Forage Quality Graphs and Stats
Jonathan Spiess
June 20, 2021
ADG vs patch contrast
Weight Gains
Questions/Hypothesis
Are animals gaining weight each year? -I expect average daily gain to be positive for each livestock class each year
Do weight gains differ between years for any livetock class? -I do not expect years to differ despite variable precipitation
Results
Gain, Hold, or Lose
##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lmer(formula = ADG ~ Year + 0 + (1 | Pasture1), data = HrecEwe,
## REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Year2016 == 0 0.257668 0.006649 38.75 <2e-16 ***
## Year2017 == 0 0.289309 0.006907 41.89 <2e-16 ***
## Year2018 == 0 0.347246 0.006873 50.53 <2e-16 ***
## Year2019 == 0 0.285975 0.007097 40.30 <2e-16 ***
## Year2020 == 0 0.230448 0.007034 32.76 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Confidence Intervals
##
## Fit: lmer(formula = ADG ~ Year + 0 + (1 | Pasture1), data = HrecEwe,
## REML = FALSE)
##
## Quantile = 2.5424
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## Year2016 == 0 0.2577 0.2408 0.2746
## Year2017 == 0 0.2893 0.2717 0.3069
## Year2018 == 0 0.3472 0.3298 0.3647
## Year2019 == 0 0.2860 0.2679 0.3040
## Year2020 == 0 0.2304 0.2126 0.2483## boundary (singular) fit: see ?isSingular##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lmer(formula = ADG ~ Year + 0 + (1 | Pasture1), data = HrecCow,
## REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Year2016 == 0 0.63743 0.07042 9.051 < 2e-16 ***
## Year2017 == 0 0.45246 0.07134 6.342 1.13e-09 ***
## Year2018 == 0 1.28520 0.06954 18.482 < 2e-16 ***
## Year2019 == 0 0.62715 0.07599 8.254 1.11e-15 ***
## Year2020 == 0 0.62208 0.07656 8.126 2.22e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Confidence Intervals
##
## Fit: lmer(formula = ADG ~ Year + 0 + (1 | Pasture1), data = HrecCow,
## REML = FALSE)
##
## Quantile = 2.569
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## Year2016 == 0 0.6374 0.4565 0.8183
## Year2017 == 0 0.4525 0.2692 0.6357
## Year2018 == 0 1.2852 1.1066 1.4638
## Year2019 == 0 0.6271 0.4319 0.8224
## Year2020 == 0 0.6221 0.4254 0.8188##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lmer(formula = ADG ~ Year + 0 + (1 | Pasture1), data = HrecCalf,
## REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## Year2016 == 0 2.98508 0.03921 76.13 <2e-16 ***
## Year2017 == 0 3.09372 0.03896 79.40 <2e-16 ***
## Year2018 == 0 2.62170 0.03875 67.66 <2e-16 ***
## Year2019 == 0 2.95727 0.04116 71.86 <2e-16 ***
## Year2020 == 0 2.96987 0.04198 70.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Confidence Intervals
##
## Fit: lmer(formula = ADG ~ Year + 0 + (1 | Pasture1), data = HrecCalf,
## REML = FALSE)
##
## Quantile = 2.5639
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## Year2016 == 0 2.9851 2.8846 3.0856
## Year2017 == 0 3.0937 2.9938 3.1936
## Year2018 == 0 2.6217 2.5224 2.7210
## Year2019 == 0 2.9573 2.8518 3.0628
## Year2020 == 0 2.9699 2.8622 3.0775ADG CI's
ADG Means
Year Test
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## EweY 7 -3361.01 0.00 1 1 1687.53
## EweNull 3 -3133.30 227.71 0 1 1569.65## Data: HrecEwe
## Models:
## EweNull: ADG ~ 1 + (1 | Pasture1)
## EweY: ADG ~ Year + (1 | Pasture1)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## EweNull 3 -3133.3 -3115.7 1569.7 -3139.3
## EweY 7 -3361.1 -3320.1 1687.5 -3375.1 235.74 4 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## CalfY 7 179.70 0.00 1 1 -82.69
## CalfNull 3 260.19 80.49 0 1 -127.06## Data: HrecCalf
## Models:
## CalfNull: ADG ~ 1 + (1 | Pasture1)
## CalfY: ADG ~ Year + (1 | Pasture1)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## CalfNull 3 260.12 271.68 -127.058 254.12
## CalfY 7 179.37 206.36 -82.685 165.37 88.745 4 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## CowY 7 707.26 0.00 1 1 -346.47
## CowNull 3 774.98 67.72 0 1 -384.45## Data: HrecCow
## Models:
## CowNull: ADG ~ 1 + (1 | Pasture1)
## CowY: ADG ~ Year + (1 | Pasture1)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## CowNull 3 774.91 786.63 -384.45 768.91
## CowY 7 706.95 734.28 -346.47 692.95 75.964 4 1.246e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NIR Ordination
Questions/Hypotheses
How do the measured forage quality parameters relate to each other? - I expected protein to be dissimilar from fibers and lignin
Which treatment and environmental variables influence the ordination? - I expected differences within TSF levels and between months - I expected biomass to be more associated with fibers and lignin - I expected selection index and moisture content to be more associated with protein -
Results Summary
The first axis explains 71% of the variation.
Moisture, Month, TSF, Selection Index and Biomass are significant environmental variables for the forage quality data.
Available biomass vector is more associated with fibers. Selection Index vector is more associated with protein. Moisture vector is opposite of lignin.
TSF: Pairwise factorfit says they are all different. Recent burns has
Ordination Setup
## importance.MDS1 importance.MDS2 importance.MDS3
## Eigenvalue 1.19 0.47 0.01
## Proportion Explained 0.71 0.28 0.01
## Cumulative Proportion 0.71 0.99 1.00##
## ***VECTORS
##
## MDS1 MDS2 r2 Pr(>r)
## Moisture 0.96763 -0.25239 0.6335 0.002 **
## Powell 0.88703 0.46171 0.1246 0.002 **
## KgHa -0.90506 -0.42529 0.2003 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499
##
## ***FACTORS:
##
## Centroids:
## MDS1 MDS2
## TSFRB 0.1109 0.0322
## TSF1to2 -0.0056 -0.0606
## TSF2to3 -0.0730 -0.1032
## TSF3plus -0.1284 -0.0443
## TSFNYB -0.0237 0.0618
## ESDClayey 0.0073 0.0227
## ESDLoamy 0.0148 -0.0202
## ESDSaline Lowland -0.0224 -0.0120
## ESDSandy 0.0096 0.0063
## ESDThin Claypan -0.0182 -0.0827
## MonthMay 0.0769 -0.0019
## MonthJune 0.2541 -0.0566
## MonthJuly 0.0098 -0.0343
## MonthAugust -0.0897 0.0052
## MonthSeptember -0.1193 0.0717
## TreatmentCattle 0.0217 0.0543
## TreatmentSheep -0.0252 -0.0631
##
## Goodness of fit:
## r2 Pr(>r)
## TSF 0.1025 0.002 **
## ESD 0.0063 0.508
## Month 0.2360 0.002 **
## Treatment 0.0451 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Blocks: strata
## Permutation: free
## Number of permutations: 499Ordinations
Figure: NIR Ordi + Vectors
Figure: NIR TSF Ordi
##
## Pairwise comparisons using factor fitting to an ordination
##
## data: NIR.cap3 by NIREnv_2$TSF
## 999 permutations
##
## RB 1to2 2to3 3plus
## 1to2 0.0012 - - -
## 2to3 0.0012 0.0056 - -
## 3plus 0.0012 0.0012 0.0160 -
## NYB 0.0012 0.0012 0.0012 0.0012
##
## P value adjustment method: fdr## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Figure: NIR Month Ordi
Available Biomass (Kg/Ha)
Hypothesis
Recently burned patches will have lower available biomass than other patches, especially not yet burned and 3 years since fire.
Summary
Results
Figure: Biomass Overall
Biomass Model Results
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## KgHaInt 14 3590.71 0.00 1 1 -1781.25
## KgHaT 9 3637.25 46.54 0 1 -1809.58
## KgHaTG 10 3638.61 47.90 0 1 -1809.25
## KgHaNull 5 4002.96 412.25 0 1 -1996.46
## KgHaG 6 4004.32 413.61 0 1 -1996.14## Data: HRECSIOmit
## Models:
## KgHaT: log(KgHa + 1) ~ TSF + (1 | Location/Year/Month)
## KgHaInt: log(KgHa + 1) ~ TSF * Treatment + (1 | Location/Year/Month)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## KgHaT 9 3637.2 3687.5 -1809.6 3619.2
## KgHaInt 14 3590.5 3668.8 -1781.2 3562.5 56.661 5 5.941e-11 ***
## ---
## 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(KgHa + 1) ~ TSF + (1 | Location/Year/Month),
## data = subset(HRECSIOmit, Treatment == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 0.33516 0.05185 6.463 <0.001 ***
## 2to3 - RB == 0 0.42032 0.06095 6.896 <0.001 ***
## 3plus - RB == 0 0.46521 0.08255 5.635 <0.001 ***
## NYB - RB == 0 0.48111 0.04764 10.100 <0.001 ***
## 2to3 - 1to2 == 0 0.08516 0.06198 1.374 0.6331
## 3plus - 1to2 == 0 0.13006 0.08323 1.563 0.5090
## NYB - 1to2 == 0 0.14595 0.05410 2.698 0.0508 .
## 3plus - 2to3 == 0 0.04489 0.08481 0.529 0.9835
## NYB - 2to3 == 0 0.06079 0.06556 0.927 0.8812
## NYB - 3plus == 0 0.01589 0.08827 0.180 0.9998
## ---
## 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(KgHa + 1) ~ TSF + (1 | Location/Year/Month),
## data = subset(HRECSIOmit, Treatment == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 0.75425 0.05769 13.075 < 0.001 ***
## 2to3 - RB == 0 0.95273 0.06681 14.261 < 0.001 ***
## 3plus - RB == 0 1.08377 0.08568 12.649 < 0.001 ***
## NYB - RB == 0 0.56043 0.05432 10.318 < 0.001 ***
## 2to3 - 1to2 == 0 0.19848 0.06861 2.893 0.02965 *
## 3plus - 1to2 == 0 0.32952 0.08672 3.800 0.00138 **
## NYB - 1to2 == 0 -0.19382 0.06123 -3.165 0.01296 *
## 3plus - 2to3 == 0 0.13104 0.08962 1.462 0.57700
## NYB - 2to3 == 0 -0.39229 0.07306 -5.370 < 0.001 ***
## NYB - 3plus == 0 -0.52334 0.09341 -5.603 < 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)ADF
Hypothesis
Recently burned patches will have lower ADF than other patches, especially not yet burned and 3 years since fire.
Summary
Results
Figure: ADF Overall
ADF Overall Models
##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## ADFTG 10 9419.42 0.00 0.53 0.53 -4699.65
## ADFT 9 9420.35 0.93 0.33 0.86 -4701.13
## ADFInt 14 9422.01 2.60 0.14 1.00 -4696.90
## ADFG 6 9769.77 350.35 0.00 1.00 -4878.86
## ADFNull 5 9770.57 351.15 0.00 1.00 -4880.27## Data: HRECSIOmit
## Models:
## ADFT: ADF ~ TSF + (1 | Location/Year/Month)
## ADFTG: ADF ~ TSF + Treatment + (1 | Location/Year/Month)
## ADFInt: ADF ~ TSF * Treatment + (1 | Location/Year/Month)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## ADFT 9 9420.3 9470.6 -4701.1 9402.3
## ADFTG 10 9419.3 9475.3 -4699.7 9399.3 2.9521 1 0.08577 .
## ADFInt 14 9421.8 9500.1 -4696.9 9393.8 5.5062 4 0.23919
## ---
## 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 = ADF ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 2.18428 0.24391 8.955 <0.001 ***
## 2to3 - RB == 0 2.97242 0.28631 10.382 <0.001 ***
## 3plus - RB == 0 3.28390 0.38732 8.479 <0.001 ***
## NYB - RB == 0 2.15293 0.22369 9.624 <0.001 ***
## 2to3 - 1to2 == 0 0.78814 0.29141 2.705 0.0501 .
## 3plus - 1to2 == 0 1.09962 0.39073 2.814 0.0369 *
## NYB - 1to2 == 0 -0.03135 0.25377 -0.124 0.9999
## 3plus - 2to3 == 0 0.31148 0.39866 0.781 0.9329
## NYB - 2to3 == 0 -0.81949 0.30695 -2.670 0.0552 .
## NYB - 3plus == 0 -1.13097 0.41310 -2.738 0.0457 *
## ---
## 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 = ADF ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 2.6093 0.2235 11.676 < 0.001 ***
## 2to3 - RB == 0 2.9632 0.2584 11.469 < 0.001 ***
## 3plus - RB == 0 3.1265 0.3307 9.455 < 0.001 ***
## NYB - RB == 0 1.8031 0.2096 8.603 < 0.001 ***
## 2to3 - 1to2 == 0 0.3538 0.2656 1.332 0.66192
## 3plus - 1to2 == 0 0.5172 0.3350 1.544 0.52364
## NYB - 1to2 == 0 -0.8062 0.2359 -3.418 0.00542 **
## 3plus - 2to3 == 0 0.1633 0.3468 0.471 0.98947
## NYB - 2to3 == 0 -1.1601 0.2808 -4.131 < 0.001 ***
## NYB - 3plus == 0 -1.3234 0.3586 -3.691 0.00210 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)NDF
Hypothesis
Recently burned patches will have lower NDF than other patches, especially not yet burned and 3 years since fire.
Summary
Results
Figure: NDF Overall
NDF Overall Models
print(aictab(cand.set = NDF.mods, modnames = NDF.mod.names))##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## NDFInt 14 11044.53 0.00 0.58 0.58 -5508.16
## NDFTG 10 11045.32 0.79 0.39 0.98 -5512.60
## NDFT 9 11051.09 6.56 0.02 1.00 -5516.50
## NDFG 6 11270.35 225.82 0.00 1.00 -5629.15
## NDFNull 5 11276.16 231.64 0.00 1.00 -5633.07anova(NDFTG, NDFT, NDFInt)## Data: HRECSIOmit
## Models:
## NDFT: NDF ~ TSF + (1 | Location/Year/Month)
## NDFTG: NDF ~ TSF + Treatment + (1 | Location/Year/Month)
## NDFInt: NDF ~ TSF * Treatment + (1 | Location/Year/Month)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## NDFT 9 11051 11101 -5516.5 11033
## NDFTG 10 11045 11101 -5512.6 11025 7.7965 1 0.005235 **
## NDFInt 14 11044 11123 -5508.2 11016 8.8891 4 0.063932 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(Mult_NDFtC)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = NDF ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 2.8247 0.4050 6.975 <0.001 ***
## 2to3 - RB == 0 3.8974 0.4751 8.203 <0.001 ***
## 3plus - RB == 0 4.4087 0.6425 6.862 <0.001 ***
## NYB - RB == 0 2.6438 0.3711 7.124 <0.001 ***
## 2to3 - 1to2 == 0 1.0727 0.4838 2.217 0.1654
## 3plus - 1to2 == 0 1.5840 0.6483 2.443 0.0988 .
## NYB - 1to2 == 0 -0.1809 0.4207 -0.430 0.9925
## 3plus - 2to3 == 0 0.5114 0.6618 0.773 0.9355
## NYB - 2to3 == 0 -1.2535 0.5086 -2.465 0.0939 .
## NYB - 3plus == 0 -1.7649 0.6844 -2.579 0.0704 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)summary(Mult_NDFtS)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = NDF ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 3.10744 0.29044 10.699 < 1e-04 ***
## 2to3 - RB == 0 3.40599 0.33616 10.132 < 1e-04 ***
## 3plus - RB == 0 3.04894 0.43076 7.078 < 1e-04 ***
## NYB - RB == 0 1.83279 0.27312 6.711 < 1e-04 ***
## 2to3 - 1to2 == 0 0.29856 0.34531 0.865 0.906184
## 3plus - 1to2 == 0 -0.05849 0.43613 -0.134 0.999924
## NYB - 1to2 == 0 -1.27464 0.30779 -4.141 0.000254 ***
## 3plus - 2to3 == 0 -0.35705 0.45101 -0.792 0.930309
## NYB - 2to3 == 0 -1.57320 0.36691 -4.288 0.000174 ***
## NYB - 3plus == 0 -1.21615 0.46879 -2.594 0.068071 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)ADL
Hypothesis
Recently burned patches will have lower ADL than other patches, especially not yet burned and 3 years since fire.
Summary
Results
Figure: ADL Overall
ADL Overall Models
print(aictab(cand.set = ADL.mods, modnames = ADL.mod.names))##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## ADLT 9 4093.91 0.00 0.65 0.65 -2037.91
## ADLTG 10 4095.46 1.56 0.30 0.95 -2037.68
## ADLInt 14 4098.89 4.99 0.05 1.00 -2035.34
## ADLNull 5 4361.23 267.32 0.00 1.00 -2175.60
## ADLG 6 4362.77 268.87 0.00 1.00 -2175.37anova(ADLTG, ADLT, ADLInt)## Data: HRECSIOmit
## Models:
## ADLT: ADL ~ TSF + (1 | Location/Year/Month)
## ADLTG: ADL ~ TSF + Treatment + (1 | Location/Year/Month)
## ADLInt: ADL ~ TSF * Treatment + (1 | Location/Year/Month)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## ADLT 9 4093.8 4144.2 -2037.9 4075.8
## ADLTG 10 4095.4 4151.3 -2037.7 4075.4 0.4641 1 0.4957
## ADLInt 14 4098.7 4177.0 -2035.3 4070.7 4.6716 4 0.3227summary(Mult_ADFtC)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = ADF ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 2.18428 0.24391 8.955 <0.001 ***
## 2to3 - RB == 0 2.97242 0.28631 10.382 <0.001 ***
## 3plus - RB == 0 3.28390 0.38732 8.479 <0.001 ***
## NYB - RB == 0 2.15293 0.22369 9.624 <0.001 ***
## 2to3 - 1to2 == 0 0.78814 0.29141 2.705 0.0504 .
## 3plus - 1to2 == 0 1.09962 0.39073 2.814 0.0372 *
## NYB - 1to2 == 0 -0.03135 0.25377 -0.124 0.9999
## 3plus - 2to3 == 0 0.31148 0.39866 0.781 0.9329
## NYB - 2to3 == 0 -0.81949 0.30695 -2.670 0.0553 .
## NYB - 3plus == 0 -1.13097 0.41310 -2.738 0.0457 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)summary(Mult_ADFtS)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = ADF ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 2.6093 0.2235 11.676 < 0.001 ***
## 2to3 - RB == 0 2.9632 0.2584 11.469 < 0.001 ***
## 3plus - RB == 0 3.1265 0.3307 9.455 < 0.001 ***
## NYB - RB == 0 1.8031 0.2096 8.603 < 0.001 ***
## 2to3 - 1to2 == 0 0.3538 0.2656 1.332 0.66197
## 3plus - 1to2 == 0 0.5172 0.3350 1.544 0.52361
## NYB - 1to2 == 0 -0.8062 0.2359 -3.418 0.00524 **
## 3plus - 2to3 == 0 0.1633 0.3468 0.471 0.98947
## NYB - 2to3 == 0 -1.1601 0.2808 -4.131 < 0.001 ***
## NYB - 3plus == 0 -1.3234 0.3586 -3.691 0.00213 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)CP
Hypothesis
Recently burned patches will have higher CP than other patches, especially not yet burned and 3 years since fire.
Summary
Results
Figure: CP Overall
CP Overall Models
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingularprint(aictab(cand.set = CP.mods,
modnames = CP.mod.names))##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## CPInt 14 -618.72 0.00 1 1 323.47
## CPTG 10 -561.28 57.44 0 1 290.69
## CPT 9 -557.23 61.49 0 1 287.66
## CPG 6 -170.42 448.30 0 1 91.23
## CPNull 5 -166.36 452.36 0 1 88.20anova(CPInt, CPTG)## Data: HRECSIOmit
## Models:
## CPTG: log(CP + 1) ~ TSF + Treatment + (1 | Location/Year/Month)
## CPInt: log(CP + 1) ~ TSF * Treatment + (1 | Location/Year/Month)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## CPTG 10 -561.39 -505.43 290.69 -581.39
## CPInt 14 -618.93 -540.58 323.47 -646.93 65.544 4 1.976e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(Mult_CPtC) #Unburned > RB & 1YSF##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(CP + 1) ~ TSF + (1 | Location/Year/Month),
## data = subset(HRECSIOmit, Treatment == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 -0.182507 0.020261 -9.008 < 0.001 ***
## 2to3 - RB == 0 -0.297727 0.023835 -12.491 < 0.001 ***
## 3plus - RB == 0 -0.410821 0.032304 -12.717 < 0.001 ***
## NYB - RB == 0 0.002409 0.018636 0.129 0.99993
## 2to3 - 1to2 == 0 -0.115220 0.024223 -4.757 < 0.001 ***
## 3plus - 1to2 == 0 -0.228315 0.032551 -7.014 < 0.001 ***
## NYB - 1to2 == 0 0.184916 0.021191 8.726 < 0.001 ***
## 3plus - 2to3 == 0 -0.113094 0.033147 -3.412 0.00547 **
## NYB - 2to3 == 0 0.300136 0.025698 11.679 < 0.001 ***
## NYB - 3plus == 0 0.413231 0.034601 11.943 < 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)summary(Mult_CPtS)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(CP + 1) ~ TSF + (1 | Location/Year/Month),
## data = subset(HRECSIOmit, Treatment == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 -0.19397 0.01683 -11.525 <0.001 ***
## 2to3 - RB == 0 -0.23343 0.01950 -11.970 <0.001 ***
## 3plus - RB == 0 -0.26438 0.02502 -10.567 <0.001 ***
## NYB - RB == 0 -0.14780 0.01587 -9.313 <0.001 ***
## 2to3 - 1to2 == 0 -0.03946 0.02002 -1.972 0.2709
## 3plus - 1to2 == 0 -0.07041 0.02531 -2.782 0.0410 *
## NYB - 1to2 == 0 0.04617 0.01791 2.578 0.0711 .
## 3plus - 2to3 == 0 -0.03095 0.02615 -1.183 0.7531
## NYB - 2to3 == 0 0.08563 0.02138 4.005 <0.001 ***
## NYB - 3plus == 0 0.11658 0.02733 4.266 <0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Moisture Content
Hypothesis
Recently burned patches will have higher moisture content than other patches, especially not yet burned and 3 years since fire.
Summary
Results
Figure: Moisture Content
Moisture Overall Models
print(aictab(cand.set = Moisture.mods, modnames = Moisture.mod.names))##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## MoistureT 9 13872.67 0.00 0.58 0.58 -6927.29
## MoistureTG 10 13873.36 0.69 0.41 0.99 -6926.63
## MoistureInt 14 13880.51 7.83 0.01 1.00 -6926.15
## MoistureNull 5 14249.23 376.56 0.00 1.00 -7119.60
## MoistureG 6 14249.87 377.20 0.00 1.00 -7118.92anova(MoistureTG, MoistureT, MoistureInt)## Data: HRECSIOmit
## Models:
## MoistureT: Moisture ~ TSF + (1 | Location/Year/Month)
## MoistureTG: Moisture ~ TSF + Treatment + (1 | Location/Year/Month)
## MoistureInt: Moisture ~ TSF * Treatment + (1 | Location/Year/Month)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## MoistureT 9 13873 13923 -6927.3 13855
## MoistureTG 10 13873 13929 -6926.6 13853 1.3286 1 0.2490
## MoistureInt 14 13880 13959 -6926.1 13852 0.9594 4 0.9159summary(Mult_MoisturetC)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = Moisture ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Cattle"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 -6.2908 0.6356 -9.897 < 0.001 ***
## 2to3 - RB == 0 -8.9398 0.7490 -11.936 < 0.001 ***
## 3plus - RB == 0 -10.0099 1.0165 -9.847 < 0.001 ***
## NYB - RB == 0 -6.6776 0.5860 -11.395 < 0.001 ***
## 2to3 - 1to2 == 0 -2.6490 0.7602 -3.484 0.00424 **
## 3plus - 1to2 == 0 -3.7190 1.0233 -3.634 0.00246 **
## NYB - 1to2 == 0 -0.3867 0.6676 -0.579 0.97691
## 3plus - 2to3 == 0 -1.0700 1.0405 -1.028 0.83535
## NYB - 2to3 == 0 2.2623 0.8112 2.789 0.03981 *
## NYB - 3plus == 0 3.3323 1.0924 3.050 0.01800 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)summary(Mult_MoisturetS)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = Moisture ~ TSF + (1 | Location/Year/Month), data = subset(HRECSIOmit,
## Treatment == "Sheep"), REML = FALSE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 -6.8583 0.7420 -9.243 <0.001 ***
## 2to3 - RB == 0 -8.4641 0.8609 -9.832 <0.001 ***
## 3plus - RB == 0 -10.4419 1.1059 -9.442 <0.001 ***
## NYB - RB == 0 -6.6676 0.7018 -9.501 <0.001 ***
## 2to3 - 1to2 == 0 -1.6058 0.8828 -1.819 0.3522
## 3plus - 1to2 == 0 -3.5836 1.1178 -3.206 0.0113 *
## NYB - 1to2 == 0 0.1907 0.7933 0.240 0.9992
## 3plus - 2to3 == 0 -1.9778 1.1534 -1.715 0.4141
## NYB - 2to3 == 0 1.7964 0.9485 1.894 0.3107
## NYB - 3plus == 0 3.7743 1.2128 3.112 0.0150 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)Selection Index
Hypothesis
Recently burned patches will have a higher selection index than other patches, especially not yet burned and 3 years since fire.
Summary
Results
Figure: Selection Index Overall
Selection Index Overall Model
print(aictab(cand.set = SI.mods, modnames = SI.mod.names))##
## Model selection based on AICc:
##
## K AICc Delta_AICc AICcWt Cum.Wt LL
## SIT 9 -1237.93 0.00 0.64 0.64 628.01
## SITG 10 -1235.91 2.02 0.23 0.87 628.01
## SIInt 14 -1234.74 3.19 0.13 1.00 631.48
## SINull 5 -1078.43 159.50 0.00 1.00 544.23
## SIG 6 -1076.41 161.52 0.00 1.00 544.23anova(SITG, SIT, SIInt)## Data: HRECSIOmit
## Models:
## SIT: (Powell + 0.001) ~ TSF + (1 | Location/Year/Month)
## SITG: (Powell + 0.001) ~ TSF + Treatment + (1 | Location/Year/Month)
## SIInt: (Powell + 0.001) ~ TSF * Treatment + (1 | Location/Year/Month)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## SIT 9 -1238 -1187.7 628.01 -1256
## SITG 10 -1236 -1180.1 628.01 -1256 0.0028 1 0.9581
## SIInt 14 -1235 -1156.6 631.48 -1263 6.9302 4 0.1396summary(Mult_SITC)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = (Powell + 0.001) ~ TSF + (1 | Location/Year/Month),
## data = subset(HRECSIOmit, Treatment == "Cattle"), family = Gamma)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 0.9182 0.2135 4.300 < 0.001 ***
## 2to3 - RB == 0 1.0201 0.2732 3.734 0.00136 **
## 3plus - RB == 0 2.8323 0.8314 3.407 0.00453 **
## NYB - RB == 0 0.7609 0.1506 5.052 < 0.001 ***
## 2to3 - 1to2 == 0 0.1019 0.3342 0.305 0.99767
## 3plus - 1to2 == 0 1.9140 0.8534 2.243 0.13664
## NYB - 1to2 == 0 -0.1573 0.2444 -0.644 0.96091
## 3plus - 2to3 == 0 1.8121 0.8702 2.082 0.19297
## NYB - 2to3 == 0 -0.2593 0.2979 -0.870 0.89042
## NYB - 3plus == 0 -2.0714 0.8399 -2.466 0.08014 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)summary(Mult_SITS)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = (Powell + 0.001) ~ TSF + (1 | Location/Year/Month),
## data = subset(HRECSIOmit, Treatment == "Sheep"), family = Gamma)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 1to2 - RB == 0 1.1331 0.2242 5.054 < 0.001 ***
## 2to3 - RB == 0 2.1193 0.4259 4.976 < 0.001 ***
## 3plus - RB == 0 3.8482 0.9421 4.085 < 0.001 ***
## NYB - RB == 0 0.6242 0.1280 4.876 < 0.001 ***
## 2to3 - 1to2 == 0 0.9862 0.4741 2.080 0.18775
## 3plus - 1to2 == 0 2.7150 0.9649 2.814 0.02939 *
## NYB - 1to2 == 0 -0.5089 0.2446 -2.081 0.18718
## 3plus - 2to3 == 0 1.7289 1.0306 1.678 0.39189
## NYB - 2to3 == 0 -1.4951 0.4369 -3.422 0.00410 **
## NYB - 3plus == 0 -3.2239 0.9472 -3.404 0.00437 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)VP Calc
SI (No good)
knitr::kable(SItrim)| Pasture | Model | Variable | Year | grp | var1 | var2 | vcov | sdcor |
|---|---|---|---|---|---|---|---|---|
| CC | SI20CC | SI | 2020 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| CS | SI20CS | SI | 2020 | Month:Patch | (Intercept) | NA | 1.508219e+05 | 388.3579554 |
| FNC | SI20FNC | SI | 2020 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FNS | SI20FNS | SI | 2020 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSC | SI20FSC | SI | 2020 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSS | SI20FSS | SI | 2020 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| CC | SI19CC | SI | 2019 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| CS | SI19CS | SI | 2019 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FNC | SI19FNC | SI | 2019 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FNS | SI19FNS | SI | 2019 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSC | SI19FSC | SI | 2019 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSS | SI19FSS | SI | 2019 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| CC | SI18CC | SI | 2018 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| CS | SI18CS | SI | 2018 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FNC | SI18FNC | SI | 2018 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FNS | SI18FNS | SI | 2018 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSC | SI18FSC | SI | 2018 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSS | SI18FSS | SI | 2018 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| CC | SI17CC | SI | 2017 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| CS | SI17CS | SI | 2017 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FNC | SI17FNC | SI | 2017 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FNS | SI17FNS | SI | 2017 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSC | SI17FSC | SI | 2017 | Month:Patch | (Intercept) | NA | 0.000000e+00 | 0.0000000 |
| FSS | SI17FSS | SI | 2017 | Month:Patch | (Intercept) | NA | 4.637580e-02 | 0.2153505 |
Kg (worked)
knitr::kable(Kgtrim)| Pasture | Model | Variable | Year | grp | var1 | var2 | vcov | sdcor |
|---|---|---|---|---|---|---|---|---|
| CC | Kg20CC | Kg | 2020 | Month:Patch | (Intercept) | NA | 0.3471960 | 0.5892334 |
| CS | Kg20CS | Kg | 2020 | Month:Patch | (Intercept) | NA | 1.1933079 | 1.0923863 |
| FNC | Kg20FNC | Kg | 2020 | Month:Patch | (Intercept) | NA | 0.3972905 | 0.6303098 |
| FNS | Kg20FNS | Kg | 2020 | Month:Patch | (Intercept) | NA | 0.1480919 | 0.3848271 |
| FSC | Kg20FSC | Kg | 2020 | Month:Patch | (Intercept) | NA | 0.1147279 | 0.3387151 |
| FSS | Kg20FSS | Kg | 2020 | Month:Patch | (Intercept) | NA | 0.3575610 | 0.5979641 |
| CC | Kg19CC | Kg | 2019 | Month:Patch | (Intercept) | NA | 0.0211822 | 0.1455411 |
| CS | Kg19CS | Kg | 2019 | Month:Patch | (Intercept) | NA | 0.0340281 | 0.1844671 |
| FNC | Kg19FNC | Kg | 2019 | Month:Patch | (Intercept) | NA | 0.0671740 | 0.2591795 |
| FNS | Kg19FNS | Kg | 2019 | Month:Patch | (Intercept) | NA | 0.1166756 | 0.3415781 |
| FSC | Kg19FSC | Kg | 2019 | Month:Patch | (Intercept) | NA | 0.2458152 | 0.4957976 |
| FSS | Kg19FSS | Kg | 2019 | Month:Patch | (Intercept) | NA | 0.0470063 | 0.2168094 |
| CC | Kg18CC | Kg | 2018 | Month:Patch | (Intercept) | NA | 0.0000000 | 0.0000000 |
| CS | Kg18CS | Kg | 2018 | Month:Patch | (Intercept) | NA | 0.0000000 | 0.0000000 |
| FNC | Kg18FNC | Kg | 2018 | Month:Patch | (Intercept) | NA | 0.0534835 | 0.2312651 |
| FNS | Kg18FNS | Kg | 2018 | Month:Patch | (Intercept) | NA | 0.1403747 | 0.3746661 |
| FSC | Kg18FSC | Kg | 2018 | Month:Patch | (Intercept) | NA | 0.0729335 | 0.2700620 |
| FSS | Kg18FSS | Kg | 2018 | Month:Patch | (Intercept) | NA | 0.1081251 | 0.3288238 |
| CC | Kg17CC | Kg | 2017 | Month:Patch | (Intercept) | NA | 0.0000000 | 0.0000000 |
| CS | Kg17CS | Kg | 2017 | Month:Patch | (Intercept) | NA | 0.0000000 | 0.0000000 |
| FNC | Kg17FNC | Kg | 2017 | Month:Patch | (Intercept) | NA | 0.1839535 | 0.4288980 |
| FNS | Kg17FNS | Kg | 2017 | Month:Patch | (Intercept) | NA | 0.0060304 | 0.0776555 |
| FSC | Kg17FSC | Kg | 2017 | Month:Patch | (Intercept) | NA | 0.1365853 | 0.3695745 |
| FSS | Kg17FSS | Kg | 2017 | Month:Patch | (Intercept) | NA | 0.0896470 | 0.2994110 |