31250
## NatC4     1.1146  0.20280 -0.1847 -0.07024  0.16637 -0.74590
## NatForb   0.5836  0.08325 -0.1591  0.57495  0.53015  0.44586
## NatLeg    0.7230 -0.49246  0.1532  0.35484  0.31834 -0.05029
## NatShrub  0.4011  0.56387  0.2741  0.64604 -0.82108  0.04514
## 
## 
## Site scores (weighted sums of species scores)
## 
##         MDS1     MDS2     MDS3       MDS4     MDS5      MDS6
## 1  -0.357114  0.58683 -0.46103  0.4659040  0.86043  0.974682
## 2  -0.545204  0.74033  0.03669  0.1011135  0.72536  0.472427
## 3   0.265373  0.68855  0.13042  0.5908626 -1.23259  0.678552
## 4  -0.276448  1.02792  0.18916 -0.1897130  0.88961  0.716042
## 5  -0.395027  0.20235  0.27199  0.5148036 -1.14186  0.028855
## 6  -0.597516  0.62292 -0.65031 -0.2637010  0.08869  0.218621
## 7  -0.770318  0.78030 -0.22092 -0.0433457  0.36889 -0.876631
## 8  -1.004303  0.55878 -0.61794  0.1741706  0.16386 -0.461612
## 9   0.528507  0.37955  0.08723  0.2164612 -0.19098 -0.180268
## 10  0.647493  0.19645  0.16238  0.0416178 -0.26508 -0.002929
## 11  0.589568  0.42359  0.41009  0.6793952 -0.54232 -0.188810
## 12 -0.308487  1.04506  0.35453  0.4896962 -0.16183 -0.130419
## 13  0.006249  0.89274  0.47789  0.3418032 -0.01666 -0.731627
## 14  0.372132  0.76015  0.16457  0.4199637 -0.03915 -0.823261
## 15 -0.391719  0.83214 -0.10096 -0.0261141  0.67013 -0.386277
## 16  0.567297  0.56901 -0.26817 -0.8453727  0.24750 -0.568502
## 17  0.114716  0.46562  0.31325  0.0426515  0.92775  0.876507
## 18 -0.256524 -0.22895 -1.59145  0.0797095 -0.35638  0.530061
## 19  0.613942  0.58673  0.09373  0.2988155 -0.95109  0.331682
## 20  0.269800  0.13076 -1.19599 -0.4930833 -0.16642  0.611331
## 21 -0.770913  1.12806  0.35134 -0.2658412  0.66969 -0.033458
## 22  0.150269  0.96636  0.25410  0.6188012 -1.83489  0.615426
## 23  0.250905  0.88227  0.14839  0.9479413 -1.28906  0.482546
## 24  0.465285  0.84537  0.11266  0.7418691  0.39462  0.340769
## 25 -0.648345 -0.56374  0.26633 -0.3337501 -0.52780 -0.286958
## 26  0.175303 -0.27901  0.37842 -0.1598478  0.22051 -0.719519
## 27 -0.526015 -0.29939  0.67108 -0.4698127 -0.24438 -0.031310
## 28  0.034840  0.03117  0.20427 -0.5115259  0.11536  0.834922
## 29 -0.451595 -0.05626 -0.21292  0.1535391  0.10745 -0.885238
## 30 -0.835208 -0.11332 -0.15943 -0.1344416 -0.30390 -0.445808
## 31 -0.332284 -0.35156 -0.59094 -0.4986868 -0.62146  0.121284
## 32 -0.805726 -0.17222 -0.30187  0.0432348 -0.23115 -0.327598
## 33  0.632064 -0.11456 -0.21796 -0.4156311  0.16693 -0.523509
## 34  0.657677 -0.56548  0.37632  0.1653398  0.49704 -1.036245
## 35  0.582495 -0.38501  0.08138  0.4377648  0.73067 -0.483674
## 36 -0.057357  0.29848  0.59146 -0.0117889  0.76730  0.672032
## 37 -0.512115 -0.29710  0.33913 -0.1187699 -0.06073  0.147467
## 38  0.790388  0.25601 -0.68454 -0.6013173  0.15911 -1.212812
## 39  0.611212 -0.33279 -0.45881 -0.2336973  0.07310 -0.455676
## 40 -0.252194 -0.59644  0.32607 -0.1006006 -0.18527 -0.866606
## 41  0.062108 -1.11761  0.47536 -0.0861326 -0.25242  0.516923
## 42  0.607357 -0.26670 -0.34958 -0.4606184  0.02378  0.434486
## 43 -0.411012  0.60464  0.84230 -0.6847256  0.30655 -0.286631
## 44 -0.022955  0.39064  0.31041 -1.0730828 -0.11106  0.129968
## 45 -0.243734 -0.83097  0.46063  0.0850857 -0.10110  0.046842
## 46  0.742217  0.06125 -0.57212 -1.4451991 -0.55240  0.104366
## 47 -0.211613 -0.80995  0.49311  0.1078189 -0.04551 -0.010828
## 48 -0.149683 -0.36975  0.11399 -0.2863427 -0.12923  0.359630
## 49 -0.572095  0.37603  0.67358 -0.6213268  0.08058  0.007678
## 50  0.053076  0.18462  0.35037 -0.4609749  0.25291 -0.356391
## 51 -0.478110 -0.29480  0.58785 -0.3058963 -0.10746  0.134811
## 52  0.228649  0.22933  0.50311  0.2108024 -0.11157 -0.405197
## 53 -0.375622 -0.44167 -0.14207  0.2522366  0.04530  0.283387
## 54 -0.396666 -0.25138  0.13615  0.2691815  0.25493 -0.020908
## 55 -0.656382 -0.63575 -0.20015 -0.2557811 -0.67489 -0.274590
## 56 -0.329956 -0.29417 -0.44017 -0.5439841 -0.55140  0.386949
## 57  0.739684 -0.19944 -0.12518  0.1690036  0.67473 -0.111795
## 58  0.618524 -0.37356  0.31273  0.7813285  0.32410 -0.466973
## 59  0.764446 -0.21989  0.11622  0.7637539  0.35454  0.033158
## 60  0.022770 -0.02439  0.58405  0.2057717  0.76099  0.808060
## 61 -0.032236 -0.39501 -0.22278  0.5163822  0.40941 -0.022173
## 62  0.842248  0.12145 -0.44023  0.0800579 -0.50124 -0.615023
## 63  0.699403 -0.08490 -0.35725  0.1628761  0.65355 -0.075130
## 64 -0.440174 -0.84969 -0.10068  0.3189483 -0.19757  0.325068
## 65  0.304675 -0.69961  0.39678 -0.1423494  0.04805 -0.112693
## 66  0.601466 -0.23011  0.30710  0.6468034 -0.84319  0.740075
## 67 -0.401684 -0.15468  0.69780 -0.6369172 -0.19425  0.197754
## 68 -0.048541  0.10281  0.41996 -0.7267747 -0.04992 -0.559267
## 69 -0.090070 -0.83257  0.32868  0.4714792  0.25320  0.416123
## 70  0.759685 -0.12971 -0.44068 -0.6914910 -0.03250  0.205497
## 71  0.277811 -0.37013 -0.20967  0.0849285  0.25783  0.257372
## 72 -0.058157 -0.76381 -0.03586  0.2860720  0.05053  0.455594
## 73  0.214260  0.18640  0.36597  0.0059924 -0.63251 -1.036512
## 74 -0.562985  0.12230  0.65133 -0.4233989  0.06050  0.031877
## 75 -0.527253 -0.53943  0.53715 -0.3908452 -0.39428 -0.065972
## 76  0.177174  0.09926  0.83977  0.0000452 -0.47573 -0.199511
## 77 -0.800377  0.04549 -0.72415  0.5145081  0.16587 -0.054712
## 78 -1.113169 -0.02603 -1.11235  0.3533705 -0.33138 -0.760466
## 79 -0.687955  0.14864 -0.76537  0.6317634  0.36335 -0.178997
## 80 -0.102940 -0.34529 -0.37896 -0.2579027 -0.29365 -0.772572
## 81  0.611800 -0.44395  0.03690  0.2760517  0.57245 -0.299994
## 82  0.710009  0.11996  0.16927  1.3067016 -0.53060 -0.353477
## 83  0.539241  0.05845 -0.46966 -0.2145677  0.33758 -0.106991
## 84 -0.002918  0.29133  0.69297  0.2760247  1.03160  0.344866
## 85  0.665675 -0.12839 -0.81664 -0.1981758  0.22483  0.924909
## 86  0.588723  0.08477 -0.66088 -0.1538094  0.34070 -0.525367
## 87 -0.302930 -0.56524 -0.12508  0.3473579  0.09955  0.698175
## 88 -0.589107 -0.74754 -0.68313  0.5210784 -0.26994 -0.299275
## 89  0.705603 -0.40634  0.01809 -0.0399329  0.43300  0.495029
## 90 -0.136537  0.07662  0.47009 -1.1775177 -0.38500  0.604371
## 91 -0.146116 -0.54600  0.51905 -0.0013364  0.12620  0.628137
## 92  0.075184 -0.11116  0.57359 -0.5036493  0.11362 -0.137164
## 93  0.623493  0.42872 -0.90638 -1.2225134 -0.26997  0.548071
## 94  0.079737  0.15321 -0.80323 -0.3737587 -0.01241  0.728669
## 95 -0.702100 -0.04632 -1.20560  1.1251337  0.47465  0.022203
## 96  0.046956 -0.88164  0.24385  0.7600295  0.47536  0.244100

Functional Group Plot

Functional Group Fit

FG.fit1 <- envfit(FG.MDSe ~ TSF + Management + BGCover + GCover + LitCover + LitMean + 
                     MaxDead + MaxLive + VOR_Mean, data = EnvPatch2, choices = c(1:3), perm=499, 
                   strata=EnvPatch2$YearLoc)
FG.fit1
## 
## ***VECTORS
## 
##             NMDS1    NMDS2    NMDS3     r2 Pr(>r)  
## BGCover   0.42428 -0.88407  0.19598 0.0336  0.482  
## GCover   -0.99072  0.13383  0.02380 0.0360  0.692  
## LitCover  0.04751  0.59234  0.80429 0.0867  0.102  
## LitMean  -0.16146  0.38361  0.90927 0.0967  0.150  
## MaxDead   0.08529 -0.05216  0.99499 0.0948  0.048 *
## MaxLive   0.11977 -0.84856 -0.51537 0.1279  0.986  
## VOR_Mean -0.14117 -0.87112 -0.47033 0.0745  0.956  
## ---
## 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         0.0540 -0.0467  0.0148
## TSF1yr2yr         0.0174 -0.0314 -0.0158
## TSF3yr4yr        -0.1077 -0.0579  0.0365
## TSFRB             0.0139 -0.0074 -0.0075
## TSFUnburned      -0.0181  0.0458  0.0018
## ManagementCattle  0.0395  0.0118 -0.0411
## ManagementSheep  -0.0395 -0.0118  0.0411
## 
## Goodness of fit:
##                r2 Pr(>r)
## TSF        0.0624  0.192
## Management 0.0715  1.000
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499
#MaxDead and LitCover are the only two even trending significantly

FG.fit2 <- envfit(FG.MDSe ~ LitCover + MaxDead, data = EnvPatch2, choices = c(1:3), perm=499, 
                   strata=EnvPatch2$YearLoc)
FG.fit2
## 
## ***VECTORS
## 
##              NMDS1     NMDS2     NMDS3     r2 Pr(>r)  
## LitCover  0.047506  0.592340  0.804290 0.0867  0.094 .
## MaxDead   0.085292 -0.052160  0.994990 0.0948  0.066 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks:  strata 
## Permutation: free
## Number of permutations: 499

Functional Group Fit Plot

Functional Group TSF Spider