4/12/2022

Sampling in the Adirondacks!

Goal: Compare Mercury (Hg) trophic transfer among varying pH Adirondack lakes

  • I am using collected data on lake and fish characteristics including:
  • watershed:surface area
  • pH
  • chlorophyll a
  • dissolved organic carbon(DOC)
  • dissolved selenium (Se)
  • Hg and selenium data from 56 fish that are divided into prey and predator groups or low and high trophic level (TL) groups.

Loaded dataset from csv

  • Some of my variables in my dataset
setwd("~/Desktop/R data")
Mastersheet_ADK_Se_Hg_Honors <- read.csv("Mastersheet-ADK Se-Hg Honors.csv")
colnames(Mastersheet_ADK_Se_Hg_Honors)
##  [1] "my_ID"               "pH"                  "hypo_DO"            
##  [4] "chl_a"               "phyto_se_1"          "phyto_se_2"         
##  [7] "EF"                  "water_se_avg"        "WS_SA"              
## [10] "Waterbody"           "year"                "species"            
## [13] "TL"                  "origin"              "length"             
## [16] "Fish_Wt"             "percent_moisture"    "THg_DW"             
## [19] "THg_WW"              "THg_WW_Avg_Moisture" "log10THg"           
## [22] "THg_ww_plug"         "Se_ICP_ppb"          "Se_ICP_ppm"         
## [25] "Sample_Se_DW"        "Se_divided_by_mm"    "Hg_divided_by_mm"   
## [28] "Se_Hg_ratio"         "delta_N"             "delta_C"            
## [31] "lake_zoop_deltaN"    "lake_zoop_deltaC"    "TL_calc"            
## [34] "adj_THg_ww_plug"     "Hg_TMS"              "Hg_TMF"             
## [37] "Prey_avg_SeHg"       "log10Se"             "DOC_s"

Packages

library(ggplot2)
library(ggpubr)
library(devtools)
## Loading required package: usethis
library(data.table)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:data.table':
## 
##     between, first, last
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Sites

  • 6 study lakes ranging from pH 5.1-7.45
pHbarplot

Background

A subset from my honors thesis work looking at Hg trophic magnifiation at different pH lakes

  • Hg is freed from DOC and methylated more at low pH
  • More bioavailable
  • Can compare biomagnification/ trophic transfer via plotting log10THg vs. delta 15 N isotope

Objectives:

  1. Look for differences in low trophic level (TL) fish sizes to see if they are comparable among water bodies Note: fish can be compared using size as a proxy for exposure time to a toxin i.e. Mercury(Hg)

  2. Create regression and get equation to size adjust Hg values based on the mean length

  3. Check if size adjusted prey Hg is lognormal

  4. Find Hg TMS by plotting Hg vs delta N

  5. See if Hg TMS are statistically different among sites that vary in pH

Objective 1:Look for differences in low TL fish sizes to see if they are comparable among water bodies

Objective 1

  • ANOVA to test for sig differences in length
  • Tukey connecting letters report
  • First, I needed to filter the fish trophic levels to only use low TL fish from my data set

Filtering trophic level

lowTL<-Mastersheet_ADK_Se_Hg_Honors %>% filter(TL == "low")
head(lowTL)
##       my_ID   pH hypo_DO  chl_a phyto_se_1 phyto_se_2        EF water_se_avg
## 1: BL-YP-01 7.41      NA 13.300  0.2778867  0.2404661  6.038082  4.29236e-05
## 2: BL-YP-02 7.41      NA 13.300  0.2778867  0.2404661  6.038082  4.29236e-05
## 3: BL-YP-03 7.41      NA 13.300  0.2778867  0.2404661  6.038082  4.29236e-05
## 4: BL-YP-04 7.41      NA 13.300  0.2778867  0.2404661  6.038082  4.29236e-05
## 5: BL-YP-05 7.41      NA 13.300  0.2778867  0.2404661  6.038082  4.29236e-05
## 6: FL-YP-01 6.51    7.68  1.946  0.9617706         NA 18.368422  5.23600e-05
##    WS_SA        Waterbody year species  TL                origin length Fish_Wt
## 1:   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet    224     148
## 2:   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet    228     166
## 3:   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet    187      82
## 4:   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet    194     102
## 5:   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet    193      84
## 6:   8.2     Francis Lake 2014      YP low       Charley- Fillet    218     104
##    percent_moisture    THg_DW THg_WW THg_WW_Avg_Moisture   log10THg THg_ww_plug
## 1:      82.24877487 0.6647429  0.118                 n/a -1.9876691  0.13701442
## 2:      82.88155569 0.7652565  0.131                 n/a -1.8833863  0.15207426
## 3:      85.83355246 0.8117773  0.115                 n/a -2.0133649  0.13353857
## 4:      79.06571132 0.5206769  0.109                 n/a -2.0668313  0.12658626
## 5:      86.60999426 0.4092605 0.0548                 n/a -2.7529761  0.06373789
## 6:             80.7 2.3904800 0.4536            0.577139 -0.6441003  0.52513477
##    Se_ICP_ppb  Se_ICP_ppm Sample_Se_DW Se_divided_by_mm Hg_divided_by_mm
## 1:   2.373204 0.002373204    1.2220743      0.015477132      0.000683057
## 2:   1.413760 0.001413760    0.7316897      0.009266587      0.000758135
## 3:   1.761470 0.001761470    0.9140867      0.011576580      0.000665729
## 4:   2.231434 0.002231434    1.0749525      0.013613886      0.000631070
## 5:   2.455623 0.002455623    1.2242224      0.015504336      0.000317752
## 6:   3.730000 0.003730000    1.9631862      0.024863047      0.002617951
##    Se_Hg_ratio   delta_N   delta_C lake_zoop_deltaN lake_zoop_deltaC  TL_calc
## 1:        4.02 11.516846 -28.31252             1.68        -28.23704 4.893646
## 2:        2.09  9.951286 -27.75586             1.68        -28.23704 4.433187
## 3:        2.46 10.229079 -27.64202             1.68        -28.23704 4.514891
## 4:        4.52 10.192370 -27.37382             1.68        -28.23704 4.504094
## 5:        6.53 10.939434 -26.83743             1.68        -28.23704 4.723819
## 6:        1.83  9.096081 -29.21649             3.86        -33.41000 3.540024
##    adj_THg_ww_plug  Hg_TMS   Hg_TMF Prey_avg_SeHg     log10Se DOC_s
## 1:        2.860124 0.50820 3.222552          3.93  0.20054970   5.1
## 2:        2.399665 0.50820 3.222552          3.93 -0.31239873   5.1
## 3:        2.481369 0.50820 3.222552          3.93 -0.08982983   5.1
## 4:        2.470572 0.50820 3.222552          3.93  0.07227644   5.1
## 5:        2.690297 0.50820 3.222552          3.93  0.20230585   5.1
## 6:        1.530802 0.61711 4.141045          3.14  0.67456877   7.1

Anova test to compare lengths among water body for only low TL fish

aovpreylength<-aov(length~Waterbody, data = lowTL)
summary(aov(length~Waterbody, data = lowTL))
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## Waterbody    5  22415    4483   15.64 1.32e-06 ***
## Residuals   22   6306     287                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Visualize differences in prey length

Visualzing Tukey test connecting letters report

  • Using the package multicompview, I was able to visualize the lakes that had prey fish with significantly different lengths via a Tukey test
library(multcompView)
lowTLlengthtukey<-TukeyHSD(aov(length~Waterbody, data = lowTL))
tukey.cld.preylength <- multcompLetters4(aovpreylength, lowTLlengthtukey)
print(tukey.cld.preylength)
## $Waterbody
##   Horseshoe Lake     Francis Lake        Rock Pond    Halfmoon Lake 
##              "a"             "ab"             "ab"              "b" 
## Butterfield Lake        Moss Lake 
##              "b"              "c"

Are there differences in low TL fish legnths?

  • p-value=1.32e-06
  • There are differences in low TL fish lengths
  • I needed to size adjust Hg concentrations to convey similar fish exposure times, despite having different ages (as shown through length proxy)

Conclusion

Now, we can see that prey fish from Horseshoe lake and Moss lake have significant diffferences in length and thus will need to be size adjusted using a regression model.

Objective 2. Create regression and get equation to size adjust Hg values based on the mean length

Objective 2

  • New data set without Horseshoe or Moss Lakes to create the regression
noHLorML_lowTL<-filter(lowTL, Waterbody != "Moss Lake" & Waterbody!= "Horseshoe Lake")
droplevels
## function (x, ...) 
## UseMethod("droplevels")
## <bytecode: 0x7fb4a2061e78>
## <environment: namespace:base>

Here’s the data now

print(noHLorML_lowTL)
##         my_ID   pH hypo_DO     chl_a phyto_se_1 phyto_se_2        EF
##  1:  BL-YP-01 7.41      NA 13.300000  0.2778867  0.2404661  6.038082
##  2:  BL-YP-02 7.41      NA 13.300000  0.2778867  0.2404661  6.038082
##  3:  BL-YP-03 7.41      NA 13.300000  0.2778867  0.2404661  6.038082
##  4:  BL-YP-04 7.41      NA 13.300000  0.2778867  0.2404661  6.038082
##  5:  BL-YP-05 7.41      NA 13.300000  0.2778867  0.2404661  6.038082
##  6:  FL-YP-01 6.51    7.68  1.946000  0.9617706         NA 18.368422
##  7:  FL-YP-02 6.51    7.68  1.946000  0.9617706         NA 18.368422
##  8:  FL-YP-03 6.51    7.68  1.946000  0.9617706         NA 18.368422
##  9:  FL-YP-04 6.51    7.68  1.946000  0.9617706         NA 18.368422
## 10:  FL-YP-05 6.51    7.68  1.946000  0.9617706         NA 18.368422
## 11: HML-YP-01 5.51    0.23  5.285714  2.0726858         NA 38.287352
## 12: HML-YP-02 5.51    0.23  5.285714  2.0726858         NA 38.287352
## 13: HML-YP-03 5.51    0.23  5.285714  2.0726858         NA 38.287352
## 14: HML-YP-04 5.51    0.23  5.285714  2.0726858         NA 38.287352
## 15: HML-YP-05 5.51    0.23  5.285714  2.0726858         NA 38.287352
## 16:  RP-YP-01 5.21    5.33  2.765432  0.8677076         NA  9.568370
## 17:  RP-YP-02 5.21    5.33  2.765432  0.8677076         NA  9.568370
## 18:  RP-YP-03 5.21    5.33  2.765432  0.8677076         NA  9.568370
## 19:  RP-YP-04 5.21    5.33  2.765432  0.8677076         NA  9.568370
##     water_se_avg WS_SA        Waterbody year species  TL                origin
##  1:  4.29236e-05   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet
##  2:  4.29236e-05   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet
##  3:  4.29236e-05   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet
##  4:  4.29236e-05   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet
##  5:  4.29236e-05   4.1 Butterfield Lake 2016      YP low D.E.C.-Standard Filet
##  6:  5.23600e-05   8.2     Francis Lake 2014      YP low       Charley- Fillet
##  7:  5.23600e-05   8.2     Francis Lake 2014      YP low         Charley- Plug
##  8:  5.23600e-05   8.2     Francis Lake 2014      YP low         Charley- Plug
##  9:  5.23600e-05   8.2     Francis Lake 2014      YP low         Charley- Plug
## 10:  5.23600e-05   8.2     Francis Lake 2014      YP low         Charley- Plug
## 11:  5.41350e-05  20.8    Halfmoon Lake 2016      YP low         Charley- Plug
## 12:  5.41350e-05  20.8    Halfmoon Lake 2016      YP low         Charley- Plug
## 13:  5.41350e-05  20.8    Halfmoon Lake 2016      YP low         Charley- Plug
## 14:  5.41350e-05  20.8    Halfmoon Lake 2016      YP low         Charley- Plug
## 15:  5.41350e-05  20.8    Halfmoon Lake 2016      YP low         Charley- Plug
## 16:  9.06850e-05  22.0        Rock Pond 2016      YP low         Charley- Plug
## 17:  9.06850e-05  22.0        Rock Pond 2016      YP low         Charley- Plug
## 18:  9.06850e-05  22.0        Rock Pond 2016      YP low         Charley- Plug
## 19:  9.06850e-05  22.0        Rock Pond 2016      YP low         Charley- Plug
##     length Fish_Wt percent_moisture    THg_DW  THg_WW THg_WW_Avg_Moisture
##  1:    224     148      82.24877487 0.6647429   0.118                 n/a
##  2:    228     166      82.88155569 0.7652565   0.131                 n/a
##  3:    187      82      85.83355246 0.8117773   0.115                 n/a
##  4:    194     102      79.06571132 0.5206769   0.109                 n/a
##  5:    193      84      86.60999426 0.4092605  0.0548                 n/a
##  6:    218     104             80.7 2.3904800  0.4536            0.577139
##  7:    260     210             87.7 3.3116300  0.4084            0.659978
##  8:    204      88             74.3 1.1745500  0.3015            0.234077
##  9:    235     138             81.9 2.3024300  0.4165            0.458854
## 10:    236     130             78.6 4.0089890  0.8569            0.798956
## 11:    210     128              n/a 1.3910000     n/a              0.2803
## 12:    209     110              n/a 2.0640000     n/a               0.416
## 13:    205     120              n/a 1.3320000     n/a              0.2684
## 14:    211     116              n/a 1.5210000     n/a              0.3065
## 15:    200     102              n/a 1.5950000     n/a              0.3214
## 16:    204      92             81.1 1.6500000 0.31185              0.3111
## 17:    248     178             79.5 5.0300000 1.03115              1.0331
## 18:    227     134             80.7 6.3100000 1.21783              1.2244
## 19:    207      94             81.5 1.6300000 0.30155              0.3167
##        log10THg THg_ww_plug Se_ICP_ppb  Se_ICP_ppm Sample_Se_DW
##  1: -1.98766910  0.13701442   2.373204 0.002373204    1.2220743
##  2: -1.88338633  0.15207426   1.413760 0.001413760    0.7316897
##  3: -2.01336494  0.13353857   1.761470 0.001761470    0.9140867
##  4: -2.06683130  0.12658626   2.231434 0.002231434    1.0749525
##  5: -2.75297614  0.06373789   2.455623 0.002455623    1.2242224
##  6: -0.64410034  0.52513477   3.730000 0.003730000    1.9631862
##  7: -0.41554878  0.65997800   5.120000 0.005120000    2.7059653
##  8: -1.45210516  0.23407700   4.590000 0.004590000    3.0952568
##  9: -0.77902320  0.45885400   1.350000 0.001350000    2.3630706
## 10: -0.22444940  0.79895600   4.330000 0.004330000    2.3099038
## 11: -1.27189482  0.28030000   4.979420 0.004979424    2.4188961
## 12: -0.87707002  0.41600000   3.833150 0.003833147    3.8399879
## 13: -1.31527687  0.26840000   4.348470 0.004348473    2.1950371
## 14: -1.18253752  0.30650000   3.268080 0.003268081    1.6981022
## 15: -1.13506883  0.32140000   5.377940 0.005377943    2.5209572
## 16: -1.16764088  0.31110000   2.299780 0.002299776    1.1385577
## 17:  0.03256399  1.03310000   2.619320 0.002619322    1.3586544
## 18:  0.20245093  1.22440000   2.157330 0.002157329    1.1134991
## 19: -1.14980032  0.31670000   2.298090 0.002298085    1.2048105
##     Se_divided_by_mm Hg_divided_by_mm Se_Hg_ratio   delta_N   delta_C
##  1:      0.015477132      0.000683057        4.02 11.516846 -28.31252
##  2:      0.009266587      0.000758135        2.09  9.951286 -27.75586
##  3:      0.011576580      0.000665729        2.46 10.229079 -27.64202
##  4:      0.013613886      0.000631070        4.52 10.192370 -27.37382
##  5:      0.015504336      0.000317752        6.53 10.939434 -26.83743
##  6:      0.024863047      0.002617951        1.83  9.096081 -29.21649
##  7:      0.034270077      0.003290184        1.28  9.600077 -29.88216
##  8:      0.039200314      0.001166943        8.63  8.153571 -29.15088
##  9:      0.029927439      0.002287522        2.37  9.330221 -29.53968
## 10:      0.029254101      0.003983030        1.57  9.303434 -28.72447
## 11:      0.030634450      0.001397378        4.38  8.405568 -31.46434
## 12:      0.048632065      0.002073882        4.69  7.848991 -29.76543
## 13:      0.027799355      0.001338053        4.16  7.889668 -33.35717
## 14:      0.021505854      0.001527992        2.81  8.614905 -31.45662
## 15:      0.031927016      0.001602273        3.99  8.164484 -30.64527
## 16:      0.014419424      0.001550925        1.76  7.598978 -33.84726
## 17:      0.017206870      0.005150307        0.68  9.305418 -30.01240
## 18:      0.014102065      0.006103993        0.45  9.909617 -31.10739
## 19:      0.015258491      0.001578842        1.79  8.205161 -33.38515
##     lake_zoop_deltaN lake_zoop_deltaC  TL_calc adj_THg_ww_plug  Hg_TMS   Hg_TMF
##  1:         1.680000        -28.23704 4.893646        2.860124 0.50820 3.222552
##  2:         1.680000        -28.23704 4.433187        2.399665 0.50820 3.222552
##  3:         1.680000        -28.23704 4.514891        2.481369 0.50820 3.222552
##  4:         1.680000        -28.23704 4.504094        2.470572 0.50820 3.222552
##  5:         1.680000        -28.23704 4.723819        2.690297 0.50820 3.222552
##  6:         3.860000        -33.41000 3.540024        1.530802 0.61711 4.141045
##  7:         3.860000        -33.41000 3.688258        1.679036 0.61711 4.141045
##  8:         3.860000        -33.41000 3.262815        1.253593 0.61711 4.141045
##  9:         3.860000        -33.41000 3.608889        1.599666 0.61711 4.141045
## 10:         3.860000        -33.41000 3.601010        1.591788 0.61711 4.141045
## 11:         3.446474        -32.16594 3.458557        1.444729 0.66610 4.635536
## 12:         3.446474        -32.16594 3.294858        1.281030 0.66610 4.635536
## 13:         3.446474        -32.16594 3.306822        1.292993 0.66610 4.635536
## 14:         3.446474        -32.16594 3.520127        1.506298 0.66610 4.635536
## 15:         3.446474        -32.16594 3.387650        1.373822 0.66610 4.635536
## 16:         3.200000        -38.58500 3.293817        1.277243 0.79820 6.283477
## 17:         3.200000        -38.58500 3.795711        1.779137 0.79820 6.283477
## 18:         3.200000        -38.58500 3.973417        1.956843 0.79820 6.283477
## 19:         3.200000        -38.58500 3.472106        1.455532 0.79820 6.283477
##     Prey_avg_SeHg     log10Se DOC_s
##  1:          3.93  0.20054970   5.1
##  2:          3.93 -0.31239873   5.1
##  3:          3.93 -0.08982983   5.1
##  4:          3.93  0.07227644   5.1
##  5:          3.93  0.20230585   5.1
##  6:          3.14  0.67456877   7.1
##  7:          3.14  0.99545870   7.1
##  8:          3.14  1.12987087   7.1
##  9:          3.14  0.85996188   7.1
## 10:          3.14  0.83720590   7.1
## 11:          4.01  0.88331130   9.8
## 12:          4.01  1.34546921   9.8
## 13:          4.01  0.78619894   9.8
## 14:          4.01  0.52951127   9.8
## 15:          4.01  0.92463866   9.8
## 16:          1.17  0.12976231  21.4
## 17:          1.17  0.30649483  21.4
## 18:          1.17  0.10750736  21.4
## 19:          1.17  0.18632227  21.4

Regression for size adjustment

ggplot(noHLorML_lowTL, aes(x = length, y = THg_ww_plug , color = Waterbody)) + geom_point()+
geom_smooth(method="lm", col="black", se=FALSE)
## `geom_smooth()` using formula 'y ~ x'

Coefficents of regression equation to use

fit.noHLorML_lowTL<-lm(THg_ww_plug~length, noHLorML_lowTL )
fitsumm<-summary(fit.noHLorML_lowTL)
print(fitsumm$coefficients)
##                Estimate  Std. Error   t value    Pr(>|t|)
## (Intercept) -1.95651729 0.635912938 -3.076706 0.006837257
## length       0.01096139 0.002935714  3.733805 0.001652008
  • p value is significant
  • equation is y= .010961(length) - 1.956517

Size adjusting in excel

  • Used excel to find the resdiuals
  • Used the mean fish length to size adjust all low trophic level fish -new column is called “adj_THg_ww_plug”
print(Mastersheet_ADK_Se_Hg_Honors$"adj_THg_ww_plug")
##  [1]       NA       NA       NA       NA       NA       NA       NA 2.860124
##  [9] 2.399665 2.481369 2.470572 2.690297       NA       NA       NA       NA
## [17]       NA 1.530802 1.679036 1.253593 1.599666 1.591788       NA       NA
## [25]       NA       NA 2.380531 2.280153 2.474782 2.555319       NA       NA
## [33]       NA 1.444729 1.281030 1.292993 1.506298 1.373822       NA       NA
## [41]       NA       NA       NA 2.207956 2.095030 2.174691 2.243556 2.001363
## [49]       NA       NA       NA       NA 1.277243 1.779137 1.956843 1.455532

Objective 3. Check if size adjusted prey Hg is lognormal

Objective 3

  • Viewing low TL size adjusted THg values by water body
Hg_lowTL_adj

Resdiual plot

res_lowadj<-resid(lm_Hg_lowTL_adj)
plot(fitted(lm_Hg_lowTL_adj),res_lowadj)
abline(0,0)

> - Look at q-q plot?

q-q plot

  • Points don’t stray at ends
  • normally distributed!
qqnorm(res_lowadj)
qqline(res_lowadj)

Objective 4. Find Hg TMS by plotting Hg vs delta N

Objective 4

-I plotted each individual TMS -used delta 15N stable isotope data and log10THg

Regression 1: Butterfield Lake

BLTL<-Mastersheet_ADK_Se_Hg_Honors %>% filter(Waterbody == "Butterfield Lake")

BL_TMS<-ggplot(BLTL, aes(x = delta_N, y = log10THg)) + geom_point(color="black")+labs(x="δ15N", y= "log10THg (ppm, WW)")+
  geom_smooth(method=lm, se=FALSE, color="tomato")+
  theme(axis.text.x=element_text(size=8),panel.border = element_blank(), panel.grid.major = element_blank(),panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))+
  annotate("text", x = 11.8, y = 0, parse = TRUE, size = 2,label = as.character(expression(paste(italic(R)^{2}~"="~"0.53,"~italic(p)~"="~"0.004"))))+
  rremove("legend")+
  annotate("text", x = 11.8, y = -0.15, parse = TRUE, size = 2,label = as.character(expression(paste(Y~"="~"-0.08(x)+0.78"))))

Butterfield Lake

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

Regression 2: Halfmoon Lake

  • Same code for all other lakes, but one more example…
HMLTL<-Mastersheet_ADK_Se_Hg_Honors %>% filter(Waterbody == "Halfmoon Lake")

HML_TMS<-ggplot(HMLTL, aes(x = delta_N, y = log10THg)) + geom_point(color="black")+
  labs(x="δ15N", y= "log10THg (ppm, WW)")+
  geom_smooth(method=lm, se=FALSE, color="green4")+
  theme(axis.text.x=element_text(size=8),panel.border = element_blank(), panel.grid.major = element_blank(),panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))+
  annotate("text", x = 8.8, y = 0.2, parse = TRUE, size = 2,label = as.character(expression(paste(italic(R)^{2}~"="~"0.79,"~italic(p)~"="~"0.002"))))+
  rremove("legend")+
  annotate("text", x = 8.8, y = 0.1, parse = TRUE, size = 2,label = as.character(expression(paste(Y~"="~"-0.32(x)+3.5"))))

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

All the plots on one

ggarrange(FL_TMS, RP_TMS,HML_TMS, HL_TMS,ML_TMS, BL_TMS, ncol= 3,nrow = 2)

Objective 5. See there are statistical differences in these Hg trophic magnification slopes

Ancova

  • Significant differences in TMS equations among lakes
hgTMS_ancova<-aov(log10THg+delta_N~Waterbody, data = Mastersheet_ADK_Se_Hg_Honors)
summary(hgTMS_ancova)
##             Df Sum Sq Mean Sq F value Pr(>F)  
## Waterbody    5   33.4   6.681   3.313 0.0116 *
## Residuals   50  100.8   2.017                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Next steps

  • Looking at signifcant drivers of TMS (pH and Se:Hg molar ratio) to understand which is more important
  • Looking at drivers of Se:Hg molar ratio
  • More advanced stats hopefully this summer (stepwise and multiple linear regressions)

Questions?