Correlation between FOI and Environmental Variables

Maps of Villages by Environment

Human FOI Correlation

Annual Precipitation

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ `Annual Precipitation`, 
##     data = HumEnviroVardf)
## 
## Coefficients:
##            (Intercept)  `Annual Precipitation`  
##             -0.2506439               0.0001119

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$`Annual Precipitation` and HumEnviroVardf$HumanRevGroupModLambda
## t = 2.7153, df = 8, p-value = 0.02644
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1115429 0.9206999
## sample estimates:
##       cor 
## 0.6925324

Wettest

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ Wettest, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)      Wettest  
##  -0.1566348    0.0004093

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$Wettest and HumEnviroVardf$HumanRevGroupModLambda
## t = 2.6573, df = 8, p-value = 0.02893
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.09683174 0.91840326
## sample estimates:
##       cor 
## 0.6847126

Driest

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ Driest, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)       Driest  
##    0.080678    -0.001899

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$Driest and HumEnviroVardf$HumanRevGroupModLambda
## t = -0.77002, df = 8, p-value = 0.4634
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7656726  0.4396603
## sample estimates:
##        cor 
## -0.2626821

Seasonal

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ Seasonal, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)     Seasonal  
##   -0.377742     0.005503

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$Seasonal and HumEnviroVardf$HumanRevGroupModLambda
## t = 2.8179, df = 8, p-value = 0.02257
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1370696 0.9245562
## sample estimates:
##       cor 
## 0.7057848

NDVI Mean

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ NDVIMean, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)     NDVIMean  
##   8.830e-03    4.998e-10

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$NDVIMean and HumEnviroVardf$HumanRevGroupModLambda
## t = 0.48873, df = 8, p-value = 0.6381
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5145169  0.7224447
## sample estimates:
##       cor 
## 0.1702684

NDVI Median

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ NDVIMedian, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)   NDVIMedian  
##   3.252e-02    1.302e-10

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$NDVIMedian and HumEnviroVardf$HumanRevGroupModLambda
## t = 0.14665, df = 8, p-value = 0.887
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5973202  0.6598924
## sample estimates:
##        cor 
## 0.05177992

NDVI Max

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ NDVIMax, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)      NDVIMax  
##   1.704e-01   -1.568e-09

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$NDVIMax and HumEnviroVardf$HumanRevGroupModLambda
## t = -0.89192, df = 8, p-value = 0.3985
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7822465  0.4057060
## sample estimates:
##        cor 
## -0.3007431

NDVI Min

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ NDVIMin, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)      NDVIMin  
##   2.429e-02    7.388e-10

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$NDVIMin and HumEnviroVardf$HumanRevGroupModLambda
## t = 1.4801, df = 8, p-value = 0.1771
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2344095  0.8462362
## sample estimates:
##       cor 
## 0.4636466

LST Mean

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ LSTMean, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)      LSTMean  
##    2.048356    -0.006658

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$LSTMean and HumEnviroVardf$HumanRevGroupModLambda
## t = -1.3502, df = 8, p-value = 0.2139
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8341621  0.2728379
## sample estimates:
##        cor 
## -0.4307925

LST Median

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ LSTMedian, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)    LSTMedian  
##    2.281191    -0.007428

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$LSTMedian and HumEnviroVardf$HumanRevGroupModLambda
## t = -1.3274, df = 8, p-value = 0.221
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8319346  0.2795614
## sample estimates:
##       cor 
## -0.424846

LST Max

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ LSTMax, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)       LSTMax  
##    1.598491    -0.005134

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$LSTMax and HumEnviroVardf$HumanRevGroupModLambda
## t = -1.1069, df = 8, p-value = 0.3005
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8085333  0.3441696
## sample estimates:
##        cor 
## -0.3644275

LST Min

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ LSTMin, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)       LSTMin  
##    0.416869    -0.001257

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$LSTMin and HumEnviroVardf$HumanRevGroupModLambda
## t = -0.5994, df = 8, p-value = 0.5655
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7403092  0.4857100
## sample estimates:
##        cor 
## -0.2073172

Tmin

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ Tmin, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)         Tmin  
##   -1.355446     0.005939

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$Tmin and HumEnviroVardf$HumanRevGroupModLambda
## t = 1.9818, df = 8, p-value = 0.08282
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.08736077  0.88404989
## sample estimates:
##       cor 
## 0.5738288

Tmax

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ Tmax, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)         Tmax  
##     7.15394     -0.02281

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$Tmax and HumEnviroVardf$HumanRevGroupModLambda
## t = -2.443, df = 8, p-value = 0.04038
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.90912458 -0.04083903
## sample estimates:
##        cor 
## -0.6536576

Precip

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = HumanRevGroupModLambda ~ Precip, data = HumEnviroVardf)
## 
## Coefficients:
## (Intercept)       Precip  
##   -0.250644     0.001343

## 
##  Pearson's product-moment correlation
## 
## data:  HumEnviroVardf$Precip and HumEnviroVardf$HumanRevGroupModLambda
## t = 2.7153, df = 8, p-value = 0.02644
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1115429 0.9206999
## sample estimates:
##       cor 
## 0.6925324

Dog FOI Correlation

Annual Precipitation

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ `Annual Precipitation`, data = DogEnviroVardf)
## 
## Coefficients:
##            (Intercept)  `Annual Precipitation`  
##               9.833117               -0.002961

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$`Annual Precipitation` and DogEnviroVardf$DogRevGroupModLambda
## t = -0.5582, df = 3, p-value = 0.6157
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9357644  0.7890681
## sample estimates:
##        cor 
## -0.3067433

Wettest

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ Wettest, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)      Wettest  
##     7.20081     -0.01054

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$Wettest and DogEnviroVardf$DogRevGroupModLambda
## t = -0.53303, df = 3, p-value = 0.631
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.934018  0.794243
## sample estimates:
##       cor 
## -0.294131

Driest

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ Driest, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)       Driest  
##     0.46544      0.07856

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$Driest and DogEnviroVardf$DogRevGroupModLambda
## t = 0.34043, df = 3, p-value = 0.756
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8307644  0.9187905
## sample estimates:
##       cor 
## 0.1928594

Seasonal

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ Seasonal, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)     Seasonal  
##      9.8227      -0.1018

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$Seasonal and DogEnviroVardf$DogRevGroupModLambda
## t = -0.38484, df = 3, p-value = 0.726
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9226117  0.8228259
## sample estimates:
##        cor 
## -0.2168978

NDVI Mean

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ NDVIMean, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)     NDVIMean  
##   3.796e+00   -2.609e-08

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$NDVIMean and DogEnviroVardf$DogRevGroupModLambda
## t = -0.14312, df = 3, p-value = 0.8953
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8992794  0.8625877
## sample estimates:
##         cor 
## -0.08234898

NDVI Median

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ NDVIMedian, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)   NDVIMedian  
##   2.316e+00   -4.147e-09

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$NDVIMedian and DogEnviroVardf$DogRevGroupModLambda
## t = -0.02571, df = 3, p-value = 0.9811
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8855130  0.8789337
## sample estimates:
##         cor 
## -0.01484223

NDVI Max

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ NDVIMax, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)      NDVIMax  
##   9.406e+00   -9.031e-08

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$NDVIMax and DogEnviroVardf$DogRevGroupModLambda
## t = -0.40848, df = 3, p-value = 0.7103
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9245673  0.8184816
## sample estimates:
##      cor 
## -0.22954

NDVI Min

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ NDVIMin, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)      NDVIMin  
##   5.492e+00   -1.330e-07

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$NDVIMin and DogEnviroVardf$DogRevGroupModLambda
## t = -4.3964, df = 3, p-value = 0.02181
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9954997 -0.2686792
## sample estimates:
##        cor 
## -0.9303979

LST Mean

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ LSTMean, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)      LSTMean  
##    -323.490        1.081

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$LSTMean and DogEnviroVardf$DogRevGroupModLambda
## t = 1.0278, df = 3, p-value = 0.3797
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6765700  0.9602451
## sample estimates:
##       cor 
## 0.5103053

LST Median

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ LSTMedian, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)    LSTMedian  
##    -381.250        1.272

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$LSTMedian and DogEnviroVardf$DogRevGroupModLambda
## t = 1.4053, df = 3, p-value = 0.2546
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5678775  0.9720068
## sample estimates:
##       cor 
## 0.6300622

LST Max

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ LSTMax, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)       LSTMax  
##   -273.0922       0.9075

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$LSTMax and DogEnviroVardf$DogRevGroupModLambda
## t = 0.51959, df = 3, p-value = 0.6393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7969681  0.9330639
## sample estimates:
##       cor 
## 0.2873335

LST Min

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ LSTMin, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)       LSTMin  
##    10.15893     -0.02732

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$LSTMin and DogEnviroVardf$DogRevGroupModLambda
## t = -0.082579, df = 3, p-value = 0.9394
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8923944  0.8712501
## sample estimates:
##         cor 
## -0.04762277

Tmin

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ Tmin, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)         Tmin  
##     39.3715      -0.1586

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$Tmin and DogEnviroVardf$DogRevGroupModLambda
## t = -0.46955, df = 3, p-value = 0.6707
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9293748  0.8068811
## sample estimates:
##        cor 
## -0.2616492

Tmax

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ Tmax, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)         Tmax  
##    -293.076        0.947

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$Tmax and DogEnviroVardf$DogRevGroupModLambda
## t = 0.79845, df = 3, p-value = 0.483
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7351558  0.9500167
## sample estimates:
##       cor 
## 0.4186454

Precip

## `geom_smooth()` using formula 'y ~ x'

## 
## Call:
## lm(formula = DogRevGroupModLambda ~ Precip, data = DogEnviroVardf)
## 
## Coefficients:
## (Intercept)       Precip  
##     9.83312     -0.03553

## 
##  Pearson's product-moment correlation
## 
## data:  DogEnviroVardf$Precip and DogEnviroVardf$DogRevGroupModLambda
## t = -0.5582, df = 3, p-value = 0.6157
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9357644  0.7890681
## sample estimates:
##        cor 
## -0.3067433

Correlation matrix

##                      Annual Precipitation Wettest Driest Seasonal  Tmin  Tmax
## Annual Precipitation                 1.00    1.00  -0.93     0.97  0.98 -0.94
## Wettest                              1.00    1.00  -0.94     0.97  0.98 -0.93
## Driest                              -0.93   -0.94   1.00    -0.99 -0.98  0.76
## Seasonal                             0.97    0.97  -0.99     1.00  1.00 -0.83
## Tmin                                 0.98    0.98  -0.98     1.00  1.00 -0.87
## Tmax                                -0.94   -0.93   0.76    -0.83 -0.87  1.00
## Precip                               1.00    1.00  -0.93     0.97  0.98 -0.94
## NDVIMean                            -0.85   -0.85   0.77    -0.82 -0.81  0.74
## NDVIMedian                          -0.71   -0.70   0.54    -0.61 -0.60  0.66
## NDVIMax                             -0.69   -0.69   0.52    -0.60 -0.60  0.67
## NDVIMin                             -0.06   -0.07   0.17    -0.15 -0.11 -0.09
## LSTMean                             -0.44   -0.41   0.10    -0.21 -0.25  0.61
## LSTMedian                           -0.62   -0.61   0.39    -0.47 -0.49  0.69
## LSTMax                              -0.61   -0.60   0.44    -0.50 -0.50  0.58
## LSTMin                               0.19    0.21  -0.53     0.42  0.37  0.10
## DogRevGroupModLambda                -0.31   -0.29   0.19    -0.22 -0.26  0.42
##                      Precip NDVIMean NDVIMedian NDVIMax NDVIMin LSTMean
## Annual Precipitation   1.00    -0.85      -0.71   -0.69   -0.06   -0.44
## Wettest                1.00    -0.85      -0.70   -0.69   -0.07   -0.41
## Driest                -0.93     0.77       0.54    0.52    0.17    0.10
## Seasonal               0.97    -0.82      -0.61   -0.60   -0.15   -0.21
## Tmin                   0.98    -0.81      -0.60   -0.60   -0.11   -0.25
## Tmax                  -0.94     0.74       0.66    0.67   -0.09    0.61
## Precip                 1.00    -0.85      -0.71   -0.69   -0.06   -0.44
## NDVIMean              -0.85     1.00       0.94    0.93    0.38    0.46
## NDVIMedian            -0.71     0.94       1.00    0.94    0.23    0.68
## NDVIMax               -0.69     0.93       0.94    1.00    0.46    0.56
## NDVIMin               -0.06     0.38       0.23    0.46    1.00   -0.43
## LSTMean               -0.44     0.46       0.68    0.56   -0.43    1.00
## LSTMedian             -0.62     0.59       0.75    0.55   -0.47    0.92
## LSTMax                -0.61     0.78       0.92    0.75   -0.13    0.81
## LSTMin                 0.19    -0.03       0.23    0.31   -0.08    0.63
## DogRevGroupModLambda  -0.31    -0.08      -0.01   -0.23   -0.93    0.51
##                      LSTMedian LSTMax LSTMin DogRevGroupModLambda
## Annual Precipitation     -0.62  -0.61   0.19                -0.31
## Wettest                  -0.61  -0.60   0.21                -0.29
## Driest                    0.39   0.44  -0.53                 0.19
## Seasonal                 -0.47  -0.50   0.42                -0.22
## Tmin                     -0.49  -0.50   0.37                -0.26
## Tmax                      0.69   0.58   0.10                 0.42
## Precip                   -0.62  -0.61   0.19                -0.31
## NDVIMean                  0.59   0.78  -0.03                -0.08
## NDVIMedian                0.75   0.92   0.23                -0.01
## NDVIMax                   0.55   0.75   0.31                -0.23
## NDVIMin                  -0.47  -0.13  -0.08                -0.93
## LSTMean                   0.92   0.81   0.63                 0.51
## LSTMedian                 1.00   0.91   0.27                 0.63
## LSTMax                    0.91   1.00   0.21                 0.29
## LSTMin                    0.27   0.21   1.00                -0.05
## DogRevGroupModLambda      0.63   0.29  -0.05                 1.00
## 
## n= 5 
## 
## 
## P
##                      Annual Precipitation Wettest Driest Seasonal Tmin   Tmax  
## Annual Precipitation                      0.0000  0.0235 0.0076   0.0036 0.0175
## Wettest              0.0000                       0.0195 0.0057   0.0024 0.0208
## Driest               0.0235               0.0195         0.0008   0.0030 0.1392
## Seasonal             0.0076               0.0057  0.0008          0.0002 0.0837
## Tmin                 0.0036               0.0024  0.0030 0.0002          0.0569
## Tmax                 0.0175               0.0208  0.1392 0.0837   0.0569       
## Precip               0.0000               0.0000  0.0235 0.0076   0.0036 0.0175
## NDVIMean             0.0662               0.0665  0.1246 0.0904   0.0992 0.1499
## NDVIMedian           0.1829               0.1901  0.3438 0.2747   0.2805 0.2285
## NDVIMax              0.1938               0.1989  0.3667 0.2888   0.2886 0.2126
## NDVIMin              0.9300               0.9114  0.7864 0.8084   0.8621 0.8802
## LSTMean              0.4640               0.4902  0.8679 0.7353   0.6799 0.2705
## LSTMedian            0.2608               0.2781  0.5118 0.4265   0.3970 0.1987
## LSTMax               0.2726               0.2852  0.4608 0.3910   0.3924 0.3020
## LSTMin               0.7615               0.7353  0.3559 0.4765   0.5350 0.8730
## DogRevGroupModLambda 0.6157               0.6310  0.7560 0.7260   0.6707 0.4830
##                      Precip NDVIMean NDVIMedian NDVIMax NDVIMin LSTMean
## Annual Precipitation 0.0000 0.0662   0.1829     0.1938  0.9300  0.4640 
## Wettest              0.0000 0.0665   0.1901     0.1989  0.9114  0.4902 
## Driest               0.0235 0.1246   0.3438     0.3667  0.7864  0.8679 
## Seasonal             0.0076 0.0904   0.2747     0.2888  0.8084  0.7353 
## Tmin                 0.0036 0.0992   0.2805     0.2886  0.8621  0.6799 
## Tmax                 0.0175 0.1499   0.2285     0.2126  0.8802  0.2705 
## Precip                      0.0662   0.1829     0.1938  0.9300  0.4640 
## NDVIMean             0.0662          0.0195     0.0212  0.5286  0.4375 
## NDVIMedian           0.1829 0.0195              0.0168  0.7108  0.2061 
## NDVIMax              0.1938 0.0212   0.0168             0.4392  0.3268 
## NDVIMin              0.9300 0.5286   0.7108     0.4392          0.4663 
## LSTMean              0.4640 0.4375   0.2061     0.3268  0.4663         
## LSTMedian            0.2608 0.2904   0.1466     0.3386  0.4197  0.0290 
## LSTMax               0.2726 0.1203   0.0259     0.1484  0.8375  0.0992 
## LSTMin               0.7615 0.9669   0.7140     0.6098  0.8977  0.2557 
## DogRevGroupModLambda 0.6157 0.8953   0.9811     0.7103  0.0218  0.3797 
##                      LSTMedian LSTMax LSTMin DogRevGroupModLambda
## Annual Precipitation 0.2608    0.2726 0.7615 0.6157              
## Wettest              0.2781    0.2852 0.7353 0.6310              
## Driest               0.5118    0.4608 0.3559 0.7560              
## Seasonal             0.4265    0.3910 0.4765 0.7260              
## Tmin                 0.3970    0.3924 0.5350 0.6707              
## Tmax                 0.1987    0.3020 0.8730 0.4830              
## Precip               0.2608    0.2726 0.7615 0.6157              
## NDVIMean             0.2904    0.1203 0.9669 0.8953              
## NDVIMedian           0.1466    0.0259 0.7140 0.9811              
## NDVIMax              0.3386    0.1484 0.6098 0.7103              
## NDVIMin              0.4197    0.8375 0.8977 0.0218              
## LSTMean              0.0290    0.0992 0.2557 0.3797              
## LSTMedian                      0.0307 0.6654 0.2546              
## LSTMax               0.0307           0.7369 0.6393              
## LSTMin               0.6654    0.7369        0.9394              
## DogRevGroupModLambda 0.2546    0.6393 0.9394