Veg Stats & Figures
Jonathan Spiess
July 7, 2021
Community Composition
Broad FG Models
Grass
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## GrassT 7 2284.82 0.00 0.70 0.70 -1135.21
## GrassTG 8 2286.67 1.85 0.28 0.97 -1135.08
## GrassInt 11 2291.24 6.41 0.03 1.00 -1134.14
## GrassNull 4 2300.77 15.94 0.00 1.00 -1146.31
## GrassG 5 2302.58 17.75 0.00 1.00 -1146.18## Data: VegIntT
## Models:
## GrassT: Grass ~ TSF + (1 | Pasture/Year)
## GrassTG: Grass ~ Management + TSF + (1 | Pasture/Year)
## GrassInt: Grass ~ Management * TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## GrassT 7 2284.4 2310.1 -1135.2 2270.4
## GrassTG 8 2286.2 2315.6 -1135.1 2270.2 0.2669 1 0.6054
## GrassInt 11 2290.3 2330.7 -1134.1 2268.3 1.8719 3 0.5994## Data: VegIntT
## Models:
## GrassNull: Grass ~ 1 + (1 | Pasture/Year)
## GrassT: Grass ~ TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## GrassNull 4 2300.6 2315.3 -1146.3 2292.6
## GrassT 7 2284.4 2310.1 -1135.2 2270.4 22.2 3 5.928e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## GrassNull: Grass ~ 1 + (1 | Pasture/Year)
## GrassG: Grass ~ Management + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## GrassNull 4 2300.6 2315.3 -1146.3 2292.6
## GrassG 5 2302.4 2320.7 -1146.2 2292.4 0.2605 1 0.6098##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = Grass ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 5.6310 2.7853 2.022 0.1702
## 3 Years Since Fire - Recently Burned == 0 14.8883 4.8923 3.043 0.0117
## Not Yet Burned - Recently Burned == 0 5.7808 2.7374 2.112 0.1409
## 3 Years Since Fire - Intermediate == 0 9.2573 4.6779 1.979 0.1857
## Not Yet Burned - Intermediate == 0 0.1498 2.8720 0.052 0.9999
## Not Yet Burned - 3 Years Since Fire == 0 -9.1076 5.0888 -1.790 0.2658
##
## Intermediate - Recently Burned == 0
## 3 Years Since Fire - Recently Burned == 0 *
## Not Yet Burned - Recently Burned == 0
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = Grass ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 4.2658 2.3396 1.823 0.25097
## 3 Years Since Fire - Recently Burned == 0 7.8114 4.0465 1.930 0.20503
## Not Yet Burned - Recently Burned == 0 8.3345 2.2779 3.659 0.00163
## 3 Years Since Fire - Intermediate == 0 3.5456 3.8896 0.912 0.79056
## Not Yet Burned - Intermediate == 0 4.0687 2.3606 1.724 0.29914
## Not Yet Burned - 3 Years Since Fire == 0 0.5231 4.1578 0.126 0.99925
##
## Intermediate - Recently Burned == 0
## 3 Years Since Fire - Recently Burned == 0
## Not Yet Burned - Recently Burned == 0 **
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Forb
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## ForbInt 11 584.39 0.00 0.97 0.97 -280.72
## ForbTG 8 592.50 8.11 0.02 0.99 -288.00
## ForbT 7 594.78 10.39 0.01 0.99 -290.19
## ForbG 5 595.42 11.03 0.00 1.00 -292.61
## ForbNull 4 597.76 13.36 0.00 1.00 -294.81## Data: VegIntT
## Models:
## ForbT: log(Forb + 1) ~ TSF + (1 | Pasture/Year)
## ForbTG: log(Forb + 1) ~ Management + TSF + (1 | Pasture/Year)
## ForbInt: log(Forb + 1) ~ Management * TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## ForbT 7 594.39 620.10 -290.19 580.39
## ForbTG 8 591.99 621.38 -288.00 575.99 4.393 1 0.036088 *
## ForbInt 11 583.45 623.85 -280.72 561.45 14.547 3 0.002247 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## ForbNull: log(Forb + 1) ~ 1 + (1 | Pasture/Year)
## ForbT: log(Forb + 1) ~ TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## ForbNull 4 597.62 612.31 -294.81 589.62
## ForbT 7 594.39 620.10 -290.19 580.39 9.2304 3 0.02638 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## ForbNull: log(Forb + 1) ~ 1 + (1 | Pasture/Year)
## ForbG: log(Forb + 1) ~ Management + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## ForbNull 4 597.62 612.31 -294.81 589.62
## ForbG 5 595.21 613.58 -292.61 585.21 4.4024 1 0.03589 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(Forb + 1) ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 0.06834 0.14221 0.481 0.96164
## 3 Years Since Fire - Recently Burned == 0 0.06272 0.24570 0.255 0.99387
## Not Yet Burned - Recently Burned == 0 -0.51435 0.13882 -3.705 0.00101
## 3 Years Since Fire - Intermediate == 0 -0.00562 0.23727 -0.024 0.99999
## Not Yet Burned - Intermediate == 0 -0.58269 0.13914 -4.188 < 0.001
## Not Yet Burned - 3 Years Since Fire == 0 -0.57707 0.24838 -2.323 0.08730
##
## Intermediate - Recently Burned == 0
## 3 Years Since Fire - Recently Burned == 0
## Not Yet Burned - Recently Burned == 0 **
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0 ***
## Not Yet Burned - 3 Years Since Fire == 0 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(Forb + 1) ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 0.154284 0.127821 1.207 0.609
## 3 Years Since Fire - Recently Burned == 0 0.041831 0.223277 0.187 0.998
## Not Yet Burned - Recently Burned == 0 0.161269 0.124948 1.291 0.555
## 3 Years Since Fire - Intermediate == 0 -0.112453 0.213182 -0.527 0.950
## Not Yet Burned - Intermediate == 0 0.006985 0.132814 0.053 1.000
## Not Yet Burned - 3 Years Since Fire == 0 0.119438 0.233176 0.512 0.954
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(Forb + 1) ~ Management + (1 | Pasture/Year),
## data = VegIntT, REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Sheep - Cattle == 0 -0.7356 0.2887 -2.548 0.0108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Legume
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## LegumeInt 11 686.86 0.00 0.94 0.94 -331.95
## LegumeTG 8 692.29 5.43 0.06 1.00 -337.89
## LegumeT 7 699.52 12.66 0.00 1.00 -342.56
## LegumeG 5 701.15 14.29 0.00 1.00 -345.47
## LegumeNull 4 707.05 20.19 0.00 1.00 -349.45## Data: VegIntT
## Models:
## LegumeT: log(Legume + 1) ~ TSF + (1 | Pasture/Year)
## LegumeTG: log(Legume + 1) ~ Management + TSF + (1 | Pasture/Year)
## LegumeInt: log(Legume + 1) ~ Management * TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LegumeT 7 699.12 724.83 -342.56 685.12
## LegumeTG 8 691.78 721.17 -337.89 675.78 9.341 1 0.002241 **
## LegumeInt 11 685.91 726.32 -331.95 663.91 11.870 3 0.007843 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## LegumeNull: log(Legume + 1) ~ 1 + (1 | Pasture/Year)
## LegumeT: log(Legume + 1) ~ TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LegumeNull 4 706.91 721.60 -349.45 698.91
## LegumeT 7 699.12 724.83 -342.56 685.12 13.789 3 0.003207 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## LegumeNull: log(Legume + 1) ~ 1 + (1 | Pasture/Year)
## LegumeG: log(Legume + 1) ~ Management + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LegumeNull 4 706.91 721.6 -349.45 698.91
## LegumeG 5 700.94 719.3 -345.47 690.94 7.9722 1 0.00475 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(Legume + 1) ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 -0.4947 0.1894 -2.612 0.0412
## 3 Years Since Fire - Recently Burned == 0 -0.6114 0.3270 -1.870 0.2305
## Not Yet Burned - Recently Burned == 0 0.2222 0.1849 1.202 0.6138
## 3 Years Since Fire - Intermediate == 0 -0.1166 0.3159 -0.369 0.9820
## Not Yet Burned - Intermediate == 0 0.7169 0.1848 3.880 <0.001
## Not Yet Burned - 3 Years Since Fire == 0 0.8336 0.3300 2.526 0.0524
##
## Intermediate - Recently Burned == 0 *
## 3 Years Since Fire - Recently Burned == 0
## Not Yet Burned - Recently Burned == 0
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0 ***
## Not Yet Burned - 3 Years Since Fire == 0 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(Legume + 1) ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 0.119839 0.118645 1.010 0.732
## 3 Years Since Fire - Recently Burned == 0 0.125368 0.209352 0.599 0.929
## Not Yet Burned - Recently Burned == 0 0.027319 0.116484 0.235 0.995
## 3 Years Since Fire - Intermediate == 0 0.005529 0.198394 0.028 1.000
## Not Yet Burned - Intermediate == 0 -0.092520 0.126978 -0.729 0.880
## Not Yet Burned - 3 Years Since Fire == 0 -0.098050 0.222231 -0.441 0.970
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(Legume + 1) ~ Management + (1 | Pasture/Year),
## data = VegIntT, REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Sheep - Cattle == 0 -0.7225 0.2296 -3.146 0.00165 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Species Ordination1
Setup1
##
## Call:
## metaMDS(comm = SpHRECProp10, distance = "euclidean", k = 3, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(SpHRECProp10)
## Distance: euclidean
##
## Dimensions: 3
## Stress: 0.09874177
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation
## Species: expanded scores based on 'wisconsin(SpHRECProp10)'## importance.MDS1 importance.MDS2 importance.MDS3
## Eigenvalue 3.24 3.08 1.17
## Proportion Explained 0.32 0.30 0.11
## Cumulative Proportion 0.32 0.62 0.73
## importance.MDS4 importance.MDS5 importance.MDS6
## Eigenvalue 0.83 0.76 0.51
## Proportion Explained 0.08 0.07 0.05
## Cumulative Proportion 0.81 0.89 0.94
## importance.MDS7 importance.MDS8
## Eigenvalue 0.40 0.26
## Proportion Explained 0.04 0.03
## Cumulative Proportion 0.97 1.00
Env Fit
##
## ***VECTORS
##
## NMDS1 NMDS2 NMDS3 r2 Pr(>r)
## BGCover 0.55657 -0.31890 0.76716 0.0502 0.212
## GCover -0.45693 -0.71673 0.52679 0.0200 0.578
## LitCover -0.32055 0.48312 0.81476 0.0072 0.746
## LitMean -0.09014 -0.37847 0.92121 0.0059 0.692
## MaxDead 0.49540 -0.38826 0.77706 0.0677 0.392
## MaxLive -0.20036 -0.31241 0.92858 0.0538 0.314
## VOR_Mean -0.62195 -0.44996 0.64087 0.0653 0.086 .
## ---
## 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
## TSFRB -0.0118 0.0093 0.0083
## TSF1yr2yr 0.0747 -0.0236 -0.0128
## TSF2yr3yr 0.1014 -0.0698 -0.0187
## TSF3yr4yr 0.0732 -0.0751 -0.0242
## TSFUnburned -0.0755 0.0414 0.0111
## ManagementCattle -0.0046 0.0737 -0.0290
## ManagementSheep 0.0046 -0.0737 0.0290
##
## Goodness of fit:
## r2 Pr(>r)
## TSF 0.0799 0.146
## Management 0.0746 1.000
## Blocks: strata
## Permutation: free
## Number of permutations: 499##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2 NMDS3
## TSFRB -0.0118 0.0093 0.0083
## TSF1yr2yr 0.0747 -0.0236 -0.0128
## TSF2yr3yr 0.1014 -0.0698 -0.0187
## TSF3yr4yr 0.0732 -0.0751 -0.0242
## TSFUnburned -0.0755 0.0414 0.0111
##
## Goodness of fit:
## r2 Pr(>r)
## TSF 0.0799 0.134
## Blocks: strata
## Permutation: free
## Number of permutations: 499
Species Ordination2
Setup2
##
## Call:
## metaMDS(comm = SpHRECProp10Int, distance = "euclidean", k = 3, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(SpHRECProp10Int)
## Distance: euclidean
##
## Dimensions: 3
## Stress: 0.09811867
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation
## Species: expanded scores based on 'wisconsin(SpHRECProp10Int)'## importance.MDS1 importance.MDS2 importance.MDS3
## Eigenvalue 3.24 3.08 1.17
## Proportion Explained 0.32 0.30 0.11
## Cumulative Proportion 0.32 0.62 0.73
## importance.MDS4 importance.MDS5 importance.MDS6
## Eigenvalue 0.83 0.76 0.51
## Proportion Explained 0.08 0.07 0.05
## Cumulative Proportion 0.81 0.89 0.94
## importance.MDS7 importance.MDS8
## Eigenvalue 0.40 0.26
## Proportion Explained 0.04 0.03
## Cumulative Proportion 0.97 1.00
Env Fit2
##
## ***VECTORS
##
## NMDS1 NMDS2 NMDS3 r2 Pr(>r)
## BGCover 0.54694 -0.30016 0.78151 0.0532 0.218
## GCover -0.41190 -0.73755 0.53512 0.0214 0.530
## LitCover -0.46669 0.75971 0.45282 0.0048 0.874
## LitMean -0.11634 -0.67360 0.72988 0.0018 0.922
## MaxDead 0.63843 -0.41882 0.64576 0.0530 0.482
## MaxLive -0.18096 -0.31953 0.93014 0.0529 0.360
## VOR_Mean -0.62348 -0.49023 0.60906 0.0612 0.136
## Blocks: strata
## Permutation: free
## Number of permutations: 499
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2 NMDS3
## TSFRecently Burned -0.0122 0.0066 0.0166
## TSFIntermediate 0.0865 -0.0387 -0.0192
## TSF3 Years Since Fire 0.0769 -0.0671 -0.0429
## TSFNot Yet Burned -0.0768 0.0390 0.0121
## ManagementCattle -0.0084 0.0758 -0.0386
## ManagementSheep 0.0084 -0.0758 0.0386
##
## Goodness of fit:
## r2 Pr(>r)
## TSF 0.0784 0.086 .
## Management 0.0862 1.000
## ---
## 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
## TSFRecently Burned -0.0122 0.0066 0.0166
## TSFIntermediate 0.0865 -0.0387 -0.0192
## TSF3 Years Since Fire 0.0769 -0.0671 -0.0429
## TSFNot Yet Burned -0.0768 0.0390 0.0121
##
## Goodness of fit:
## r2 Pr(>r)
## TSF 0.0784 0.066 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499
Functional Group Ordination1
Setup
##
## 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))'## importance.MDS1 importance.MDS2 importance.MDS3
## Eigenvalue 3.07 1.18 0.87
## Proportion Explained 0.51 0.20 0.15
## Cumulative Proportion 0.51 0.70 0.85
## importance.MDS4 importance.MDS5 importance.MDS6
## Eigenvalue 0.47 0.24 0.19
## Proportion Explained 0.08 0.04 0.03
## Cumulative Proportion 0.93 0.97 1.00
Env Fit
##
## ***VECTORS
##
## NMDS1 NMDS2 NMDS3 r2 Pr(>r)
## BGCover 0.61005 0.72930 0.30977 0.0276 0.530
## GCover -0.96483 0.02107 0.26204 0.0385 0.680
## LitCover 0.00500 -0.71263 0.70152 0.0875 0.084 .
## LitMean -0.18004 -0.59738 0.78149 0.0980 0.114
## MaxDead 0.10794 -0.23779 0.96530 0.0950 0.052 .
## MaxLive 0.20291 0.92066 -0.33349 0.1217 0.994
## VOR_Mean -0.05783 0.94683 -0.31649 0.0657 0.984
## ---
## 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
## TSFRB 0.0178 0.0059 -0.0133
## TSF1yr2yr 0.0234 0.0397 -0.0007
## TSF2yr3yr 0.0650 0.0342 0.0210
## TSF3yr4yr -0.1017 0.0501 0.0550
## 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 0.172
## Management 0.0745 1.000
## Blocks: strata
## Permutation: free
## Number of permutations: 499
Functional Group Ordination2
Setup2
##
## Call:
## metaMDS(comm = FGFineHRECInt, distance = "euclidean", k = 3, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(FGFineHRECInt))
## 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(FGFineHRECInt))'## importance.MDS1 importance.MDS2 importance.MDS3
## Eigenvalue 3.07 1.18 0.87
## Proportion Explained 0.51 0.20 0.15
## Cumulative Proportion 0.51 0.70 0.85
## importance.MDS4 importance.MDS5 importance.MDS6
## Eigenvalue 0.47 0.24 0.19
## Proportion Explained 0.08 0.04 0.03
## Cumulative Proportion 0.93 0.97 1.00
Env Fit2
##
## ***VECTORS
##
## NMDS1 NMDS2 NMDS3 r2 Pr(>r)
## BGCover 0.50202 0.86189 0.07155 0.0266 0.528
## GCover -0.72234 -0.54895 0.42057 0.0350 0.730
## LitCover 0.00162 -0.71862 0.69540 0.0877 0.070 .
## LitMean -0.16832 -0.64780 0.74298 0.0991 0.106
## MaxDead 0.11749 -0.21456 0.96962 0.0900 0.076 .
## MaxLive 0.19964 0.93052 -0.30703 0.1234 0.992
## VOR_Mean -0.00510 0.96599 -0.25851 0.0576 0.988
## ---
## 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
## TSFRecently Burned 0.0178 0.0059 -0.0133
## TSFIntermediate 0.0400 0.0375 0.0080
## TSF3 Years Since Fire -0.1017 0.0501 0.0550
## TSFNot Yet Burned -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 0.0610 0.116
## Management 0.0745 1.000
## Blocks: strata
## Permutation: free
## Number of permutations: 499
Structural Patterns
Models and Graphs
VOR
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## VORT 8 2594.21 0.00 0.61 0.61 -1289.10
## VORTG 9 2595.29 1.07 0.36 0.97 -1288.63
## VORInt 12 2600.36 6.15 0.03 1.00 -1288.16
## VORNull 5 2653.78 59.57 0.00 1.00 -1321.88
## VORG 6 2654.80 60.58 0.00 1.00 -1321.39## Data: VegInt
## Models:
## VORT: log(VOR_Mean + 1) ~ TSF + (1 | Transect/Pasture/Year)
## VORTG: log(VOR_Mean + 1) ~ Management + TSF + (1 | Transect/Pasture/Year)
## VORInt: log(VOR_Mean + 1) ~ Management * TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## VORT 8 2594.2 2648.2 -1289.1 2578.2
## VORTG 9 2595.3 2656.0 -1288.6 2577.3 0.9320 1 0.3344
## VORInt 12 2600.3 2681.3 -1288.2 2576.3 0.9482 3 0.8138## Data: VegInt
## Models:
## VORNull: log(VOR_Mean + 1) ~ 1 + (1 | Transect/Pasture/Year)
## VORT: log(VOR_Mean + 1) ~ TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## VORNull 5 2653.8 2687.5 -1321.9 2643.8
## VORT 8 2594.2 2648.2 -1289.1 2578.2 65.579 3 3.772e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegInt
## Models:
## VORNull: log(VOR_Mean + 1) ~ 1 + (1 | Transect/Pasture/Year)
## VORG: log(VOR_Mean + 1) ~ Management + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## VORNull 5 2653.8 2687.5 -1321.9 2643.8
## VORG 6 2654.8 2695.3 -1321.4 2642.8 0.9878 1 0.3203##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(VOR_Mean + 1) ~ TSF + (1 | Transect/Pasture/Year),
## data = subset(VegInt, Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 0.36795 0.08111 4.536 < 0.001
## 3 Years Since Fire - Recently Burned == 0 0.45807 0.13826 3.313 0.00503
## Not Yet Burned - Recently Burned == 0 0.22702 0.07870 2.885 0.01940
## 3 Years Since Fire - Intermediate == 0 0.09012 0.13405 0.672 0.90360
## Not Yet Burned - Intermediate == 0 -0.14094 0.07770 -1.814 0.25585
## Not Yet Burned - 3 Years Since Fire == 0 -0.23105 0.13801 -1.674 0.32535
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 **
## Not Yet Burned - Recently Burned == 0 *
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(VOR_Mean + 1) ~ TSF + (1 | Transect/Pasture/Year),
## data = subset(VegInt, Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 0.46189 0.05985 7.718 < 0.001
## 3 Years Since Fire - Recently Burned == 0 0.53707 0.10142 5.295 < 0.001
## Not Yet Burned - Recently Burned == 0 0.26485 0.05786 4.578 < 0.001
## 3 Years Since Fire - Intermediate == 0 0.07519 0.09865 0.762 0.86617
## Not Yet Burned - Intermediate == 0 -0.19703 0.05681 -3.469 0.00282
## Not Yet Burned - 3 Years Since Fire == 0 -0.27222 0.10074 -2.702 0.03254
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0 **
## Not Yet Burned - 3 Years Since Fire == 0 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)MaxLive
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## MaxLiveT 8 23400.51 0.00 0.55 0.55 -11692.25
## MaxLiveTG 9 23401.14 0.63 0.40 0.94 -11691.56
## MaxLiveInt 12 23405.09 4.58 0.06 1.00 -11690.52
## MaxLiveNull 5 23424.65 24.13 0.00 1.00 -11707.32
## MaxLiveG 6 23425.21 24.69 0.00 1.00 -11706.60## Data: VegInt
## Models:
## MaxLiveT: MaxLive ~ TSF + (1 | Transect/Pasture/Year)
## MaxLiveTG: MaxLive ~ Management + TSF + (1 | Transect/Pasture/Year)
## MaxLiveInt: MaxLive ~ Management * TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## MaxLiveT 8 23401 23455 -11692 23385
## MaxLiveTG 9 23401 23462 -11692 23383 1.3791 1 0.2403
## MaxLiveInt 12 23405 23486 -11690 23381 2.0709 3 0.5578## Data: VegInt
## Models:
## MaxLiveNull: MaxLive ~ 1 + (1 | Transect/Pasture/Year)
## MaxLiveT: MaxLive ~ TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## MaxLiveNull 5 23425 23458 -11707 23415
## MaxLiveT 8 23401 23455 -11692 23385 30.145 3 1.287e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegInt
## Models:
## MaxLiveNull: MaxLive ~ 1 + (1 | Transect/Pasture/Year)
## MaxLiveG: MaxLive ~ Management + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## MaxLiveNull 5 23425 23458 -11707 23415
## MaxLiveG 6 23425 23466 -11707 23413 1.4438 1 0.2295##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = MaxLive ~ TSF + (1 | Transect/Pasture/Year), data = subset(VegInt,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 1.2495 0.4475 2.792 0.0251
## 3 Years Since Fire - Recently Burned == 0 0.7213 0.7637 0.944 0.7727
## Not Yet Burned - Recently Burned == 0 0.3810 0.4344 0.877 0.8096
## 3 Years Since Fire - Intermediate == 0 -0.5283 0.7399 -0.714 0.8870
## Not Yet Burned - Intermediate == 0 -0.8685 0.4303 -2.018 0.1724
## Not Yet Burned - 3 Years Since Fire == 0 -0.3403 0.7639 -0.445 0.9691
##
## Intermediate - Recently Burned == 0 *
## 3 Years Since Fire - Recently Burned == 0
## Not Yet Burned - Recently Burned == 0
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = MaxLive ~ TSF + (1 | Transect/Pasture/Year), data = subset(VegInt,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 2.0142 0.3541 5.688 < 0.001
## 3 Years Since Fire - Recently Burned == 0 1.3092 0.5902 2.218 0.11164
## Not Yet Burned - Recently Burned == 0 0.8260 0.3407 2.424 0.06791
## 3 Years Since Fire - Intermediate == 0 -0.7050 0.5783 -1.219 0.60306
## Not Yet Burned - Intermediate == 0 -1.1883 0.3197 -3.717 0.00102
## Not Yet Burned - 3 Years Since Fire == 0 -0.4833 0.5702 -0.848 0.82501
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0
## Not Yet Burned - Recently Burned == 0 .
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0 **
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)MaxDead
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## MaxDeadT 8 22488.89 0.00 0.44 0.44 -11236.43
## MaxDeadTG 9 22489.00 0.11 0.42 0.86 -11235.48
## MaxDeadInt 12 22491.17 2.29 0.14 1.00 -11233.56
## MaxDeadNull 5 22655.00 166.11 0.00 1.00 -11322.49
## MaxDeadG 6 22655.57 166.68 0.00 1.00 -11321.78## Data: VegInt
## Models:
## MaxDeadT: MaxDead ~ TSF + (1 | Transect/Pasture/Year)
## MaxDeadTG: MaxDead ~ Management + TSF + (1 | Transect/Pasture/Year)
## MaxDeadInt: MaxDead ~ Management * TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## MaxDeadT 8 22489 22543 -11236 22473
## MaxDeadTG 9 22489 22550 -11236 22471 1.8975 1 0.1684
## MaxDeadInt 12 22491 22572 -11234 22467 3.8427 3 0.2790## Data: VegInt
## Models:
## MaxDeadNull: MaxDead ~ 1 + (1 | Transect/Pasture/Year)
## MaxDeadT: MaxDead ~ TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## MaxDeadNull 5 22655 22689 -11322 22645
## MaxDeadT 8 22489 22543 -11236 22473 172.12 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegInt
## Models:
## MaxDeadNull: MaxDead ~ 1 + (1 | Transect/Pasture/Year)
## MaxDeadG: MaxDead ~ Management + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## MaxDeadNull 5 22655 22689 -11322 22645
## MaxDeadG 6 22656 22696 -11322 22644 1.4323 1 0.2314##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = MaxDead ~ TSF + (1 | Transect/Pasture/Year), data = subset(VegInt,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 1.8297 0.1891 9.676 < 0.001
## 3 Years Since Fire - Recently Burned == 0 3.9464 0.3226 12.232 < 0.001
## Not Yet Burned - Recently Burned == 0 1.1975 0.1833 6.532 < 0.001
## 3 Years Since Fire - Intermediate == 0 2.1167 0.3129 6.765 < 0.001
## Not Yet Burned - Intermediate == 0 -0.6322 0.1817 -3.479 0.00252
## Not Yet Burned - 3 Years Since Fire == 0 -2.7489 0.3226 -8.521 < 0.001
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 ***
## Not Yet Burned - Intermediate == 0 **
## Not Yet Burned - 3 Years Since Fire == 0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = MaxDead ~ TSF + (1 | Transect/Pasture/Year), data = subset(VegInt,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 2.2072 0.2848 7.749 < 1e-04
## 3 Years Since Fire - Recently Burned == 0 4.4893 0.4817 9.320 < 1e-04
## Not Yet Burned - Recently Burned == 0 1.0447 0.2752 3.796 0.000747
## 3 Years Since Fire - Intermediate == 0 2.2821 0.4690 4.866 < 1e-04
## Not Yet Burned - Intermediate == 0 -1.1624 0.2686 -4.327 < 1e-04
## Not Yet Burned - 3 Years Since Fire == 0 -3.4446 0.4768 -7.225 < 1e-04
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 ***
## Not Yet Burned - Intermediate == 0 ***
## Not Yet Burned - 3 Years Since Fire == 0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Litter Depth
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## LitMeanT 8 3696.29 0.00 0.61 0.61 -1840.14
## LitMeanTG 9 3697.35 1.06 0.36 0.97 -1839.66
## LitMeanInt 12 3702.22 5.92 0.03 1.00 -1839.08
## LitMeanNull 5 3785.41 89.12 0.00 1.00 -1887.70
## LitMeanG 6 3786.75 90.46 0.00 1.00 -1887.37## Data: VegInt
## Models:
## LitMeanT: log(LitMean + 1) ~ TSF + (1 | Transect/Pasture/Year)
## LitMeanTG: log(LitMean + 1) ~ Management + TSF + (1 | Transect/Pasture/Year)
## LitMeanInt: log(LitMean + 1) ~ Management * TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LitMeanT 8 3696.3 3750.3 -1840.1 3680.3
## LitMeanTG 9 3697.3 3758.1 -1839.7 3679.3 0.9450 1 0.3310
## LitMeanInt 12 3702.2 3783.2 -1839.1 3678.2 1.1575 3 0.7632## Data: VegInt
## Models:
## LitMeanNull: log(LitMean + 1) ~ 1 + (1 | Transect/Pasture/Year)
## LitMeanT: log(LitMean + 1) ~ TSF + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LitMeanNull 5 3785.4 3819.2 -1887.7 3775.4
## LitMeanT 8 3696.3 3750.3 -1840.1 3680.3 95.133 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegInt
## Models:
## LitMeanNull: log(LitMean + 1) ~ 1 + (1 | Transect/Pasture/Year)
## LitMeanG: log(LitMean + 1) ~ Management + (1 | Transect/Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LitMeanNull 5 3785.4 3819.2 -1887.7 3775.4
## LitMeanG 6 3786.7 3827.3 -1887.4 3774.7 0.6632 1 0.4154##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(LitMean + 1) ~ TSF + (1 | Transect/Pasture/Year),
## data = subset(VegInt, Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 0.25694 0.06162 4.170 <0.001
## 3 Years Since Fire - Recently Burned == 0 0.78343 0.10471 7.482 <0.001
## Not Yet Burned - Recently Burned == 0 0.38347 0.05970 6.423 <0.001
## 3 Years Since Fire - Intermediate == 0 0.52649 0.10174 5.175 <0.001
## Not Yet Burned - Intermediate == 0 0.12653 0.05848 2.164 0.126
## Not Yet Burned - 3 Years Since Fire == 0 -0.39996 0.10400 -3.846 <0.001
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 ***
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(LitMean + 1) ~ TSF + (1 | Transect/Pasture/Year),
## data = subset(VegInt, Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 0.35508 0.08163 4.350 < 0.001
## 3 Years Since Fire - Recently Burned == 0 0.88995 0.13605 6.541 < 0.001
## Not Yet Burned - Recently Burned == 0 0.40763 0.07855 5.190 < 0.001
## 3 Years Since Fire - Intermediate == 0 0.53487 0.13330 4.012 < 0.001
## Not Yet Burned - Intermediate == 0 0.05255 0.07369 0.713 0.88733
## Not Yet Burned - 3 Years Since Fire == 0 -0.48232 0.13144 -3.670 0.00128
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 ***
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Bare Ground
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## BGCoverT 7 662.21 0.00 0.71 0.71 -323.90
## BGCoverTG 8 664.19 1.99 0.26 0.97 -323.84
## BGCoverInt 11 668.66 6.46 0.03 1.00 -322.86
## BGCoverNull 4 751.39 89.18 0.00 1.00 -371.62
## BGCoverG 5 753.33 91.13 0.00 1.00 -371.56## Data: VegIntT
## Models:
## BGCoverT: log(BGCover + 1) ~ TSF + (1 | Pasture/Year)
## BGCoverTG: log(BGCover + 1) ~ Management + TSF + (1 | Pasture/Year)
## BGCoverInt: log(BGCover + 1) ~ Management * TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## BGCoverT 7 661.81 687.52 -323.90 647.81
## BGCoverTG 8 663.68 693.07 -323.84 647.68 0.1266 1 0.7220
## BGCoverInt 11 667.71 708.12 -322.86 645.71 1.9677 3 0.5791## Data: VegIntT
## Models:
## BGCoverNull: log(BGCover + 1) ~ 1 + (1 | Pasture/Year)
## BGCoverT: log(BGCover + 1) ~ TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## BGCoverNull 4 751.25 765.94 -371.62 743.25
## BGCoverT 7 661.81 687.52 -323.90 647.81 95.44 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## BGCoverNull: log(BGCover + 1) ~ 1 + (1 | Pasture/Year)
## BGCoverG: log(BGCover + 1) ~ Management + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## BGCoverNull 4 751.25 765.94 -371.62 743.25
## BGCoverG 5 753.12 771.49 -371.56 743.12 0.1251 1 0.7236##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(BGCover + 1) ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 -0.4992 0.1589 -3.142 0.00832
## 3 Years Since Fire - Recently Burned == 0 -1.5541 0.2794 -5.561 < 0.001
## Not Yet Burned - Recently Burned == 0 -0.9579 0.1562 -6.131 < 0.001
## 3 Years Since Fire - Intermediate == 0 -1.0549 0.2669 -3.952 < 0.001
## Not Yet Burned - Intermediate == 0 -0.4587 0.1646 -2.788 0.02502
## Not Yet Burned - 3 Years Since Fire == 0 0.5962 0.2914 2.046 0.16201
##
## Intermediate - Recently Burned == 0 **
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 ***
## Not Yet Burned - Intermediate == 0 *
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(BGCover + 1) ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 -0.8050 0.1574 -5.115 <0.001
## 3 Years Since Fire - Recently Burned == 0 -1.5195 0.2697 -5.635 <0.001
## Not Yet Burned - Recently Burned == 0 -1.0167 0.1527 -6.658 <0.001
## 3 Years Since Fire - Intermediate == 0 -0.7145 0.2607 -2.741 0.0292
## Not Yet Burned - Intermediate == 0 -0.2117 0.1544 -1.371 0.5049
## Not Yet Burned - 3 Years Since Fire == 0 0.5028 0.2728 1.843 0.2424
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 *
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Ground Litter
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## GCoverTG 8 2150.11 0.00 0.52 0.52 -1066.80
## GCoverT 7 2151.23 1.12 0.30 0.82 -1068.42
## GCoverInt 11 2152.26 2.15 0.18 1.00 -1064.66
## GCoverG 5 2192.79 42.67 0.00 1.00 -1091.29
## GCoverNull 4 2194.11 44.00 0.00 1.00 -1092.99## Data: VegIntT
## Models:
## GCoverT: GCover ~ TSF + (1 | Pasture/Year)
## GCoverTG: GCover ~ Management + TSF + (1 | Pasture/Year)
## GCoverInt: GCover ~ Management * TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## GCoverT 7 2150.8 2176.6 -1068.4 2136.8
## GCoverTG 8 2149.6 2179.0 -1066.8 2133.6 3.2324 1 0.07219 .
## GCoverInt 11 2151.3 2191.7 -1064.7 2129.3 4.2888 3 0.23192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## GCoverNull: GCover ~ 1 + (1 | Pasture/Year)
## GCoverT: GCover ~ TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## GCoverNull 4 2194.0 2208.7 -1093.0 2186.0
## GCoverT 7 2150.8 2176.6 -1068.4 2136.8 49.138 3 1.219e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## GCoverNull: GCover ~ 1 + (1 | Pasture/Year)
## GCoverG: GCover ~ Management + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## GCoverNull 4 2194.0 2208.7 -1093.0 2186.0
## GCoverG 5 2192.6 2210.9 -1091.3 2182.6 3.398 1 0.06528 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = GCover ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 -10.0407 2.1574 -4.654 <0.001
## 3 Years Since Fire - Recently Burned == 0 -10.7909 3.6452 -2.960 0.0153
## Not Yet Burned - Recently Burned == 0 -9.8166 2.0905 -4.696 <0.001
## 3 Years Since Fire - Intermediate == 0 -0.7502 3.5543 -0.211 0.9965
## Not Yet Burned - Intermediate == 0 0.2241 1.9617 0.114 0.9994
## Not Yet Burned - 3 Years Since Fire == 0 0.9743 3.5427 0.275 0.9924
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 *
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = GCover ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 -6.111 1.993 -3.066 0.01098
## 3 Years Since Fire - Recently Burned == 0 -16.612 3.383 -4.911 < 0.001
## Not Yet Burned - Recently Burned == 0 -7.763 1.928 -4.027 < 0.001
## 3 Years Since Fire - Intermediate == 0 -10.501 3.288 -3.194 0.00688
## Not Yet Burned - Intermediate == 0 -1.653 1.900 -0.870 0.81357
## Not Yet Burned - 3 Years Since Fire == 0 8.848 3.368 2.627 0.04013
##
## Intermediate - Recently Burned == 0 *
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 **
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Vertical Litter
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## LitCoverT 7 2068.47 0.00 0.62 0.62 -1027.04
## LitCoverTG 8 2070.06 1.59 0.28 0.90 -1026.78
## LitCoverInt 11 2072.23 3.76 0.10 1.00 -1024.64
## LitCoverNull 4 2187.29 118.81 0.00 1.00 -1089.57
## LitCoverG 5 2188.93 120.46 0.00 1.00 -1089.36## Data: VegIntT
## Models:
## LitCoverT: LitCover ~ TSF + (1 | Pasture/Year)
## LitCoverTG: LitCover ~ Management + TSF + (1 | Pasture/Year)
## LitCoverInt: LitCover ~ Management * TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LitCoverT 7 2068.1 2093.8 -1027.0 2054.1
## LitCoverTG 8 2069.6 2098.9 -1026.8 2053.6 0.5241 1 0.4691
## LitCoverInt 11 2071.3 2111.7 -1024.6 2049.3 4.2640 3 0.2343## Data: VegIntT
## Models:
## LitCoverNull: LitCover ~ 1 + (1 | Pasture/Year)
## LitCoverT: LitCover ~ TSF + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LitCoverNull 4 2187.2 2201.8 -1089.6 2179.2
## LitCoverT 7 2068.1 2093.8 -1027.0 2054.1 125.07 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Data: VegIntT
## Models:
## LitCoverNull: LitCover ~ 1 + (1 | Pasture/Year)
## LitCoverG: LitCover ~ Management + (1 | Pasture/Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## LitCoverNull 4 2187.2 2201.8 -1089.6 2179.2
## LitCoverG 5 2188.7 2207.1 -1089.4 2178.7 0.4279 1 0.513##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = LitCover ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 9.311 1.917 4.857 <0.001
## 3 Years Since Fire - Recently Burned == 0 17.668 3.340 5.290 <0.001
## Not Yet Burned - Recently Burned == 0 10.960 1.878 5.837 <0.001
## 3 Years Since Fire - Intermediate == 0 8.357 3.210 2.603 0.0422
## Not Yet Burned - Intermediate == 0 1.648 1.926 0.856 0.8202
## Not Yet Burned - 3 Years Since Fire == 0 -6.708 3.425 -1.959 0.1938
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 *
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = LitCover ~ TSF + (1 | Pasture/Year), data = subset(VegIntT,
## Management == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Intermediate - Recently Burned == 0 11.2456 1.6167 6.956 <1e-05
## 3 Years Since Fire - Recently Burned == 0 26.5125 2.8168 9.412 <1e-05
## Not Yet Burned - Recently Burned == 0 12.1714 1.5787 7.710 <1e-05
## 3 Years Since Fire - Intermediate == 0 15.2669 2.6944 5.666 <1e-05
## Not Yet Burned - Intermediate == 0 0.9257 1.6670 0.555 0.942
## Not Yet Burned - 3 Years Since Fire == 0 -14.3412 2.9291 -4.896 <1e-05
##
## Intermediate - Recently Burned == 0 ***
## 3 Years Since Fire - Recently Burned == 0 ***
## Not Yet Burned - Recently Burned == 0 ***
## 3 Years Since Fire - Intermediate == 0 ***
## Not Yet Burned - Intermediate == 0
## Not Yet Burned - 3 Years Since Fire == 0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Structure Ordination 1
Setup
##
## Call:
## metaMDS(comm = StrSpeHREC, distance = "euclidean", k = 3, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(StrSpeHREC)
## Distance: euclidean
##
## Dimensions: 3
## Stress: 0.04695251
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation
## Species: expanded scores based on 'wisconsin(StrSpeHREC)'## importance.MDS1 importance.MDS2 importance.MDS3
## Eigenvalue 2.07 0.72 0.50
## Proportion Explained 0.58 0.20 0.14
## Cumulative Proportion 0.58 0.78 0.92
## importance.MDS4 importance.MDS5 importance.MDS6
## Eigenvalue 0.17 0.07 0.04
## Proportion Explained 0.05 0.02 0.01
## Cumulative Proportion 0.97 0.99 1.00
Env Fit
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2 NMDS3
## TSFRB -0.1822 -0.0164 -0.0123
## TSF1yr2yr 0.0173 -0.0309 0.0142
## TSF2yr3yr 0.0803 -0.0040 0.0075
## TSF3yr4yr 0.1735 0.0727 0.0533
## 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##
## ***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##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2 NMDS3
## TSFRB -0.1822 -0.0164 -0.0123
## TSF1yr2yr 0.0173 -0.0309 0.0142
## TSF2yr3yr 0.0803 -0.0040 0.0075
## TSF3yr4yr 0.1735 0.0727 0.0533
## 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 comparisons using factor fitting to an ordination
##
## data: StSp.MDSe by EnvPatchHREC2$TSF
## 999 permutations
##
## RB 1yr2yr 2yr3yr 3yr4yr
## 1yr2yr 0.0025 - - -
## 2yr3yr 0.0025 0.2278 - -
## 3yr4yr 0.0025 0.0080 0.1313 -
## Unburned 0.0025 0.0443 0.5470 0.0083
##
## P value adjustment method: fdr##
## Pairwise comparisons using factor fitting to an ordination
##
## data: StSp.MDSe by EnvPatchHREC2$Management
## 999 permutations
##
## Cattle
## Sheep 0.45
##
## P value adjustment method: fdr
Structure Ordination 2
Setup2
##
## Call:
## metaMDS(comm = StrSpeHRECInt, distance = "euclidean", k = 3, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(StrSpeHRECInt)
## Distance: euclidean
##
## Dimensions: 3
## Stress: 0.04695246
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation
## Species: expanded scores based on 'wisconsin(StrSpeHRECInt)'## importance.MDS1 importance.MDS2 importance.MDS3
## Eigenvalue 2.07 0.72 0.50
## Proportion Explained 0.58 0.20 0.14
## Cumulative Proportion 0.58 0.78 0.92
## importance.MDS4 importance.MDS5 importance.MDS6
## Eigenvalue 0.17 0.07 0.04
## Proportion Explained 0.05 0.02 0.01
## Cumulative Proportion 0.97 0.99 1.00
Env Fit2
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2 NMDS3
## TSFRecently Burned -0.1822 -0.0164 -0.0123
## TSFIntermediate 0.0425 -0.0202 0.0115
## TSF3 Years Since Fire 0.1735 0.0727 0.0533
## TSFNot Yet Burned 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 0.3775 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##
## ***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##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2 NMDS3
## TSFRecently Burned -0.1822 -0.0164 -0.0123
## TSFIntermediate 0.0425 -0.0202 0.0115
## TSF3 Years Since Fire 0.1735 0.0727 0.0533
## TSFNot Yet Burned 0.0571 0.0157 -0.0102
##
## Goodness of fit:
## r2 Pr(>r)
## TSF 0.3775 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499##
## Pairwise comparisons using factor fitting to an ordination
##
## data: StSp.MDSeInt by EnvPatchHREC2$TSF
## 999 permutations
##
## RB 1yr2yr 2yr3yr 3yr4yr
## 1yr2yr 0.0025 - - -
## 2yr3yr 0.0025 0.1878 - -
## 3yr4yr 0.0025 0.0067 0.0657 -
## Unburned 0.0025 0.0800 0.3380 0.0067
##
## P value adjustment method: fdr##
## Pairwise comparisons using factor fitting to an ordination
##
## data: StSp.MDSeInt by EnvPatchHREC2$Management
## 999 permutations
##
## Cattle
## Sheep 0.44
##
## P value adjustment method: fdr
Heterogeneity Over Time
## `summarise()` has grouped output by 'Year', 'Variable'. You can override using the `.groups` argument.VOR
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.03568 0.03568 10.56 0.0314 *
## Residuals 4 0.01351 0.00338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "VOR"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.15424 0.04746 3.25 0.0314 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 7.017e-11 7.017e-11 1.326 0.314
## Residuals 4 2.117e-10 5.293e-11##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "VOR"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 6.84e-06 5.94e-06 1.151 0.314
## (Adjusted p values reported -- single-step method)Max Live
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 1.4380 1.4380 19.63 0.0114 *
## Residuals 4 0.2931 0.0733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "ML"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.9791 0.2210 4.43 0.0114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.02013 0.02013 1 0.374
## Residuals 4 0.08051 0.02013##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "ML"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 -0.1158 0.1158 -1 0.374
## (Adjusted p values reported -- single-step method)Max Dead
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.07551 0.07551 174.5 0.00019 ***
## Residuals 4 0.00173 0.00043
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "MD"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.22436 0.01698 13.21 0.00019 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 1.098e-11 1.098e-11 3.488 0.135
## Residuals 4 1.259e-11 3.148e-12##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "MD"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 -2.706e-06 1.449e-06 -1.868 0.135
## (Adjusted p values reported -- single-step method)Litter Depth
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.19293 0.19293 58.8 0.00155 **
## Residuals 4 0.01312 0.00328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "LM"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.35864 0.04677 7.668 0.00155 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.000539 0.0005392 0.197 0.68
## Residuals 4 0.010922 0.0027306##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "LM"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.01896 0.04267 0.444 0.68
## (Adjusted p values reported -- single-step method)Bare Ground Cover
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.3431 0.3431 16.89 0.0147 *
## Residuals 4 0.0812 0.0203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "BG"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.4782 0.1164 4.11 0.0147 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 1.373e-09 1.373e-09 2.692 0.176
## Residuals 4 2.040e-09 5.101e-10##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "BG"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 3.026e-05 1.844e-05 1.641 0.176
## (Adjusted p values reported -- single-step method)Ground Litter
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 25.899 25.899 19.17 0.0119 *
## Residuals 4 5.405 1.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "GC"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 4.1552 0.9491 4.378 0.0119 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 4.980e-08 4.976e-08 0.497 0.52
## Residuals 4 4.004e-07 1.001e-07##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "GC"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.0001821 0.0002583 0.705 0.52
## (Adjusted p values reported -- single-step method)Standing Litter
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 30.343 30.343 38.81 0.00338 **
## Residuals 4 3.128 0.782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "LC"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 4.498 0.722 6.229 0.00338 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 1.399e-14 1.399e-14 1 0.374
## Residuals 4 5.596e-14 1.399e-14##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "LC"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 9.658e-08 9.658e-08 1 0.374
## (Adjusted p values reported -- single-step method)Forb
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.08002 0.08002 1.168 0.341
## Residuals 4 0.27409 0.06852##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "FB"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 -0.2310 0.2137 -1.081 0.341
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.04715 0.04715 1.413 0.3
## Residuals 4 0.13348 0.03337##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "FB"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.1773 0.1492 1.189 0.3
## (Adjusted p values reported -- single-step method)Legume
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.0188 0.01885 0.097 0.771
## Residuals 4 0.7755 0.19388##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "LG"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.1121 0.3595 0.312 0.771
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.3206 0.3206 11.22 0.0286 *
## Residuals 4 0.1143 0.0286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "LG"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 0.4623 0.1380 3.35 0.0286 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Grass
## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 0.18 0.18 0.005 0.946
## Residuals 4 143.61 35.90##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonP,
## Variable == "GR"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 -0.3506 4.8924 -0.072 0.946
## (Adjusted p values reported -- single-step method)## Df Sum Sq Mean Sq F value Pr(>F)
## Year 1 1.654e-05 1.654e-05 1 0.374
## Residuals 4 6.617e-05 1.654e-05##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = sdcor ~ Year, data = subset(VPYearComparisonM,
## Variable == "GR"))
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## 2020 - 2017 == 0 -0.003321 0.003321 -1 0.374
## (Adjusted p values reported -- single-step method)Use Comparison
## `summarise()` has grouped output by 'Year', 'Variable', 'Use'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'Variable', 'Use'. You can override using the `.groups` argument.
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## VOR_use_ 4 -68.42 0.00 1 1 38.70
## VOR_use_null 3 -54.77 13.65 0 1 30.67## Data: VPUseP
## Models:
## VOR_use_null: sdcor ~ 1 + (1 | Year)
## VOR_use_: sdcor ~ Use + (1 | Year)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## VOR_use_null 3 -55.339 -49.853 30.669 -61.339
## VOR_use_ 4 -69.392 -62.078 38.696 -77.392 16.053 1 6.158e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = sdcor ~ Use + (1 | Year), data = VPUseP, REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Homogeneous - Heterogeneous == 0 -0.13815 0.03152 -4.383 1.17e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)