require(ggplot2)
## Loading required package: ggplot2
require(plyr)
## Loading required package: plyr
#Loads required plyr package and a ggplot2 package for plotting
reproaov<-aov(repro4$Brooders~repro4$Site+repro4$Pop+repro4$Site:repro4$Pop,repro4)
summary(reproaov)
## Df Sum Sq Mean Sq F value Pr(>F)
## repro4$Site 2 138 69.2 12.60 1.1e-05 ***
## repro4$Pop 2 99 49.6 9.03 0.00023 ***
## repro4$Site:repro4$Pop 4 31 7.7 1.41 0.23609
## Residuals 117 642 5.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SitevPopTukey<-TukeyHSD(reproaov)
print(SitevPopTukey)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = repro4$Brooders ~ repro4$Site + repro4$Pop + repro4$Site:repro4$Pop, data = repro4)
##
## $`repro4$Site`
## diff lwr upr p adj
## Manchester-Fidalgo -0.8571 -2.0710 0.3567 0.2186
## Oyster Bay-Fidalgo 1.6667 0.4529 2.8805 0.0041
## Oyster Bay-Manchester 2.5238 1.3100 3.7376 0.0000
##
## $`repro4$Pop`
## diff lwr upr p adj
## N-H -0.04762 -1.2614 1.166 0.9952
## S-H 1.85714 0.6433 3.071 0.0012
## S-N 1.90476 0.6910 3.119 0.0009
##
## $`repro4$Site:repro4$Pop`
## diff lwr upr p adj
## Manchester:H-Fidalgo:H -7.143e-02 -2.87058 2.7277 1.0000
## Oyster Bay:H-Fidalgo:H 1.286e+00 -1.51344 4.0849 0.8749
## Fidalgo:N-Fidalgo:H -4.330e-15 -2.79915 2.7992 1.0000
## Manchester:N-Fidalgo:H -5.000e-01 -3.29915 2.2992 0.9997
## Oyster Bay:N-Fidalgo:H 1.571e+00 -1.22773 4.3706 0.6987
## Fidalgo:S-Fidalgo:H 2.214e+00 -0.58487 5.0134 0.2427
## Manchester:S-Fidalgo:H 2.143e-01 -2.58487 3.0134 1.0000
## Oyster Bay:S-Fidalgo:H 4.357e+00 1.55799 7.1563 0.0001
## Oyster Bay:H-Manchester:H 1.357e+00 -1.44201 4.1563 0.8379
## Fidalgo:N-Manchester:H 7.143e-02 -2.72773 2.8706 1.0000
## Manchester:N-Manchester:H -4.286e-01 -3.22773 2.3706 0.9999
## Oyster Bay:N-Manchester:H 1.643e+00 -1.15630 4.4420 0.6456
## Fidalgo:S-Manchester:H 2.286e+00 -0.51344 5.0849 0.2061
## Manchester:S-Manchester:H 2.857e-01 -2.51344 3.0849 1.0000
## Oyster Bay:S-Manchester:H 4.429e+00 1.62942 7.2277 0.0001
## Fidalgo:N-Oyster Bay:H -1.286e+00 -4.08487 1.5134 0.8749
## Manchester:N-Oyster Bay:H -1.786e+00 -4.58487 1.0134 0.5353
## Oyster Bay:N-Oyster Bay:H 2.857e-01 -2.51344 3.0849 1.0000
## Fidalgo:S-Oyster Bay:H 9.286e-01 -1.87058 3.7277 0.9801
## Manchester:S-Oyster Bay:H -1.071e+00 -3.87058 1.7277 0.9529
## Oyster Bay:S-Oyster Bay:H 3.071e+00 0.27227 5.8706 0.0203
## Manchester:N-Fidalgo:N -5.000e-01 -3.29915 2.2992 0.9997
## Oyster Bay:N-Fidalgo:N 1.571e+00 -1.22773 4.3706 0.6987
## Fidalgo:S-Fidalgo:N 2.214e+00 -0.58487 5.0134 0.2427
## Manchester:S-Fidalgo:N 2.143e-01 -2.58487 3.0134 1.0000
## Oyster Bay:S-Fidalgo:N 4.357e+00 1.55799 7.1563 0.0001
## Oyster Bay:N-Manchester:N 2.071e+00 -0.72773 4.8706 0.3279
## Fidalgo:S-Manchester:N 2.714e+00 -0.08487 5.5134 0.0649
## Manchester:S-Manchester:N 7.143e-01 -2.08487 3.5134 0.9965
## Oyster Bay:S-Manchester:N 4.857e+00 2.05799 7.6563 0.0000
## Fidalgo:S-Oyster Bay:N 6.429e-01 -2.15630 3.4420 0.9983
## Manchester:S-Oyster Bay:N -1.357e+00 -4.15630 1.4420 0.8379
## Oyster Bay:S-Oyster Bay:N 2.786e+00 -0.01344 5.5849 0.0521
## Manchester:S-Fidalgo:S -2.000e+00 -4.79915 0.7992 0.3760
## Oyster Bay:S-Fidalgo:S 2.143e+00 -0.65630 4.9420 0.2834
## Oyster Bay:S-Manchester:S 4.143e+00 1.34370 6.9420 0.0003
#Standard ANOVA and TukeyHSD on non transformed data. Finds huge difference between pops and sites but heavily skewed and non normaly distributed.
repro4$arcsinbrooders<-asin(sign(repro4$Brooders)*sqrt(abs(repro4$Brooders)))
## Warning: NaNs produced
repro4$arcsinbrooders<-replace(repro4$arcsinbrooders,is.na(repro4$arcsinbrooders),0)
#For fun, arc sine transformed brooder data. This is essentially meaningless in the long run.
reproaov2<-aov(repro4$arcsinbrooders~repro4$Site+repro4$Pop+repro4$Site:repro4$Pop,repro4)
summary(reproaov2)
## Df Sum Sq Mean Sq F value Pr(>F)
## repro4$Site 2 0.3 0.137 0.44 0.64
## repro4$Pop 2 1.4 0.725 2.35 0.10
## repro4$Site:repro4$Pop 4 2.0 0.490 1.59 0.18
## Residuals 117 36.1 0.309
arcsinbroodertukey<-TukeyHSD(reproaov2)
print(arcsinbroodertukey)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = repro4$arcsinbrooders ~ repro4$Site + repro4$Pop + repro4$Site:repro4$Pop, data = repro4)
##
## $`repro4$Site`
## diff lwr upr p adj
## Manchester-Fidalgo 0.1122 -0.1757 0.4001 0.6255
## Oyster Bay-Fidalgo 0.0374 -0.2505 0.3253 0.9489
## Oyster Bay-Manchester -0.0748 -0.3627 0.2131 0.8112
##
## $`repro4$Pop`
## diff lwr upr p adj
## N-H -0.1496 -0.4375 0.13827 0.4358
## S-H -0.2618 -0.5497 0.02607 0.0827
## S-N -0.1122 -0.4001 0.17567 0.6255
##
## $`repro4$Site:repro4$Pop`
## diff lwr upr p adj
## Manchester:H-Fidalgo:H 4.488e-01 -0.2151 1.1127 0.4539
## Oyster Bay:H-Fidalgo:H 3.366e-01 -0.3273 1.0005 0.8016
## Fidalgo:N-Fidalgo:H 2.244e-01 -0.4395 0.8883 0.9776
## Manchester:N-Fidalgo:H 1.388e-15 -0.6639 0.6639 1.0000
## Oyster Bay:N-Fidalgo:H 1.122e-01 -0.5517 0.7761 0.9998
## Fidalgo:S-Fidalgo:H 2.345e-15 -0.6639 0.6639 1.0000
## Manchester:S-Fidalgo:H 1.122e-01 -0.5517 0.7761 0.9998
## Oyster Bay:S-Fidalgo:H -1.122e-01 -0.7761 0.5517 0.9998
## Oyster Bay:H-Manchester:H -1.122e-01 -0.7761 0.5517 0.9998
## Fidalgo:N-Manchester:H -2.244e-01 -0.8883 0.4395 0.9776
## Manchester:N-Manchester:H -4.488e-01 -1.1127 0.2151 0.4539
## Oyster Bay:N-Manchester:H -3.366e-01 -1.0005 0.3273 0.8016
## Fidalgo:S-Manchester:H -4.488e-01 -1.1127 0.2151 0.4539
## Manchester:S-Manchester:H -3.366e-01 -1.0005 0.3273 0.8016
## Oyster Bay:S-Manchester:H -5.610e-01 -1.2249 0.1029 0.1699
## Fidalgo:N-Oyster Bay:H -1.122e-01 -0.7761 0.5517 0.9998
## Manchester:N-Oyster Bay:H -3.366e-01 -1.0005 0.3273 0.8016
## Oyster Bay:N-Oyster Bay:H -2.244e-01 -0.8883 0.4395 0.9776
## Fidalgo:S-Oyster Bay:H -3.366e-01 -1.0005 0.3273 0.8016
## Manchester:S-Oyster Bay:H -2.244e-01 -0.8883 0.4395 0.9776
## Oyster Bay:S-Oyster Bay:H -4.488e-01 -1.1127 0.2151 0.4539
## Manchester:N-Fidalgo:N -2.244e-01 -0.8883 0.4395 0.9776
## Oyster Bay:N-Fidalgo:N -1.122e-01 -0.7761 0.5517 0.9998
## Fidalgo:S-Fidalgo:N -2.244e-01 -0.8883 0.4395 0.9776
## Manchester:S-Fidalgo:N -1.122e-01 -0.7761 0.5517 0.9998
## Oyster Bay:S-Fidalgo:N -3.366e-01 -1.0005 0.3273 0.8016
## Oyster Bay:N-Manchester:N 1.122e-01 -0.5517 0.7761 0.9998
## Fidalgo:S-Manchester:N 9.576e-16 -0.6639 0.6639 1.0000
## Manchester:S-Manchester:N 1.122e-01 -0.5517 0.7761 0.9998
## Oyster Bay:S-Manchester:N -1.122e-01 -0.7761 0.5517 0.9998
## Fidalgo:S-Oyster Bay:N -1.122e-01 -0.7761 0.5517 0.9998
## Manchester:S-Oyster Bay:N 6.384e-16 -0.6639 0.6639 1.0000
## Oyster Bay:S-Oyster Bay:N -2.244e-01 -0.8883 0.4395 0.9776
## Manchester:S-Fidalgo:S 1.122e-01 -0.5517 0.7761 0.9998
## Oyster Bay:S-Fidalgo:S -1.122e-01 -0.7761 0.5517 0.9998
## Oyster Bay:S-Manchester:S -2.244e-01 -0.8883 0.4395 0.9776
#Makes ANOVA based on arcsine transform of brooders. Finds no differences which is also skewed.
repro4$prop<-repro4$Brooders/repro4$Gaping
#creates individual proportion for each sample date
repro4$arcsinprop<-asin(sign(repro4$prop)*sqrt(abs(repro4$prop)))
repro4$arcsinprop<-replace(repro4$arcsinprop,is.na(repro4$arcsinprop),0)
#arcsine transforms individual proportion data to normalize for use in ANOVA and TukeyHSD.
reproaov3<-aov(repro4$arcsinprop~repro4$Site+repro4$Pop+repro4$Site:repro4$Pop,repro4)
summary(reproaov3)
## Df Sum Sq Mean Sq F value Pr(>F)
## repro4$Site 2 0.258 0.1290 11.33 3.2e-05 ***
## repro4$Pop 2 0.127 0.0636 5.59 0.0048 **
## repro4$Site:repro4$Pop 4 0.029 0.0073 0.64 0.6335
## Residuals 117 1.332 0.0114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary.aov(reproaov3)
## Df Sum Sq Mean Sq F value Pr(>F)
## repro4$Site 2 0.258 0.1290 11.33 3.2e-05 ***
## repro4$Pop 2 0.127 0.0636 5.59 0.0048 **
## repro4$Site:repro4$Pop 4 0.029 0.0073 0.64 0.6335
## Residuals 117 1.332 0.0114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
arcsinproptukey<-TukeyHSD(reproaov3)
print(arcsinproptukey)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = repro4$arcsinprop ~ repro4$Site + repro4$Pop + repro4$Site:repro4$Pop, data = repro4)
##
## $`repro4$Site`
## diff lwr upr p adj
## Manchester-Fidalgo -0.03381 -0.08907 0.02146 0.3178
## Oyster Bay-Fidalgo 0.07450 0.01923 0.12976 0.0050
## Oyster Bay-Manchester 0.10830 0.05304 0.16357 0.0000
##
## $`repro4$Pop`
## diff lwr upr p adj
## N-H -0.01821 -0.073473 0.03705 0.7147
## S-H 0.05645 0.001185 0.11171 0.0441
## S-N 0.07466 0.019394 0.12992 0.0049
##
## $`repro4$Site:repro4$Pop`
## diff lwr upr p adj
## Manchester:H-Fidalgo:H 0.004467 -0.122977 0.13191 1.0000
## Oyster Bay:H-Fidalgo:H 0.071372 -0.056071 0.19882 0.7015
## Fidalgo:N-Fidalgo:H -0.013294 -0.140738 0.11415 1.0000
## Manchester:N-Fidalgo:H -0.045447 -0.172890 0.08200 0.9689
## Oyster Bay:N-Fidalgo:H 0.079952 -0.047491 0.20740 0.5583
## Fidalgo:S-Fidalgo:H 0.086684 -0.040759 0.21413 0.4452
## Manchester:S-Fidalgo:H 0.012947 -0.114497 0.14039 1.0000
## Oyster Bay:S-Fidalgo:H 0.145553 0.018110 0.27300 0.0130
## Oyster Bay:H-Manchester:H 0.066905 -0.060538 0.19435 0.7696
## Fidalgo:N-Manchester:H -0.017761 -0.145204 0.10968 1.0000
## Manchester:N-Manchester:H -0.049914 -0.177357 0.07753 0.9463
## Oyster Bay:N-Manchester:H 0.075485 -0.051958 0.20293 0.6341
## Fidalgo:S-Manchester:H 0.082217 -0.045226 0.20966 0.5198
## Manchester:S-Manchester:H 0.008480 -0.118963 0.13592 1.0000
## Oyster Bay:S-Manchester:H 0.141086 0.013643 0.26853 0.0184
## Fidalgo:N-Oyster Bay:H -0.084667 -0.212110 0.04278 0.4786
## Manchester:N-Oyster Bay:H -0.116819 -0.244263 0.01062 0.1000
## Oyster Bay:N-Oyster Bay:H 0.008580 -0.118864 0.13602 1.0000
## Fidalgo:S-Oyster Bay:H 0.015312 -0.112131 0.14276 1.0000
## Manchester:S-Oyster Bay:H -0.058426 -0.185869 0.06902 0.8761
## Oyster Bay:S-Oyster Bay:H 0.074181 -0.053262 0.20162 0.6558
## Manchester:N-Fidalgo:N -0.032153 -0.159596 0.09529 0.9968
## Oyster Bay:N-Fidalgo:N 0.093246 -0.034197 0.22069 0.3433
## Fidalgo:S-Fidalgo:N 0.099979 -0.027465 0.22742 0.2528
## Manchester:S-Fidalgo:N 0.026241 -0.101202 0.15368 0.9992
## Oyster Bay:S-Fidalgo:N 0.158848 0.031404 0.28629 0.0043
## Oyster Bay:N-Manchester:N 0.125399 -0.002044 0.25284 0.0575
## Fidalgo:S-Manchester:N 0.132131 0.004688 0.25957 0.0359
## Manchester:S-Manchester:N 0.058394 -0.069050 0.18584 0.8764
## Oyster Bay:S-Manchester:N 0.191000 0.063557 0.31844 0.0002
## Fidalgo:S-Oyster Bay:N 0.006732 -0.120711 0.13418 1.0000
## Manchester:S-Oyster Bay:N -0.067005 -0.194449 0.06044 0.7681
## Oyster Bay:S-Oyster Bay:N 0.065601 -0.061842 0.19304 0.7881
## Manchester:S-Fidalgo:S -0.073738 -0.201181 0.05371 0.6631
## Oyster Bay:S-Fidalgo:S 0.058869 -0.068574 0.18631 0.8714
## Oyster Bay:S-Manchester:S 0.132607 0.005163 0.26005 0.0347
#ANOVA and TukeyHSD on Arcsine transformed proportions conservatively finds significant differences between pops and sites. More trust worthy due to normality.
Gapingsum<-ddply(repro4,.(Site,Pop),summarise,sum=sum(Gaping,na.rm=T))
#Summarises Gaping data for each pop at each site
Broodsum<-ddply(repro4,.(Site,Pop),summarise,sum=sum(Brooders,na.rm=T))
#Summarises Brooder data for each pop at each site
BroodGapingSum<-merge(Gapingsum,Broodsum,by=c("Site","Pop"))
BroodGapingSum<-rename(BroodGapingSum, c("Site"="Site","Pop"="Pop","sum.x"="Gaping","sum.y"="Brooder"))
BroodGapingSum$prop<-BroodGapingSum$Brooder/BroodGapingSum$Gaping
print(BroodGapingSum)
## Site Pop Gaping Brooder prop
## 1 Fidalgo H 814 8 0.009828
## 2 Fidalgo N 813 8 0.009840
## 3 Fidalgo S 921 39 0.042345
## 4 Manchester H 658 7 0.010638
## 5 Manchester N 631 1 0.001585
## 6 Manchester S 699 11 0.015737
## 7 Oyster Bay H 931 26 0.027927
## 8 Oyster Bay N 769 30 0.039012
## 9 Oyster Bay S 883 69 0.078143
#Creates clean Data frame for Summarized data as well as creating raw proportion data from summaries
BroodGapingSum$ArcSine<-asin(sign(BroodGapingSum$prop)*sqrt(abs(BroodGapingSum$prop)))
#Arcsine transforms raw proportions but is not useful.
arcaov<-aov(BroodGapingSum$ArcSine~BroodGapingSum$Site+BroodGapingSum$Pop+BroodGapingSum$Site:BroodGapingSum$Pop,BroodGapingSum)
summary(arcaov)
## Df Sum Sq Mean Sq
## BroodGapingSum$Site 2 0.02485 0.01242
## BroodGapingSum$Pop 2 0.01544 0.00772
## BroodGapingSum$Site:BroodGapingSum$Pop 4 0.00344 0.00086
arcTukey<-TukeyHSD(arcaov)
## Warning: NaNs produced
## Warning: NaNs produced
## Warning: NaNs produced
print(arcTukey)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = BroodGapingSum$ArcSine ~ BroodGapingSum$Site + BroodGapingSum$Pop + BroodGapingSum$Site:BroodGapingSum$Pop, data = BroodGapingSum)
##
## $`BroodGapingSum$Site`
## diff lwr upr p adj
## Manchester-Fidalgo -0.04567 NaN NaN NaN
## Oyster Bay-Fidalgo 0.08137 NaN NaN NaN
## Oyster Bay-Manchester 0.12704 NaN NaN NaN
##
## $`BroodGapingSum$Pop`
## diff lwr upr p adj
## N-H -0.01084 NaN NaN NaN
## S-H 0.08194 NaN NaN NaN
## S-N 0.09278 NaN NaN NaN
##
## $`BroodGapingSum$Site:BroodGapingSum$Pop`
## diff lwr upr p adj
## Manchester:H-Fidalgo:H 4.026e-03 NaN NaN NaN
## Oyster Bay:H-Fidalgo:H 6.860e-02 NaN NaN NaN
## Fidalgo:N-Fidalgo:H 6.125e-05 NaN NaN NaN
## Manchester:N-Fidalgo:H -5.948e-02 NaN NaN NaN
## Oyster Bay:N-Fidalgo:H 9.952e-02 NaN NaN NaN
## Fidalgo:S-Fidalgo:H 1.080e-01 NaN NaN NaN
## Manchester:S-Fidalgo:H 2.648e-02 NaN NaN NaN
## Oyster Bay:S-Fidalgo:H 1.840e-01 NaN NaN NaN
## Oyster Bay:H-Manchester:H 6.458e-02 NaN NaN NaN
## Fidalgo:N-Manchester:H -3.965e-03 NaN NaN NaN
## Manchester:N-Manchester:H -6.351e-02 NaN NaN NaN
## Oyster Bay:N-Manchester:H 9.550e-02 NaN NaN NaN
## Fidalgo:S-Manchester:H 1.039e-01 NaN NaN NaN
## Manchester:S-Manchester:H 2.245e-02 NaN NaN NaN
## Oyster Bay:S-Manchester:H 1.800e-01 NaN NaN NaN
## Fidalgo:N-Oyster Bay:H -6.854e-02 NaN NaN NaN
## Manchester:N-Oyster Bay:H -1.281e-01 NaN NaN NaN
## Oyster Bay:N-Oyster Bay:H 3.092e-02 NaN NaN NaN
## Fidalgo:S-Oyster Bay:H 3.936e-02 NaN NaN NaN
## Manchester:S-Oyster Bay:H -4.212e-02 NaN NaN NaN
## Oyster Bay:S-Oyster Bay:H 1.154e-01 NaN NaN NaN
## Manchester:N-Fidalgo:N -5.954e-02 NaN NaN NaN
## Oyster Bay:N-Fidalgo:N 9.946e-02 NaN NaN NaN
## Fidalgo:S-Fidalgo:N 1.079e-01 NaN NaN NaN
## Manchester:S-Fidalgo:N 2.642e-02 NaN NaN NaN
## Oyster Bay:S-Fidalgo:N 1.840e-01 NaN NaN NaN
## Oyster Bay:N-Manchester:N 1.590e-01 NaN NaN NaN
## Fidalgo:S-Manchester:N 1.674e-01 NaN NaN NaN
## Manchester:S-Manchester:N 8.596e-02 NaN NaN NaN
## Oyster Bay:S-Manchester:N 2.435e-01 NaN NaN NaN
## Fidalgo:S-Oyster Bay:N 8.439e-03 NaN NaN NaN
## Manchester:S-Oyster Bay:N -7.304e-02 NaN NaN NaN
## Oyster Bay:S-Oyster Bay:N 8.449e-02 NaN NaN NaN
## Manchester:S-Fidalgo:S -8.148e-02 NaN NaN NaN
## Oyster Bay:S-Fidalgo:S 7.605e-02 NaN NaN NaN
## Oyster Bay:S-Manchester:S 1.575e-01 NaN NaN NaN
#Arcsine transform on raw proportions fails due to lack of replicates
Proptest<-prop.test(BroodGapingSum$Brooder,BroodGapingSum$Gaping, conf.level=0.95)
print(Proptest)
##
## 9-sample test for equality of proportions without continuity
## correction
##
## data: BroodGapingSum$Brooder out of BroodGapingSum$Gaping
## X-squared = 139.2, df = 8, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## prop 1 prop 2 prop 3 prop 4 prop 5 prop 6 prop 7 prop 8
## 0.009828 0.009840 0.042345 0.010638 0.001585 0.015737 0.027927 0.039012
## prop 9
## 0.078143
#Proportion test with confidence levels. Doesn't have meaning.
ggplot(data=BroodGapingSum,aes(x=Site, y=prop, group=Pop, col=Pop))+
geom_line(size=2)+geom_point(size=6)+
scale_color_manual(name="Population",
labels=c("Dabob","Fidalgo","Oyster Bay"),
values=c("blue","purple","orange"))+
labs(title="Raw Proportion of Brooders\nSite by Population",
x="Site",y="Raw Proportion of Brooders")+
theme_bw()+
theme(axis.text.x=element_text(color=c("purple","red","orange"), size=20),
axis.text.y=element_text(color="black",size=15),
axis.title.x=element_text(color="black",size=25),
axis.title.y=element_text(color="black",size=25),
plot.title=element_text(color="forestgreen",size=35),
legend.justification=c(0,1),
legend.position=c(0,1))
#Graph uses raw proportions of brooders to gaping animals produced at each site.
#ANOVA and Tukey test on proportions uses arcsin transformed data for normality
#ANOVA/Tukey Find that the Oyster Bay site is significantly Different from the other two sites
#ANOVA/Tukey Find that the Oyster Bay Pop is significantly Different from the other two pops.
#Individual interactions find that the Oyster Bay pop at Oyster Bay site is significantly
#different than all almost all other populations at all other sites including itself at Manchester
#but not significantly different than the other two populations at Oyster Bay site