Floral Abundance and Richness
RichCountLM <- lmer(FloralCountMean ~ FloralRichnessMean + (1|Plot), data=SRFsummary2, REML = FALSE)
summary(RichCountLM)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: FloralCountMean ~ FloralRichnessMean + (1 | Plot)
## Data: SRFsummary2
##
## AIC BIC logLik deviance df.resid
## 384.8 392.8 -188.4 376.8 50
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.99518 -0.55628 -0.08825 0.45792 2.90368
##
## Random effects:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 35.23 5.935
## Residual 41.08 6.410
## Number of obs: 54, groups: Plot, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -20.433 3.126 22.281 -6.537 1.33e-06 ***
## FloralRichnessMean 15.985 1.155 24.985 13.843 3.20e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## FlrlRchnssM -0.850
car::Anova(RichCountLM)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: FloralCountMean
## Chisq Df Pr(>Chisq)
## FloralRichnessMean 191.63 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
RichCountLM <- lmer(log(FloralCountMean+1) ~ log(FloralRichnessMean+1) + (1|Plot), data=SRFsummary2, REML = FALSE)
summary(RichCountLM)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: log(FloralCountMean + 1) ~ log(FloralRichnessMean + 1) + (1 |
## Plot)
## Data: SRFsummary2
##
## AIC BIC logLik deviance df.resid
## 82.8 90.8 -37.4 74.8 50
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.03343 -0.72966 -0.07385 0.42038 2.24884
##
## Random effects:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 0.01978 0.1407
## Residual 0.21593 0.4647
## Number of obs: 54, groups: Plot, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.2486 0.2606 23.1212 -0.954 0.35
## log(FloralRichnessMean + 1) 2.3005 0.2208 23.6070 10.420 2.62e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## lg(FlrRM+1) -0.962
car::Anova(RichCountLM)
## Warning in printHypothesis(L, rhs, names(b)): one or more coefficients in the hypothesis include
## arithmetic operators in their names;
## the printed representation of the hypothesis will be omitted
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: log(FloralCountMean + 1)
## Chisq Df Pr(>Chisq)
## log(FloralRichnessMean + 1) 108.58 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(SRFsummary2, aes(x=FloralRichnessMean, y=FloralCountMean, color=PlotType))+
geom_point(size=3)+
labs(y="Floral Density ", x="Floral Richness", title = "Transect Level")+
theme_bw()+
theme(legend.position = "bottom",
axis.title=element_text(size=10, face="bold"),
axis.text=element_text(size=10),
legend.text=element_text(size=8),
legend.title = element_text(size=8),
panel.grid.major.x = element_blank())
ggplot(SRFsummary2, aes(x=FloralRichnessMean, y=FloralCountMean))+
geom_point(size=3, aes(color=PlotType))+
geom_smooth(method = lm, se=FALSE)+
labs(y="Floral Density ", x="Floral Richness", title = "Transect Level")+
theme_bw()+
theme(legend.position = "bottom",
axis.title=element_text(size=10, face="bold"),
axis.text=element_text(size=10),
legend.text=element_text(size=8),
legend.title = element_text(size=8),
panel.grid.major.x = element_blank())
## `geom_smooth()` using formula = 'y ~ x'
ggplot(SRFsummary2, aes(x=log(FloralRichnessMean+1), y=log(FloralCountMean+1)))+
geom_point(size=3, aes(color=PlotType))+
geom_smooth(method = lm, se=FALSE)+
labs(y="log Floral Density ", x="log Floral Richness", title = "Transect Level")+
theme_bw()+
theme(legend.position = "bottom",
axis.title=element_text(size=10, face="bold"),
axis.text=element_text(size=10),
legend.text=element_text(size=8),
legend.title = element_text(size=8),
panel.grid.major.x = element_blank())
## `geom_smooth()` using formula = 'y ~ x'
Plot Type Ordination
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1173949
## Run 1 stress 0.1180226
## Run 2 stress 0.1193567
## Run 3 stress 0.1157013
## ... New best solution
## ... Procrustes: rmse 0.06892919 max resid 0.2496462
## Run 4 stress 0.1157015
## ... Procrustes: rmse 0.0003843736 max resid 0.001236102
## ... Similar to previous best
## Run 5 stress 0.1157016
## ... Procrustes: rmse 0.0003775107 max resid 0.00204173
## ... Similar to previous best
## Run 6 stress 0.1176667
## Run 7 stress 0.1193567
## Run 8 stress 0.1176663
## Run 9 stress 0.1173957
## Run 10 stress 0.117395
## Run 11 stress 0.1157012
## ... New best solution
## ... Procrustes: rmse 0.0003633391 max resid 0.00137761
## ... Similar to previous best
## Run 12 stress 0.1193607
## Run 13 stress 0.1180205
## Run 14 stress 0.1157018
## ... Procrustes: rmse 0.0002536976 max resid 0.000897079
## ... Similar to previous best
## Run 15 stress 0.1157012
## ... Procrustes: rmse 0.0004397649 max resid 0.001306728
## ... Similar to previous best
## Run 16 stress 0.117395
## Run 17 stress 0.1159858
## ... Procrustes: rmse 0.01018069 max resid 0.06507441
## Run 18 stress 0.1157016
## ... Procrustes: rmse 0.0005735584 max resid 0.001910943
## ... Similar to previous best
## Run 19 stress 0.1173947
## Run 20 stress 0.1208159
## *** Best solution repeated 4 times
##
## Call:
## metaMDS(comm = OrdiFlowerProp5, distance = "bray", k = 3, trymax = 50)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(OrdiFlowerProp5))
## Distance: bray
##
## Dimensions: 3
## Stress: 0.1157012
## Stress type 1, weak ties
## Best solution was repeated 4 times in 20 tries
## The best solution was from try 11 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(OrdiFlowerProp5))'
## Square root transformation
## Wisconsin double standardization
## Using step-across dissimilarities:
## Too long or NA distances: 397 out of 1431 (27.7%)
## Stepping across 1431 dissimilarities...
## Connectivity of distance matrix with threshold dissimilarity 1
## Data are connected
##
## Call:
## capscale(formula = OrdiFlowerProp5 ~ 1, distance = "bray", metaMDSdist = "true")
##
## Partitioning of squared Bray shortest distance:
## Inertia Proportion
## Total 27.36 1
## Unconstrained 27.36 1
##
## Eigenvalues, and their contribution to the squared Bray shortest distance
##
## Importance of components:
## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7
## Eigenvalue 8.7040 4.7469 2.9521 2.8259 1.91533 1.23264 0.86399
## Proportion Explained 0.3182 0.1735 0.1079 0.1033 0.07002 0.04506 0.03158
## Cumulative Proportion 0.3182 0.4917 0.5996 0.7029 0.77295 0.81801 0.84959
## MDS8 MDS9 MDS10 MDS11 MDS12 MDS13 MDS14
## Eigenvalue 0.67818 0.65154 0.57064 0.43976 0.39439 0.244935 0.219884
## Proportion Explained 0.02479 0.02382 0.02086 0.01608 0.01442 0.008954 0.008038
## Cumulative Proportion 0.87438 0.89820 0.91906 0.93513 0.94955 0.958506 0.966544
## MDS15 MDS16 MDS17 MDS18 MDS19 MDS20
## Eigenvalue 0.205423 0.147601 0.111641 0.110741 0.088447 0.065533
## Proportion Explained 0.007509 0.005396 0.004081 0.004048 0.003233 0.002396
## Cumulative Proportion 0.974053 0.979449 0.983530 0.987578 0.990811 0.993207
## MDS21 MDS22 MDS23 MDS24 MDS25 MDS26
## Eigenvalue 0.052422 0.039417 0.033547 0.0254858 0.0209051 0.0140497
## Proportion Explained 0.001916 0.001441 0.001226 0.0009317 0.0007642 0.0005136
## Cumulative Proportion 0.995123 0.996564 0.997791 0.9987222 0.9994864 1.0000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 5.727091
##
##
## Species scores
##
## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6
## ACMIO 1.7536 1.12629 0.296182 1.76740 0.0439169 -0.425027
## ANAN2 0.9157 0.90010 -0.914576 -0.50861 0.2518268 -0.154936
## ANMI3 -0.4367 0.21896 0.155369 -0.09545 -0.0802496 -0.027410
## ANRO2 -0.2721 0.04711 0.001101 -0.01601 -0.1303093 -0.017306
## CAFL7 -0.6755 0.43161 -0.018710 -0.16354 0.1639007 -0.062752
## ERCO4 -0.3033 0.12424 -0.010827 -0.03502 -0.0693305 0.018794
## GETR -0.5840 0.58299 -0.020672 -0.13082 0.1992779 0.081269
## GEVI2 0.2044 -1.15218 -1.038886 0.21732 -0.2931006 0.240098
## LUCA -2.1042 -1.32470 -0.390632 0.34850 0.1203834 -0.536088
## LUSE4 1.4660 -1.26738 0.738900 -0.71858 0.8883028 0.082543
## PEGA3 0.6446 -0.25014 0.888376 -0.47876 -1.2146982 -0.346348
## PHMU3 -0.4457 0.14737 -0.040159 -0.05831 -0.0211250 0.008171
## PHLO2 -0.2905 0.35339 -0.123909 -0.15193 0.2009611 0.030070
## POGR9 0.8227 -0.05849 -0.365374 0.21588 -0.6024668 1.073460
## PSJA2 0.4671 0.15113 -0.332246 -0.35345 0.0002673 -0.302088
## RAES -0.8146 -0.20926 0.199692 0.03479 0.1740181 0.138167
## TOPA2 -0.3476 0.17898 0.976374 0.12658 0.3684249 0.199382
##
##
## Site scores (weighted sums of species scores)
##
## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6
## 1 -1.79274 0.84181 0.012804 -0.36223 -0.63854 0.326644
## 2 -1.84547 0.74536 0.008705 -0.29122 -0.64218 0.408153
## 3 -1.74079 0.98917 -0.050994 -0.63707 -0.66756 0.548637
## 4 0.25159 0.99463 -0.050654 -0.29595 -0.51523 0.345271
## 5 0.69527 0.45175 0.051084 1.26390 -0.64958 1.736954
## 6 0.68587 0.46087 0.076729 1.44854 -0.35162 1.140012
## 7 -0.59306 1.27377 -0.736483 -1.28582 1.30581 -0.042186
## 8 -0.25596 1.25446 -0.751881 -0.43421 1.08511 0.122539
## 9 -0.60177 1.22268 -0.058712 -0.54435 0.73847 0.094650
## 10 0.14537 -0.42517 -0.941326 -0.23886 -0.72205 1.360792
## 11 0.45638 -0.70744 -0.677675 -0.19358 0.87865 -0.273619
## 12 0.52302 -0.58199 -0.449090 -0.25974 1.03945 -0.748559
## 13 0.45446 0.69446 -0.272633 -0.36093 -0.93668 -0.317997
## 14 0.53808 0.51209 0.101424 0.10790 -0.94152 0.276445
## 15 0.42144 0.24087 -0.974875 0.13976 0.07664 0.395099
## 16 0.33303 -0.78425 -0.466558 0.33935 0.19500 0.984696
## 17 0.46258 -0.35426 -0.998062 -0.38663 0.62445 0.600599
## 18 0.30569 -0.52998 -1.154508 -0.42368 0.43124 1.187986
## 19 -0.49923 -0.63210 0.048070 0.39101 -0.90739 -0.989820
## 20 -0.41203 -1.29074 -0.067445 0.18020 -1.67421 -0.989339
## 21 -0.99195 -1.33528 -1.030265 0.63236 0.12369 -0.148757
## 22 0.52313 -0.77874 1.375607 -0.87663 -0.07411 1.022873
## 23 -0.31861 0.09578 2.102311 -0.69843 -1.19960 -0.744263
## 24 0.70162 -1.09085 1.264944 -1.37281 -0.38798 -0.520553
## 25 0.55462 -0.41590 0.076529 -0.53297 -0.93727 -0.059303
## 26 0.61264 0.57844 -0.444845 -1.05742 -0.84499 -0.714668
## 27 0.73355 1.20577 -1.052803 -1.37367 0.24942 -0.938051
## 28 -1.49790 -0.99929 0.988800 0.23049 1.28109 0.417810
## 29 -1.47548 -0.88834 1.265492 0.26649 1.04986 0.411330
## 30 -0.96817 -1.24559 -1.324120 0.38964 0.16681 -0.665015
## 31 -0.98746 0.67265 -0.341344 0.10478 0.29128 -0.964069
## 32 -1.28035 0.44871 0.918688 -0.27370 0.59596 -0.607261
## 33 -1.62371 -1.42558 -0.895003 0.59116 -0.05580 -1.138948
## 34 0.56546 0.66665 0.188809 1.93560 -0.08310 -1.154524
## 35 0.56546 0.66665 0.188809 1.93560 -0.08310 -1.154524
## 36 1.22940 -1.39201 1.241344 -0.90704 1.30843 0.191352
## 37 0.45448 0.40531 -0.688522 0.78605 0.51788 -0.884746
## 38 0.64241 0.54377 0.074718 0.03568 1.07763 0.012134
## 39 0.57413 0.81045 -0.315254 0.68867 0.23173 -0.002005
## 40 0.64075 -0.04112 -0.065340 -0.32772 -0.25149 -0.594731
## 41 0.48546 0.59963 0.240571 -0.44512 -0.82500 -0.561736
## 42 0.69630 0.02620 0.463489 -0.86390 -0.27468 -1.214803
## 43 0.49320 -0.11931 1.240231 0.60668 1.35877 0.314595
## 44 0.25757 -0.44042 -0.226045 0.61507 -1.02953 0.219933
## 45 0.33475 -0.32147 -0.461810 1.80006 0.27554 -0.569427
## 46 0.55397 -0.27433 -0.433108 -0.62499 0.20440 -0.022248
## 47 0.50137 -0.71321 -0.357530 -1.18941 0.34932 -0.684204
## 48 0.48780 -0.73555 -0.390659 0.11493 0.81866 0.443321
## 49 0.38151 0.13548 1.597664 0.74331 -0.43250 -0.973234
## 50 -0.19254 0.91685 1.291837 0.79522 0.68906 0.716606
## 51 -0.04782 1.02662 0.906041 0.42757 0.87377 0.523901
## 52 -0.14732 -0.34372 -0.371818 0.14833 -0.92937 1.495841
## 53 0.03979 -0.22423 -0.143605 -0.22068 -1.48669 1.168846
## 54 -0.02982 -0.39002 0.468267 -0.23960 -0.29631 1.211573
##
## ***FACTORS:
##
## Centroids:
## NMDS1 NMDS2 NMDS3
## PlotTypeGrass -0.5176 0.5460 -0.1464
## PlotTypeOpen Conifer 0.3278 -0.1403 -0.3443
## PlotTypeSagebrush 0.1572 -0.3889 0.0179
## PlotTypeTall Forb 0.0109 -0.0056 0.1576
##
## Goodness of fit:
## r2 Pr(>r)
## PlotType 0.1766 0.006 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 499
##
## Pairwise comparisons using factor fitting to an ordination
##
## data: OrdiFlower5.MDSb by OrdiEnvLines$PlotType
## 999 permutations
##
## Grass Open Conifer Sagebrush
## Open Conifer 0.009 - -
## Sagebrush 0.009 0.402 -
## Tall Forb 0.038 0.402 0.402
##
## P value adjustment method: fdr
##
## ***VECTORS
##
## NMDS1 NMDS2 NMDS3 r2 Pr(>r)
## Slope_percent -0.50668 -0.34384 0.79060 0.2965 0.002 **
## Aspect_degrees -0.39470 -0.16107 -0.90458 0.2230 0.012 *
## Elevation_meters -0.93732 0.27653 -0.21205 0.6830 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 499
