We described the study by Lemmens et al. (2015) who sampled various biotic communities in artificial ponds in Belgium that were classified into one of four management types in Box 15.2. In addition to the biotic sampling, they recorded 15 environmental variables (e.g. depth, salinity, pH etc.) for each pond and covers of submerged, floating, and emergent vegetation. They used RDA to comprehensively examine the patterns in the various biotic communities (ordination) constrained by either management variables, drainage strategy, environmental variables, or the fish community. We will focus on the macroinvertebrate community (abundances of families) and do two RDAs, one constrained by the four management categories and a second constrained by the continuous environmental variables; we did not include management type with the environmental variables in one analysis as some of the predictors varied with management type (i.e. collinearity). We will use a covariance matrix for the invertebrate data and a Hellinger transformation that converts raw abundances to proportional abundances. This provides a comparison to the CCA we will do as a second analysis below.

The paper is here and the pond subset for this example is lemminvert2.csv and we also use the environmental variables lemmenv.csv

Lemmens, P., Mergeay, J., Van Wichelen, J., De Meester, L. & Declerck, S. A. (2015). The impact of conservation management on the community composition of multiple organism groups in eutrophic interconnected man-made ponds. PLoS One, 10, e0139371.

Preliminaries

Load required packages (vegan) plus tidyverse, etc

# packages: vegan
devtools::source_url("https://raw.githubusercontent.com/mjkeough/mjkeough.github.io/refs/heads/main/R/libraries.R")   #This loads a set of packages used often
library(vegan)

Read in data files

lemminvert <- read_csv("../data/lemminvert2.csv")
Rows: 23 Columns: 32── Column specification ────────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): site, manag
dbl (30): managsymb, ca, ba, ac, ly, pla, sp, vi, co, na, ne, no, ple, ga, as, c...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
lemmenv <- read_csv("../data/lemmenv.csv")
Rows: 23 Columns: 17── Column specification ────────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): site, manag
dbl (15): depth, silt, ph, o2, temp, surface, trans, cond, chla, tn, tp, susps, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Do RDA on management first

Set no management as reference Do Hellinger transformation

lemmenv$manag <- factor(lemmenv$manag,levels=c("nm","nf","yf","li"))
lemminvert1 <- lemminvert[,-(1:3)]
lemminvert1h <- decostand(lemminvert1, method = "hellinger")
lemmens1.rda <- rda(lemminvert1h~manag, scale=FALSE, data=lemmenv)
summary(lemmens1.rda)

Call:
rda(formula = lemminvert1h ~ manag, data = lemmenv, scale = FALSE) 

Partitioning of variance:
                 Inertia Proportion
Total         0.29764520   1.000000
Constrained   0.07746038   0.260244
Unconstrained 0.22018483   0.739756

Eigenvalues, and their contribution to the variance 

Importance of components:
                            RDA1       RDA2        RDA3        PC1        PC2
Eigenvalue            0.04834204 0.02378647 0.005331859 0.05773624 0.04007458
Proportion Explained  0.16241499 0.07991553 0.017913474 0.19397671 0.13463875
Cumulative Proportion 0.16241499 0.24233052 0.260243992 0.45422070 0.58885945
                             PC3        PC4        PC5        PC6        PC7
Eigenvalue            0.03044115 0.02399770 0.01555144 0.01303226 0.01008716
Proportion Explained  0.10227329 0.08062518 0.05224826 0.04378454 0.03388988
Cumulative Proportion 0.69113274 0.77175792 0.82400618 0.86779072 0.90168060
                              PC8         PC9        PC10        PC11        PC12
Eigenvalue            0.007047255 0.005960197 0.004974072 0.003539032 0.002560003
Proportion Explained  0.023676695 0.020024504 0.016711412 0.011890102 0.008600855
Cumulative Proportion 0.925357295 0.945381799 0.962093211 0.973983313 0.982584168
                             PC13        PC14         PC15         PC16       PC17
Eigenvalue            0.001901488 0.001002660 0.0009101283 0.0007461774 0.00030008
Proportion Explained  0.006388438 0.003368641 0.0030577625 0.0025069357 0.00100818
Cumulative Proportion 0.988972607 0.992341248 0.9953990102 0.9979059459 0.99891413
                              PC18         PC19
Eigenvalue            0.0001744904 0.0001487147
Proportion Explained  0.0005862362 0.0004996375
Cumulative Proportion 0.9995003625 1.0000000000

Accumulated constrained eigenvalues
Importance of components:
                            RDA1       RDA2        RDA3
Eigenvalue            0.04834204 0.02378647 0.005331859
Proportion Explained  0.62408736 0.30707926 0.068833380
Cumulative Proportion 0.62408736 0.93116662 1.000000000
RsquareAdj(lemmens1.rda)
$r.squared
[1] 0.2602439924

$adj.r.squared
[1] 0.1434404122
coef(lemmens1.rda)
                  RDA1         RDA2          RDA3
managnf  0.49202365663 0.2703047280 -0.1664488455
managyf  0.17666816775 0.4107669398  0.3310327616
managli -0.07699886973 0.5440199345 -0.2024120249
anova.cca(lemmens1.rda, permutations=1000)
Permutation test for rda under reduced model
Permutation: free
Number of permutations: 1000

Model: rda(formula = lemminvert1h ~ manag, data = lemmenv, scale = FALSE)
         Df    Variance       F   Pr(>F)   
Model     3 0.077460376 2.22805 0.002997 **
Residual 19 0.220184826                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens1.rda, permutations=1000, by="margin")
Permutation test for rda under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 1000

Model: rda(formula = lemminvert1h ~ manag, data = lemmenv, scale = FALSE)
         Df    Variance       F   Pr(>F)   
manag     3 0.077460376 2.22805 0.004995 **
Residual 19 0.220184826                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens1.rda, permutations=1000, by="axis")
Permutation test for rda under reduced model
Forward tests for axes
Permutation: free
Number of permutations: 1000

Model: rda(formula = lemminvert1h ~ manag, data = lemmenv, scale = FALSE)
         Df    Variance       F   Pr(>F)   
RDA1      1 0.048342041 4.17149 0.007992 **
RDA2      1 0.023786475 2.05256 0.115884   
RDA3      1 0.005331859 0.46009 0.936064   
Residual 19 0.220184826                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Do triplot; category scaling type = 2 probably the best plots to display

ordiplot(lemmens1.rda,scaling=1,type="text")

ordiplot(lemmens1.rda,scaling=2,type="text")

Do RDA on env variables

Preliminary look at scatterplot; look for variables with unusual distributions and check for correlations

scatterplotMatrix(~depth+silt+ph+temp+surface+cond+chla+tn+tp+susps+subm+reed+emerg,data=lemmenv,diagonal=list(method='boxplot'))

cor(lemmenv[,c('depth','silt','ph','o2','temp','surface','trans','cond','chla','tn','tp','susps','subm','reed','emerg')])
                   depth            silt             ph             o2
depth    1.0000000000000 -0.017616760461  0.13430650028 -0.27121523185
silt    -0.0176167604607  1.000000000000 -0.17348171304 -0.17841277408
ph       0.1343065002808 -0.173481713042  1.00000000000  0.69636421850
o2      -0.2712152318545 -0.178412774082  0.69636421850  1.00000000000
temp    -0.4711265349923 -0.259634999717  0.34190093476  0.59310269041
surface  0.4494213790397 -0.042912057064  0.10830511855  0.11258805864
trans    0.2642537768666  0.024811354455 -0.26203958062 -0.38185774548
cond     0.4365517066083 -0.238480267142  0.17058921334 -0.05588518967
chla    -0.3476115412764  0.250808793062  0.41841689825  0.53236546230
tn       0.2336240250303 -0.475726688577  0.03097470515  0.15752860554
tp       0.1060976538753 -0.314323812990  0.35235706364  0.11827509068
susps   -0.3862440505484  0.009748267036  0.48986299063  0.57466492943
subm    -0.0784175341518 -0.091227150419 -0.21175426468 -0.11745731349
reed     0.0006500370807 -0.277002133433 -0.14608695599  0.01560271693
emerg   -0.2424572860053  0.105829333082 -0.38656803058 -0.09980845727
                  temp        surface           trans            cond
depth   -0.47112653499  0.44942137904  0.264253776867  0.436551706608
silt    -0.25963499972 -0.04291205706  0.024811354455 -0.238480267142
ph       0.34190093476  0.10830511855 -0.262039580615  0.170589213338
o2       0.59310269041  0.11258805864 -0.381857745485 -0.055885189668
temp     1.00000000000 -0.10252025899 -0.512112034212 -0.026694034703
surface -0.10252025899  1.00000000000  0.114288567110  0.450017240377
trans   -0.51211203421  0.11428856711  1.000000000000  0.005469303119
cond    -0.02669403470  0.45001724038  0.005469303119  1.000000000000
chla     0.43945701630  0.05415571708 -0.588751746901 -0.190481924873
tn       0.06453460843  0.34550249931  0.203991975724  0.139579640402
tp      -0.05394590824  0.27162436245  0.061584252994  0.306847862735
susps    0.67849360517 -0.05300543834 -0.751549429641 -0.048437804756
subm    -0.18746419839 -0.18724864572  0.191430935321 -0.394005804277
reed    -0.03402615003  0.11725585051  0.076445586444 -0.066476696670
emerg    0.09539126577  0.08875116750 -0.108264276403 -0.205514809634
                   chla             tn              tp           susps
depth   -0.347611541276  0.23362402503  0.106097653875 -0.386244050548
silt     0.250808793062 -0.47572668858 -0.314323812990  0.009748267036
ph       0.418416898246  0.03097470515  0.352357063644  0.489862990629
o2       0.532365462299  0.15752860554  0.118275090681  0.574664929431
temp     0.439457016299  0.06453460843 -0.053945908244  0.678493605174
surface  0.054155717085  0.34550249931  0.271624362448 -0.053005438344
trans   -0.588751746901  0.20399197572  0.061584252994 -0.751549429641
cond    -0.190481924873  0.13957964040  0.306847862735 -0.048437804756
chla     1.000000000000 -0.27957165781  0.009360204518  0.782024207885
tn      -0.279571657813  1.00000000000  0.500553919228 -0.110792574812
tp       0.009360204518  0.50055391923  1.000000000000  0.140758517363
susps    0.782024207885 -0.11079257481  0.140758517363  1.000000000000
subm     0.060139781123  0.15549644782  0.062217715215 -0.285991871423
reed     0.066846774466  0.34219123738  0.114074475863 -0.133096194086
emerg    0.173774308694  0.06346373809 -0.285908967371  0.079916961444
                  subm             reed          emerg
depth   -0.07841753415  0.0006500370807 -0.24245728601
silt    -0.09122715042 -0.2770021334331  0.10582933308
ph      -0.21175426468 -0.1460869559873 -0.38656803058
o2      -0.11745731349  0.0156027169307 -0.09980845727
temp    -0.18746419839 -0.0340261500303  0.09539126577
surface -0.18724864572  0.1172558505087  0.08875116750
trans    0.19143093532  0.0764455864441 -0.10826427640
cond    -0.39400580428 -0.0664766966696 -0.20551480963
chla     0.06013978112  0.0668467744656  0.17377430869
tn       0.15549644782  0.3421912373808  0.06346373809
tp       0.06221771522  0.1140744758629 -0.28590896737
susps   -0.28599187142 -0.1330961940857  0.07991696144
subm     1.00000000000  0.7316333379287  0.20746707245
reed     0.73163333793  1.0000000000000  0.27856872790
emerg    0.20746707245  0.2785687278980  1.00000000000
# transform chla and susps
lemmenv$lchla <- log10(lemmenv$chla)
lemmenv$lsusps <- log10(lemmenv$susps)
scatterplotMatrix(~depth+silt+ph+o2+temp+surface+trans+cond+lchla+tn+tp+lsusps+subm+reed+emerg,data=lemmenv,diagonal=list(method='boxplot'))

cor(lemmenv[,c('depth','silt','ph','o2','temp','surface','trans','cond','lchla','tn','tp','lsusps','subm','reed','emerg')])
                   depth           silt             ph             o2
depth    1.0000000000000 -0.01761676046  0.13430650028 -0.27121523185
silt    -0.0176167604607  1.00000000000 -0.17348171304 -0.17841277408
ph       0.1343065002808 -0.17348171304  1.00000000000  0.69636421850
o2      -0.2712152318545 -0.17841277408  0.69636421850  1.00000000000
temp    -0.4711265349923 -0.25963499972  0.34190093476  0.59310269041
surface  0.4494213790397 -0.04291205706  0.10830511855  0.11258805864
trans    0.2642537768666  0.02481135446 -0.26203958062 -0.38185774548
cond     0.4365517066083 -0.23848026714  0.17058921334 -0.05588518967
lchla   -0.2503397531454 -0.11550065241  0.42945349903  0.52056739754
tn       0.2336240250303 -0.47572668858  0.03097470515  0.15752860554
tp       0.1060976538753 -0.31432381299  0.35235706364  0.11827509068
lsusps  -0.3302692268658  0.01952331251  0.39782646577  0.52545125907
subm    -0.0784175341518 -0.09122715042 -0.21175426468 -0.11745731349
reed     0.0006500370807 -0.27700213343 -0.14608695599  0.01560271693
emerg   -0.2424572860053  0.10582933308 -0.38656803058 -0.09980845727
                  temp        surface           trans            cond
depth   -0.47112653499  0.44942137904  0.264253776867  0.436551706608
silt    -0.25963499972 -0.04291205706  0.024811354455 -0.238480267142
ph       0.34190093476  0.10830511855 -0.262039580615  0.170589213338
o2       0.59310269041  0.11258805864 -0.381857745485 -0.055885189668
temp     1.00000000000 -0.10252025899 -0.512112034212 -0.026694034703
surface -0.10252025899  1.00000000000  0.114288567110  0.450017240377
trans   -0.51211203421  0.11428856711  1.000000000000  0.005469303119
cond    -0.02669403470  0.45001724038  0.005469303119  1.000000000000
lchla    0.57130803875 -0.04784149815 -0.782615681927 -0.167905947561
tn       0.06453460843  0.34550249931  0.203991975724  0.139579640402
tp      -0.05394590824  0.27162436245  0.061584252994  0.306847862735
lsusps   0.67369835084 -0.14207718016 -0.754240584318 -0.082873919777
subm    -0.18746419839 -0.18724864572  0.191430935321 -0.394005804277
reed    -0.03402615003  0.11725585051  0.076445586444 -0.066476696670
emerg    0.09539126577  0.08875116750 -0.108264276403 -0.205514809634
                  lchla             tn             tp         lsusps           subm
depth   -0.250339753145  0.23362402503  0.10609765388 -0.33026922687 -0.07841753415
silt    -0.115500652408 -0.47572668858 -0.31432381299  0.01952331251 -0.09122715042
ph       0.429453499027  0.03097470515  0.35235706364  0.39782646577 -0.21175426468
o2       0.520567397541  0.15752860554  0.11827509068  0.52545125907 -0.11745731349
temp     0.571308038750  0.06453460843 -0.05394590824  0.67369835084 -0.18746419839
surface -0.047841498152  0.34550249931  0.27162436245 -0.14207718016 -0.18724864572
trans   -0.782615681927  0.20399197572  0.06158425299 -0.75424058432  0.19143093532
cond    -0.167905947561  0.13957964040  0.30684786274 -0.08287391978 -0.39400580428
lchla    1.000000000000 -0.16572488776  0.06844173512  0.87906489045 -0.02898751671
tn      -0.165724887761  1.00000000000  0.50055391923 -0.09724699645  0.15549644782
tp       0.068441735122  0.50055391923  1.00000000000  0.08021720445  0.06221771522
lsusps   0.879064890447 -0.09724699645  0.08021720445  1.00000000000 -0.25143199023
subm    -0.028987516710  0.15549644782  0.06221771522 -0.25143199023  1.00000000000
reed     0.007427848248  0.34219123738  0.11407447586 -0.15696318375  0.73163333793
emerg    0.078136797535  0.06346373809 -0.28590896737  0.08786419690  0.20746707245
                    reed          emerg
depth    0.0006500370807 -0.24245728601
silt    -0.2770021334331  0.10582933308
ph      -0.1460869559873 -0.38656803058
o2       0.0156027169307 -0.09980845727
temp    -0.0340261500303  0.09539126577
surface  0.1172558505087  0.08875116750
trans    0.0764455864441 -0.10826427640
cond    -0.0664766966696 -0.20551480963
lchla    0.0074278482481  0.07813679754
tn       0.3421912373808  0.06346373809
tp       0.1140744758629 -0.28590896737
lsusps  -0.1569631837490  0.08786419690
subm     0.7316333379287  0.20746707245
reed     1.0000000000000  0.27856872790
emerg    0.2785687278980  1.00000000000
# exclude o2, trans, susps and subm
# standardize remaining env variables
lemmenv$depth <- scale(lemmenv$depth)
lemmenv$silt <- scale(lemmenv$silt)
lemmenv$ph <- scale(lemmenv$ph)
lemmenv$temp <- scale(lemmenv$temp)
lemmenv$surface <- scale(lemmenv$surface)
lemmenv$cond <- scale(lemmenv$cond)
lemmenv$clhla <- scale(lemmenv$lchla)
lemmenv$tn <- scale(lemmenv$tn)
lemmenv$tp <- scale(lemmenv$tp)
lemmenv$reed <- scale(lemmenv$reed)
lemmenv$emerg <- scale(lemmenv$emerg)

Now do RDA

lemmens2.rda <- rda(lemminvert1h~depth+silt+ph+temp+surface+cond+lchla+tn+tp+reed+emerg, scale=FALSE, data=lemmenv)
summary(lemmens2.rda)

Call:
rda(formula = lemminvert1h ~ depth + silt + ph + temp + surface +      cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE) 

Partitioning of variance:
                Inertia Proportion
Total         0.2976452  1.0000000
Constrained   0.1738784  0.5841801
Unconstrained 0.1237668  0.4158199

Eigenvalues, and their contribution to the variance 

Importance of components:
                            RDA1       RDA2       RDA3       RDA4       RDA5
Eigenvalue            0.07097527 0.02767266 0.02190875 0.01548858 0.01387985
Proportion Explained  0.23845597 0.09297196 0.07360692 0.05203707 0.04663220
Cumulative Proportion 0.23845597 0.33142793 0.40503485 0.45707192 0.50370412
                             RDA6        RDA7       RDA8        RDA9       RDA10
Eigenvalue            0.008638895 0.007031864 0.00337650 0.002814733 0.001299755
Proportion Explained  0.029024135 0.023624989 0.01134404 0.009456670 0.004366793
Cumulative Proportion 0.532728252 0.556353241 0.56769729 0.577153955 0.581520749
                             RDA11        PC1        PC2        PC3        PC4
Eigenvalue            0.0007915507 0.03586183 0.02942058 0.02094292 0.01212848
Proportion Explained  0.0026593768 0.12048515 0.09884445 0.07036202 0.04074811
Cumulative Proportion 0.5841801256 0.70466528 0.80350973 0.87387175 0.91461985
                             PC5         PC6         PC7        PC8         PC9
Eigenvalue            0.00888458 0.006672897 0.003610473 0.00317100 0.001995268
Proportion Explained  0.02984956 0.022418964 0.012130124 0.01065362 0.006703511
Cumulative Proportion 0.94446942 0.966888383 0.979018506 0.98967213 0.996375640
                              PC10         PC11
Eigenvalue            0.0007039954 0.0003747779
Proportion Explained  0.0023652167 0.0012591431
Cumulative Proportion 0.9987408569 1.0000000000

Accumulated constrained eigenvalues
Importance of components:
                            RDA1       RDA2       RDA3       RDA4       RDA5
Eigenvalue            0.07097527 0.02767266 0.02190875 0.01548858 0.01387985
Proportion Explained  0.40818911 0.15914948 0.12600039 0.08907710 0.07982503
Cumulative Proportion 0.40818911 0.56733859 0.69333898 0.78241607 0.86224110
                             RDA6        RDA7       RDA8        RDA9       RDA10
Eigenvalue            0.008638895 0.007031864 0.00337650 0.002814733 0.001299755
Proportion Explained  0.049683537 0.040441274 0.01941874 0.016187936 0.007475080
Cumulative Proportion 0.911924642 0.952365916 0.97178466 0.987972596 0.995447677
                             RDA11
Eigenvalue            0.0007915507
Proportion Explained  0.0045523233
Cumulative Proportion 1.0000000000
RsquareAdj(lemmens2.rda)
$r.squared
[1] 0.5841801256

$adj.r.squared
[1] 0.1683602513
coef(lemmens2.rda)
                   RDA1            RDA2            RDA3            RDA4
depth   -0.027827013960  0.097787772113  0.281776841418  0.001809248940
silt    -0.041524684620  0.002443836661 -0.014896200153  0.061110545016
ph       0.027784824497 -0.099819078919 -0.126115529822  0.019408181201
temp     0.040159466862  0.182510222456  0.156464306608 -0.022332642996
surface -0.106615297865  0.058287161409 -0.147576852598  0.072742800898
cond     0.007432000526 -0.009086445965 -0.057118967599  0.023625776294
lchla   -0.241782993060 -0.118248368166  0.008496115872  0.038842483023
tn      -0.086852207478 -0.151468225314  0.021292558754 -0.004888437097
tp       0.034022811646 -0.059610394027  0.092183887666  0.011124810860
reed     0.115994815964 -0.010115642476 -0.034817128316  0.018071533005
emerg    0.081302586926 -0.035022909372  0.079539514733  0.181049457796
                   RDA5           RDA6           RDA7             RDA8
depth    0.041750117699 -0.09783228343  0.06238417334  0.1275091139774
silt    -0.003516765753 -0.12487992285 -0.19851367319  0.0002741853404
ph      -0.099856889972 -0.03236490214  0.06625034925 -0.0296118847212
temp     0.190844935945 -0.20024836705  0.11878751921  0.0239344229513
surface  0.108027770955  0.04771445227  0.08158333216  0.0842996919252
cond    -0.004346753854  0.06268509025 -0.17357130013 -0.2267534018730
lchla   -0.201350043099  0.01945458343 -0.18044651869 -0.1622892530912
tn      -0.066966106883  0.09508382346 -0.17111591842 -0.1451144331073
tp       0.149760449615 -0.12024702246  0.09625926497  0.1248741184542
reed     0.049014623705 -0.11848380762 -0.09662756860  0.0215409951205
emerg   -0.049261625592  0.01303034263  0.11961367593 -0.0167142896245
                   RDA9           RDA10          RDA11
depth   -0.109191233756 -0.035931245513 -0.02001413486
silt     0.042173793165  0.015283593038  0.12489367926
ph      -0.049888738528 -0.184727843157  0.11715791787
temp     0.025240454583  0.028688551523  0.04354011837
surface  0.069740524542 -0.029899758357 -0.06813214843
cond    -0.091851032794 -0.008026561415  0.02717284095
lchla    0.037023640316  0.088684719506 -0.20660847510
tn       0.190145382693 -0.078015669299  0.09328951688
tp      -0.128093530846  0.178299057469  0.01768816041
reed    -0.004064957083 -0.088554826464 -0.10755779866
emerg   -0.090135200928  0.017557975666  0.07219658630
anova.cca(lemmens2.rda, permutations=1000)
Permutation test for rda under reduced model
Permutation: free
Number of permutations: 1000

Model: rda(formula = lemminvert1h ~ depth + silt + ph + temp + surface + cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE)
         Df   Variance       F   Pr(>F)  
Model    11 0.17387841 1.40489 0.048951 *
Residual 11 0.12376679                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens2.rda, permutations=1000, by="margin")
Permutation test for rda under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 1000

Model: rda(formula = lemminvert1h ~ depth + silt + ph + temp + surface + cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE)
         Df    Variance       F   Pr(>F)  
depth     1 0.016940265 1.50560 0.160839  
silt      1 0.007852052 0.69787 0.676324  
ph        1 0.009913454 0.88108 0.536464  
temp      1 0.016429713 1.46022 0.169830  
surface   1 0.021651029 1.92427 0.060939 .
cond      1 0.005374499 0.47767 0.915085  
lchla     1 0.024329692 2.16235 0.039960 *
tn        1 0.011761439 1.04532 0.397602  
tp        1 0.008175005 0.72657 0.688312  
reed      1 0.020252613 1.79999 0.073926 .
emerg     1 0.016889424 1.50108 0.142857  
Residual 11 0.123766790                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens2.rda, permutations=1000, by="axis")
Permutation test for rda under reduced model
Forward tests for axes
Permutation: free
Number of permutations: 1000

Model: rda(formula = lemminvert1h ~ depth + silt + ph + temp + surface + cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE)
         Df    Variance       F   Pr(>F)  
RDA1      1 0.070975274 6.30806 0.050949 .
RDA2      1 0.027672659 2.45946 0.806194  
RDA3      1 0.021908747 1.94718 0.940060  
RDA4      1 0.015488584 1.37658 0.998002  
RDA5      1 0.013879850 1.23360 0.998002  
RDA6      1 0.008638895 0.76780 1.000000  
RDA7      1 0.007031864 0.62497           
RDA8      1 0.003376500 0.30009           
RDA9      1 0.002814733 0.25016           
RDA10     1 0.001299755 0.11552           
RDA11     1 0.000791551 0.07035           
Residual 11 0.123766790                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ideally would like ponds represented by symbols, a different one for each management category
ordiplot(lemmens2.rda,scaling=1,type="text")

ordiplot(lemmens2.rda,scaling=2,type="text")

Do CCA without hellinger transform for comparison

Management first

lemmens1.cca <- cca(lemminvert1~manag, scale=FALSE, data=lemmenv)
summary(lemmens1.cca)

Call:
cca(formula = lemminvert1 ~ manag, data = lemmenv, scale = FALSE) 

Partitioning of scaled Chi-square:
                Inertia Proportion
Total         1.3064388  1.0000000
Constrained   0.3205724  0.2453788
Unconstrained 0.9858664  0.7546212

Eigenvalues, and their contribution to the scaled Chi-square 

Importance of components:
                           CCA1       CCA2       CCA3       CA1       CA2       CA3
Eigenvalue            0.2126994 0.06529970 0.04257331 0.1979512 0.1421607 0.1361162
Proportion Explained  0.1628085 0.04998298 0.03258730 0.1515197 0.1088154 0.1041888
Cumulative Proportion 0.1628085 0.21279152 0.24537882 0.3968985 0.5057139 0.6099027
                             CA4        CA5        CA6        CA7        CA8
Eigenvalue            0.11519732 0.09440232 0.07995911 0.07312735 0.03626965
Proportion Explained  0.08817659 0.07225928 0.06120387 0.05597457 0.02776223
Cumulative Proportion 0.69807926 0.77033853 0.83154241 0.88751698 0.91527920
                             CA9       CA10       CA11       CA12        CA13
Eigenvalue            0.02754670 0.02111008 0.01986766 0.01527327 0.009234612
Proportion Explained  0.02108534 0.01615849 0.01520750 0.01169077 0.007068538
Cumulative Proportion 0.93636454 0.95252303 0.96773053 0.97942129 0.986489831
                             CA14        CA15        CA16        CA17         CA18
Eigenvalue            0.005370932 0.004777111 0.003991910 0.002447783 0.0007410925
Proportion Explained  0.004111124 0.003656590 0.003055566 0.001873630 0.0005672616
Cumulative Proportion 0.990600954 0.994257544 0.997313110 0.999186739 0.9997540011
                              CA19
Eigenvalue            0.0003213826
Proportion Explained  0.0002459989
Cumulative Proportion 1.0000000000

Accumulated constrained eigenvalues
Importance of components:
                           CCA1      CCA2       CCA3
Eigenvalue            0.2126994 0.0652997 0.04257331
Proportion Explained  0.6634988 0.2036972 0.13280404
Cumulative Proportion 0.6634988 0.8671960 1.00000000
RsquareAdj(lemmens1.cca)
$r.squared
[1] 0.245378816

$adj.r.squared
[1] 0.07299686871
coef(lemmens1.cca)
                 CCA1        CCA2          CCA3
managnf -1.8756014038 2.714225755 -0.6027935072
managyf -0.5970430277 2.479946396 -2.7985775306
managli  1.0423050498 4.267961338  0.1515415907
anova.cca(lemmens1.cca, permutations=1000)
Permutation test for cca under reduced model
Permutation: free
Number of permutations: 1000

Model: cca(formula = lemminvert1 ~ manag, data = lemmenv, scale = FALSE)
         Df  ChiSquare      F  Pr(>F)  
Model     3 0.32057241 2.0594 0.03996 *
Residual 19 0.98586641                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens1.cca, permutations=1000, by="margin")
Permutation test for cca under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 1000

Model: cca(formula = lemminvert1 ~ manag, data = lemmenv, scale = FALSE)
         Df  ChiSquare      F   Pr(>F)  
manag     3 0.32057241 2.0594 0.055944 .
Residual 19 0.98586641                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens1.cca, permutations=1000, by="axis")
Permutation test for cca under reduced model
Forward tests for axes
Permutation: free
Number of permutations: 1000

Model: cca(formula = lemminvert1 ~ manag, data = lemmenv, scale = FALSE)
         Df  ChiSquare       F  Pr(>F)  
CCA1      1 0.21269940 4.09923 0.03996 *
CCA2      1 0.06529970 1.25848 0.97303  
CCA3      1 0.04257331 0.82049 1.00000  
Residual 19 0.98586641                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ordiplot(lemmens1.cca,scaling=1,type="text")

ordiplot(lemmens1.cca,scaling=2,type="text")

Now env variables

lemmens2.cca <- cca(lemminvert1~depth+silt+ph+temp+surface+cond+lchla+tn+tp+reed+emerg, scale=FALSE, data=lemmenv)
summary(lemmens2.cca)

Call:
cca(formula = lemminvert1 ~ depth + silt + ph + temp + surface +      cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE) 

Partitioning of scaled Chi-square:
                Inertia Proportion
Total         1.3064388   1.000000
Constrained   0.8476070   0.648792
Unconstrained 0.4588318   0.351208

Eigenvalues, and their contribution to the scaled Chi-square 

Importance of components:
                           CCA1       CCA2       CCA3      CCA4       CCA5
Eigenvalue            0.3089047 0.12250474 0.11334823 0.1025672 0.06607109
Proportion Explained  0.2364479 0.09376998 0.08676123 0.0785090 0.05057343
Cumulative Proportion 0.2364479 0.33021783 0.41697906 0.4954881 0.54606149
                            CCA6       CCA7       CCA8        CCA9       CCA10
Eigenvalue            0.04594123 0.04188759 0.02394964 0.009575194 0.007580523
Proportion Explained  0.03516524 0.03206242 0.01833200 0.007329233 0.005802433
Cumulative Proportion 0.58122672 0.61328914 0.63162114 0.638950376 0.644752809
                            CCA11        CA1        CA2        CA3        CA4
Eigenvalue            0.005276912 0.13042347 0.08363283 0.07065464 0.05466948
Proportion Explained  0.004039157 0.09983129 0.06401588 0.05408186 0.04184618
Cumulative Proportion 0.648791966 0.74862325 0.81263913 0.86672099 0.90856718
                             CA5        CA6        CA7        CA8         CA9
Eigenvalue            0.03496095 0.02269653 0.02121667 0.01874746 0.009480517
Proportion Explained  0.02676049 0.01737282 0.01624008 0.01435005 0.007256763
Cumulative Proportion 0.93532767 0.95270049 0.96894057 0.98329061 0.990547374
                             CA10        CA11
Eigenvalue            0.008061064 0.004288213
Proportion Explained  0.006170258 0.003282368
Cumulative Proportion 0.996717632 1.000000000

Accumulated constrained eigenvalues
Importance of components:
                           CCA1      CCA2      CCA3      CCA4       CCA5       CCA6
Eigenvalue            0.3089047 0.1225047 0.1133482 0.1025672 0.06607109 0.04594123
Proportion Explained  0.3644433 0.1445301 0.1337273 0.1210080 0.07795015 0.05420110
Cumulative Proportion 0.3644433 0.5089734 0.6427007 0.7637087 0.84165883 0.89585993
                            CCA7       CCA8        CCA9       CCA10       CCA11
Eigenvalue            0.04188759 0.02394964 0.009575194 0.007580523 0.005276912
Proportion Explained  0.04941864 0.02825560 0.011296737 0.008943441 0.006225658
Cumulative Proportion 0.94527857 0.97353416 0.984830901 0.993774342 1.000000000
RsquareAdj(lemmens2.cca)
$r.squared
[1] 0.6487919661

$adj.r.squared
[1] 0.1810428055
coef(lemmens2.cca)
                   CCA1           CCA2          CCA3           CCA4           CCA5
depth   -0.066539712236  0.96280842662  0.5406505133  1.28833478814  0.26666886924
silt     0.161762328239 -0.09135611086  0.1293435591  0.53354734063  0.01880639300
ph      -0.006636006879 -0.13599469934  0.2518456285 -0.62046343044  0.01350561394
temp    -0.237453825454  0.27614949100 -0.3238749038  1.65888130251 -0.23938301589
surface  0.895432275786 -0.51280484309 -0.4397461854 -0.03188881466 -0.64903021311
cond     0.035730783849  0.20816466461 -0.6466292115 -0.52081629247  0.50279967795
lchla    1.081074937017 -0.11095517842  0.9026346352 -0.66509686361  1.17881979584
tn       0.110531773475 -0.37589156172  0.4497828844 -0.35433812223 -0.29211684378
tp      -0.229639704436  0.08244039408  0.4463635874  0.87161108436 -0.42254809523
reed    -0.534953402005  0.04291148566  0.3509904173  0.58057579084  0.02778363371
emerg   -0.308348442016  1.12860404740  0.5194305421 -0.45281560189 -0.24060238981
                 CCA6           CCA7           CCA8           CCA9         CCA10
depth    0.1726202332  0.65796573889 -0.82802081822  0.10827547138 -0.4476052213
silt     0.9098977223 -0.55971463694  0.70170696899  1.03840220411  0.4336179958
ph       0.5663009265 -0.29253717887  0.61713638219 -0.71714846972 -0.7727238411
temp     0.2137030431  1.02763770746  0.08488582728  0.17138584417 -1.0270382846
surface  0.3919411180  0.45754331613  0.44561621484 -0.60224107673  0.5666761109
cond    -0.7668486376 -1.00037817595  0.41863433290  0.10385365451  0.1532509210
lchla   -0.6790256069 -0.13046692996  0.79359814527  0.53223356611  1.4607412409
tn      -0.3567024003 -0.47722491534 -0.50276715774 -0.04089715496  0.9182710536
tp      -0.4654969432  0.06120385482  0.18854091789  0.96367131608 -0.8642624381
reed     0.6203607959 -0.52329288974  0.86236588574 -0.46441902411  0.1737193114
emerg   -0.2116380072  0.21862037347  0.66281604122  0.14471449531 -0.6056461956
                 CCA11
depth    0.65212933505
silt    -0.84602093795
ph      -0.60889193713
temp     0.39028411390
surface  0.87113888883
cond    -0.74056658365
lchla   -0.47059185592
tn      -1.98736568881
tp       0.97810142298
reed     0.40880133802
emerg    0.01218897821
anova.cca(lemmens2.cca, permutations=1000)
Permutation test for cca under reduced model
Permutation: free
Number of permutations: 1000

Model: cca(formula = lemminvert1 ~ depth + silt + ph + temp + surface + cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE)
         Df  ChiSquare       F   Pr(>F)  
Model    11 0.84760701 1.84732 0.022977 *
Residual 11 0.45883181                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens2.cca, permutations=1000, by="margin")
Permutation test for cca under reduced model
Marginal effects of terms
Permutation: free
Number of permutations: 1000

Model: cca(formula = lemminvert1 ~ depth + silt + ph + temp + surface + cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE)
         Df  ChiSquare       F   Pr(>F)  
depth     1 0.07653476 1.83484 0.089910 .
silt      1 0.03003632 0.72009 0.755245  
ph        1 0.03209059 0.76934 0.769231  
temp      1 0.07052392 1.69074 0.115884  
surface   1 0.09776895 2.34391 0.038961 *
cond      1 0.05217511 1.25084 0.329670  
lchla     1 0.08569971 2.05456 0.053946 .
tn        1 0.01863280 0.44670 0.966034  
tp        1 0.03916227 0.93887 0.626374  
reed      1 0.07305856 1.75150 0.129870  
emerg     1 0.09159059 2.19579 0.050949 .
Residual 11 0.45883181                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova.cca(lemmens2.cca, permutations=1000, by="axis")
Permutation test for cca under reduced model
Forward tests for axes
Permutation: free
Number of permutations: 1000

Model: cca(formula = lemminvert1 ~ depth + silt + ph + temp + surface + cond + lchla + tn + tp + reed + emerg, data = lemmenv, scale = FALSE)
         Df  ChiSquare       F   Pr(>F)  
CCA1      1 0.30890465 7.40566 0.043956 *
CCA2      1 0.12250474 2.93692 0.897103  
CCA3      1 0.11334823 2.71740 0.995005  
CCA4      1 0.10256720 2.45894 0.999001  
CCA5      1 0.06607109 1.58398 1.000000  
CCA6      1 0.04594123 1.10139           
CCA7      1 0.04188759 1.00421           
CCA8      1 0.02394964 0.57417           
CCA9      1 0.00957519 0.22955           
CCA10     1 0.00758052 0.18173           
CCA11     1 0.00527691 0.12651           
Residual 11 0.45883181                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ideally would like ponds represented by symbols, a different one for each management category
ordiplot(lemmens2.cca,scaling=1,type="text")

ordiplot(lemmens2.cca,scaling=2,type="text")

Nice graphics

Plots used for QK use the ggplot classic theme, with some tweaks. Tweaks are consolidated into theme_QK; use this theme for figures and tweak the theme to avoid repetitive code changes.

library(ggvegan)
p1<-autoplot(lemmens1.rda)
p2<-autoplot(lemmens2.rda)
p3<-autoplot(lemmens1.cca)
p4<-autoplot(lemmens2.cca)
l1rt<-fortify(lemmens1.rda)
l1rt$label <- l1rt$label %>%
  str_replace_all("managnm", "nm") %>%
  str_replace_all("managli", "li") %>%
  str_replace_all("managnf", "nf") %>%
  str_replace_all("managyf", "yf")
l2rt<-fortify(lemmens2.rda)
l1ct<-fortify(lemmens1.cca)
l1ct$label <- l1ct$label %>%
  str_replace_all("managnm", "nm") %>%
str_replace_all("managli", "li") %>%
str_replace_all("managnf", "nf") %>%
str_replace_all("managyf", "yf")
l2ct<-fortify(lemmens2.cca)
p5<-p1+p2+p3+p4
p5

library(ggrepel)
br=c("nm","li","nf","yf")  #Set order of groups; define as object to avoid repeated code
la=c("None", "Light", "No fish", "Young fish")  #Set legend text
p1a<-ggplot()+
  geom_point(aes(x=RDA1,y=RDA2, shape=lemminvert$manag), color=sc, data=filter(l1rt, Score=="sites"), 
             alpha=0.7, size=1)+
  scale_shape_manual(values=c(21,22,23,25),
                     name="Management",
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), size=2, data=filter(l1rt, Score=="species"), 
                  )+
  geom_segment(aes(x=0,y=0,xend=RDA1,yend=RDA2), arrow=arrow(length=unit(0.2,"cm")),
               data=filter(l1rt, Score=="centroids"))+
  geom_text_repel(aes(x=RDA1,y=RDA2, label=Label), size=3, data=filter(l1rt, Score=="centroids"), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position="none")
Error in `filter()`:
ℹ In argument: `Score == "sites"`.
Caused by error:
! object 'Score' not found
Run `]8;;x-r-run:rlang::last_trace()rlang::last_trace()]8;;` to see where the error occurred.
```r
p2a<-ggplot()+
  geom_point(aes(x=RDA1,y=RDA2, ), data=filter(l2rt, Score==\sites\), alpha=0.7, color=sc, size=0.5)+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), size=2, data=filter(l2rt, Score==\species\), 
                  )+
  geom_segment(aes(x=0,y=0,xend=RDA1,yend=RDA2), alpha=0.5, arrow=arrow(length=unit(0.2,\cm\)),
               data=filter(l2rt, Score==\biplot\))+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), nudge_y=-0.05,color=lc, size=3.5, data=filter(l2rt, Score==\biplot\), 
                  )+
  labs(y=NULL)+
                 coord_fixed()
p2a

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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```r
```r
p3a<-ggplot()+
  geom_point(aes(x=CCA1,y=CCA2, shape=lemminvert$manag), color= sc, data=filter(l1ct, Score==\sites\), alpha=0.7, size=1)+
  scale_shape_manual(values=c(21,22,23,25),
                     name=\Management\,
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), size=2, data=filter(l1ct, Score==\species\), 
                  )+
  geom_segment(aes(x=0,y=0,xend=CCA1,yend=CCA2), arrow=arrow(length=unit(0.2,\cm\)),
               data=filter(l1ct, Score==\centroids\))+
  geom_text_repel(aes(x=CCA1,y=CCA2, label=Label), size=3, data=filter(l1ct, Score==\centroids\), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position=\none\)
p3a

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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\" />\n\n<!-- rnb-plot-end -->\n"} -->


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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
```r
p4a<-ggplot()+
  geom_point(aes(x=CCA1,y=CCA2, ), data=filter(l2ct, Score==\sites\), alpha=0.7, color=sc, size=0.5)+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), size=2, data=filter(l2ct, Score==\species\), 
                  )+
  geom_segment(aes(x=0,y=0,xend=CCA1,yend=CCA2), alpha=0.5, arrow=arrow(length=unit(0.2,\cm\)),
               data=filter(l2ct, Score==\biplot\))+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), color=lc, size=3, data=filter(l2ct, Score==\biplot\), 
                  )+
  labs(y=NULL)+
                 coord_fixed()
p4a

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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NjX9qtc20L4UjTYXFRXZxo0bfamSNWvWzLudA+vXr++27o71OyzVgM2bN3f9tmbNmuJL6/eD/CgI+CrApDVfe4Z6RRWYN2+eDRo0yC666KIKg92oJ+EBBBBAAAEEEMgaAVIasqarM6OhGvEcOnSo3XDDDdaxY8fMaBStQAABBBBAAIGkChDwJpWXkyda4LnnnrNVq1bZpZdeWnxqfVz7yiuvFP/MDQQQQAABBBBAIFKAgDdSg9veCwwcOND0RUEAAQQQQAABBOIVIIc3XimOQwABBBBAAAEEEAilAAFvKLuNSiOAAAIIIIAAAgjEK0DAG68UxyGAAAIIIIAAAgiEUoCAN5TdRqURQAABBBBAAAEE4hUg4I1XiuMQQAABBBBAAAEEQilAwBvKbqPSCCCAAAIIIIAAAvEKEPDGK8VxCCCAAAIIIIAAAqEUIOANZbdRaQQQQAABBBBAAIF4BQh445XiOAQQQAABBBBAAIFQChDwhrLbqDQCCCCAAAIIIIBAvAIEvPFKcRwCCCCAAAIIIIBAKAUIeEPZbVQaAQQQQAABBBBAIF4BAt54pTgOAQQQQAABBBBAIJQCBLyh7DYqjQACCCCAAAIIIBCvAAFvvFIchwACCCCAAAIIIBBKAQLeUHYblUYAAQQQQAABBBCIV4CAN14pjkMAAQQQQAABBBAIpQABbyi7jUojgAACCCCAAAIIxCtAwBuvFMchgAACCCCAAAIIhFKAgDeU3UalEUAAAQQQQAABBOIVIOCNV4rjEEAAAQQQQAABBEIpkBvKWlNpBBBAAIGMENi4caNNnTrVtm7dakcddZTVqME4TEZ0LI1AwDMBfrN41iFUBwEEEMgmgXfffdc2bNhghYWFNnv27GxqOm1FAIEUChDwphCbSyGAAAIIlBTYaaedXMC7bds2a9KkSckH+QkBBBBIkAApDQmC5DQIIIAAApUXaN++veXl5VnDhg1tl112qfwJeAYCCCAQhwABbxxIHIIAAgggkByBnJwc23fffZNzcs6KAAII/J8AKQ28FBBAAAEEEEAAAQQyWoCAN6O7l8YhgAACCCCAAAIIEPDyGkAAAQQQQAABBBDIaAEC3ozuXhqHAAIIIIAAAgggwKQ1XgNZIRBrMXtNmtFXrGPSheRTnVQX35xUHxWfnHx9PfnYd+o3+i72b5ewvJ6C/4uxW8OjCKRPIGf72ofb0nd5roxA8gXWrl1rNWvWjHqh4I/uli1boh6T6ge0TJPqpQX5fSm+OukPrXbr8qUEr7WioiJfqmS1a9c2/arftGmTN3XKzc11dcIpdpfISbvQ6cuXUqdOHVO/bd68ubhK+llLy1EQ8FWAEV5fe4Z6JUxAf+Rj/aGvVauWCwjWrVuXsGtW90SNGzc21Wv16tXVPVXCnq8gXH98tSOWLyU/P9+9mfHJScGAgvD169f7wmRNmzZ1waVPTvXq1XNBk09vVpo1a2Z64+uTU/369V1gGet3WKpfaPpdoPqsWbOm+NK6j4KAzwLk8PrcO9QNAQQQQAABBBBAoNoCjPBWm5ATIIAAAokX0EfYV111lUttadSokWmk7/DDD7f999/fhgwZYrfffnviL8oZEUAAgQwVIODN0I6lWQggEG6B2bNnu5zIK6+80ubMmWNKAXj88cetoKDAOnfuHO7GUXsEEEAgxQKkNKQYnMshgAAC8QjUrVvX5XHr2FWrVlmrVq3s559/tv/85z92/PHHx3MKjkEAAQQQ+D8BAl5eCggggICHAm3atDFNpPzHP/5h7777rpsw2LNnT7dyB7PhPewwqoQAAl4LkNLgdfdQOQQQyGaB4cOHuxn6WrFDS9Rp9YD+/ftnMwltRwABBKokwAhvldh4EgIIIJA8Aa2Z+9lnn9n48ePdEme60ldffeWWYOvWrVvyLsyZEUAAgQwVYIQ3QzuWZiGAQHgFpk2bZkuWLHFpDLqtIPfAAw90X+FtFTVHAAEE0ifACG/67LkyAgggUK6ANtTQKK92r/JpJ7JyK8udCCCAQAgEGOENQSdRRQQQyC4BTVjTjl8Kevfbb7/sajytRQABBJIgQMCbBFROiQACCFRHQFsTE+hWR5DnIoAAAiUFSGko6cFPCCCAAAIIIIAAAhkmQMCbYR1KcxBAAAEEEEAAAQRKChDwlvTgJwQQQAABBBBAAIEMEyDgzbAOpTkIIIAAAggggAACJQUIeEt68BMCCCCAAAIIIIBAhgkQ8GZYh9IcBBBAAAEEEEAAgZICBLwlPfgJAQQQQAABBBBAIMMECHgzrENpDgIIIIAAAggggEBJAQLekh78hAACCCCAAAIIIJBhAgS8GdahNAcBBBBAAAEEEECgpAABb0kPfkIAAQQQSJPAtGnTbPLkyWm6OpdFAIFMFsjN5MbRNgQQQMBXgRdffNHee+89a9Cgge2www7WtWtXe/rpp23Lli128MEH29lnn+1r1RNWrzlz5lidOnVs1113dYFu69atLT8/P2Hn50QIIIBAIMAIbyDBdwQQqLLApk2bbOXKlVV+fjY+UQHvnXfeacccc4xt2LDBdtppJzvzzDNdsDtlypSsIKldu7Y9/vjjVlRUZGPGjLEFCxbYjBkzsqLtNBIBBFIrQMCbWm+uhkDGCShYmTBhgilI+/TTTzOufclqUN26dd2pt23b5r4/8cQTNn/+fOvSpYtt3bo1WZf16rx77723LV261N555x078sgjvaoblUEAgcwSIKUhs/qT1iCQcoEff/zRNMLbsGFD++mnn1J+/bBe8PTTT7drrrnGCgsL3Uf6TZo0scWLF9uiRYvciGdY21XZeh922GF2xx132PPPP28ffPBBZZ/O8QgggEBcAgS8cTFxEAIIRBPYeeedXR7m+vXrrV27dtEO4/5SAr/88otdccUV9uqrr1q9evXsxBNPdKkNymnNpnLCCSe4148M9txzT9PriIIAAggkWoCAN9GinA+BLBOoWbOm9enTxwUqwcf0WUZQpeZ27tzZ7r//ftMbBgV9KtkW7KrNmrTXt29f3XQBr7vBPwgggECCBQh4EwzK6RDIVgGC3cr1vPJXhwwZUrkncTQCCCCAQJUEmLRWJTaehAACCCCAAAIIIBAWAQLesPQU9UQgQwS09uzs2bPtwQcfdBO2tO7sm2++aVqmS8tzURBAAAEEEEi0AAFvokU5X8oEtISTZrRTwiXw8MMP2zfffGONGjWyZ555xsaOHWuawLX77rvbDTfcEK7GUFsEEEAAgVAIEPCGopuoZGmBhQsX2uWXX27z5s0r/RA/ey5Qo0YNO/74461Dhw62evVqmzRpkp1yyin2m9/8xn744QfPa0/1EEAAAQTCKMCktTD2WpbXWSOCjz76qDVu3DjLJcLZfK3qoKA3KFqtYPz48bbPPvuYHqMggAACCCCQaAEC3kSLcr6kC2g1AH0sfs8991hOTk6Z682dO9ft3BQ80KtXL2vZsmXwY5nvCr5yc3Pd8khlHkzTHaqP6qUlmyoq69ats1mzZlm3bt0qOrRajweBamSwWpUTnnrqqa5dSmFQ32hnsVdeecUmTpzolumKp83BdeWk10BlnhM8N1nfVScVn4L3oC4+OdWqVcvtKKfvvhQ5+fZ6ko9eU3l5eb4wud9Nqlfk6ynYMdCbSlIRBEoJEPCWAuFH/wV69+5dXMnyfsl+9dVX9sgjjxQfc9BBB1mbNm2Kfy7vhv7I+fSHNwjk69evX151S9yn/FdN+jrqqKNK3J+MH1Sv6v7hPeuss1zV1DYFvSrnnnuu+17ZfyrjVNlzV/f42rVrV/cUCXu+j06qU3n/fxPW6CqcSHXSG7p4/t9V4fRVeoqvTkEgHjSKCaeBBN99FSDg9bVnqFeVBU466STTV1BWrlxpP//8c/Bjme/6xa3gRCOlvhSla6heK1asKFOlyZMn20MPPWStWrWynXbayTRi+uuvv5rymm+++WY3QtW2bVs744wzyjy3Onco0NVIk7bC9aXk5+e7kVT1sS9Fm0coSPFpx7CmTZu64LKgoMAXJre7XFFRkW3cuNGbOjVr1sy0aohyy30pCr43b97stu/2pU7Nmzd3/bZmzZriKun3g3bLoyDgq8B/E+l8rSH1QgCBEgL6g9y9e3cX3H7xxRfFAYNSAjRi1rFjR7ddrYIJCgIIIIAAAgiYEfDyKkAghALBx+UaSQyKRhSVq9y6dWu36gEBbyDDdwQQQACBbBcgpSHbXwG0P5QCL730klvLdv/993fpGGpEnz59bPjw4aaPrTUKrKW+KAgggAACCCBglrP9I9BtQCCQyQLK79y0aVPUJoYth1dLeCnHUGvZBiO9kY1Tvp/alOhCDm98ouTwxuekfE9yeCu2ClMOr3KgKQj4KsAIr689Q70QiCKw5557ui14ywt29ZRkBLtRqsLdCCCAAAIIhEKAgDcU3UQlEfivwF577fXfH7iFAAIIIIAAAhUKMGmtQiIOQAABBBBAAAEEEAizAAFvmHuPuiOAAAIIIIAAAghUKEDAWyERByCAAAIIIIAAAgiEWYCAN8y9R90RQACBagjMmTPH3njjDVu+fHk1zsJTEUAAAf8FCHj97yNqiAACCCRcQNttf/XVV25r5k8++cRtX5vwi3BCBBBAwBMBAl5POoJqIIAAAqkU0Pquubm5bom7mjVrspxdKvG5FgIIpFyAZclSTs4FEUAAgfQLNGjQwHr27GlLly61Nm3apL9C1AABBBBIogABbxJxOTUCCCDgs0DDhg2tXbt2PleRuiGAAAIJESClISGMnAQBBBBAAAEEEEDAVwECXl97hnohgAACCCCAAAIIJESAgDchjJwEAQQQQAABBBBAwFcBAl5fe4Z6IYAAAggggAACCCREgIA3IYycBAEEEEAAAQQQQMBXAQJeX3uGeiGAAAIIIIAAAggkRICANyGMnAQBBBBAAAEEEEDAVwECXl97hnohEEKBbdu2Ra11rMeCJ8VzjI6N97jgvHxHAAEEEMhuATaeyO7+p/UIJETg119/tSFDhti0adNs69atNmjQIDvrrLNs+vTpNmrUKFuzZo3NmzfPDjvsMOvWrZuNGDHC6tSpY7feeqvVq1fPvvrqK/vPf/7jjo98/scff2yPPPKIzZw501q3bu3ONXDgQJs/f77tvPPO1rhxY7v++uttzz33TEg7OAkCCCCAQGYKEPBmZr/SKgRSKvDqq69abm6uffDBB7Z8+XI7/fTT7eSTT7bNmzfbhAkT3Ncuu+xiXbp0cfV65513bPTo0fbMM8/YDjvsYOPGjbNmzZrZ1KlT7Zdffinx/PHjx9sbb7zhjlNwu/vuu9ujjz7qguu+ffvalVdemdK2cjEEEEAAgfAJEPCGr8+oMQLeCbz22muWn59vt99+u6tbXl6eKVBt0aKF7b333sUjsLp9/PHHu+BYo7IvvfSSe96qVavcyPCFF15o++yzjwXPVzCsn9u2bevO+95779lDDz1kNWvWtIMPPrj4fu9AqBACCCCAgFcCBLxedQeVQSCcAmvXri0RgHbs2NEFuStWrLAGDRoUNyonJ6f4Z93WCLBSG2rVqmVt2rSx+vXrW2FhoV122WUumP3555+tYcOGxc/XufScoCj9gYIAAggggEBFAkxaq0iIxxFAoEIBjdouXbrUevfubb169bLHHnvM1q9fX+HzNm7c6EZre/bs6VIhhg4das2bN3e5vQUFBWWef8wxx9iYMWNc0Pvhhx/a119/XeYY7kAAAQQQQKC0AAFvaRF+RgCBSguccsopphFbBaT62nfffe3AAw+s8DwKeHfaaScbMGCAbdmyxc477zx7+eWXrWnTpm5iWunVGM4991z74YcfrEePHm6SmyayaXSYggACCCCAQCyBnO1/UKKvIxTrmTyGQEgEVq5caZs2bYpaWwVMtWvXtnXr1kU9JtUPaPUB1UspAb4U5dVqYppSDqKVDRs2uGN0XDzl/vvvtwMOOMC6d+9uur3//vu7YFarOuhrjz32KHGaiRMnumBaE+DUp8rjVV6vVnrwpShFQ8F/PCPcqaqz3kDoV315o+apqkPp66jPioqKTG96fCmaOKk3XqtXr/alSi7NR2k8sX6Hpbqy+hRG/ab/o0HR7wf5URDwVSC+v0q+1p56IYCAVwIK9uItWo1BQaGCV01CU3C7ZMkSFzArQNNX6aI/+meffbb16dPHPvnkE/e9VatWpjc1FAQQQAABBKIJEPBGk+F+BBBIqoAmqCn9Ydddd3XX0QoOWotXo0aNGjUq99o6vlOnTm5JMt0+5JBDyj2OOxFAAAEEEIgUIIc3UoPbCCCQMgGlkey3334unUQXVT6uUgEWLFgQsw5a6uy4445z6Q86noIAAggggEBFAgS8FQnxOAIIpERA6RBKb/jmm2/iut73339v99xzj9usQk/Qig0PP/ywLV68OK7ncxACCCCAQPYIEPBmT1/TUgS8F9hrr73c8mbxTNDRVsZKa5g0aZJb0uyOO+6wI4880rQbW6yJdd4jUEEEEEAAgYQLkMObcFJO6JuAPvbWpKhopUaNGu6j9FjHRHtusu5XnSuqd7KuHe288pFVMp20q9qUKVPsu+++c6sxRKvLr7/+6ia16fjrrrvOtNOb8nmVB3z44Yfbp59+alrbN1VFqx9oFQRNtKvO60mT+HSuli1bJrzqvr2eVJ9kv54qi+jj/zsZ+eYk19KvJ/1MQcBnAQJen3uHuiVEQEtkacmcaCX4Y+LT0laqs/6A+FSnIOBN5h82tVfr8mq1Bk1Oi1Z0nOqhlR7uvvtu69+/vz3zzDPuuzakOOGEE1Jq9+9//9stZ3XVVVe5VSZUb72u4ikazb7ooovswQcfdIH6P//5T3vjjTfieWrcxwRvUnx6Pek1rjcJ8S5hF3djq3Gg+kz18clJyxOq/3xab7o8J3Y9rMYLj6emRICANyXMXCSdAhWtYenjOrz6g6J6acteX0o86/Amoq5atWHWrFnWc/sIbayg8YorrnA5u9qEYuedd7YLL7zQBb/nnHOONWnSJCV2CtgUeCto1etM/RWsw6u0injeHGjN1zfffNM9VyO8WgdW5wnOnQhTvZZ0Pp9eTwoqfVuHV6/xwD8R7ok4h1Yzqeh3WCKuU5lz6DUevN6D58UaVAiO4TsC6RSIbwginTXk2gggkFUCyuPVBhaalBaraILbX//6Vzv66KPdYdrAQnm92owiFUUjy926dXMbZWhN4KDMnDnT3delSxf73e9+Z/PmzXMP3XTTTaatkxWgH3rooS5Y1wNXX321C0T/8Ic/uOM0an366adb586d7aSTTmKNYafCPwgggED1BAh4q+fHsxFAIMECyl+tW7du3Ks1JPjycZ1u6tSp9sILL7h1g1966SVbuHChe55GUa+99lrTFsiPPvqoC3gvv/xye+KJJ9yxb7/9to0bN87Gjh3rdpZTcPz3v//dGjZsaM8++6w7h1aZuPHGG2369OmWn59vOj8FAQQQQKB6AqQ0VM+PZyOAQIIFlAaw5557uoBXaQ0+FuUJa0WIYDe4Xr16uWpqNFePKcVhzJgxLh9U982YMcM00W7fffd1wa0O1kjvRx99ZLvvvnuJJmoiXps2bdx9Gq2uaKS7xJP5AQEEEECgXAEC3nJZuBMBBNIpoLSGOXPmuI/zmzVrls6qlHttTdAJJoLpgGBnOOWlKr9xwIABLgdbWx5rxDrYKvmggw4q93yRd0ZOmFIOs0aNk120MoTWMT7wwANd/ZN9Pc6PAAIIpFqAlIZUi3M9BBCoUGCPPfZwAWW8m1BUeMIEH9C1a1ebMGGCW+9Xo7m6raJ6axRXK000btzY5e9+/vnn1q5dO9Pko48//titGbxu3Tq3YYbOowllOkcqAtvyGDRR7r333rOff/7ZrWmsiWQUBBBAINMEGOHNtB6lPQhkgICCwN12282lNWjyl2/liCOOcKkLSrnQSG9kWoKWJtOXJp9p++Qrr7zSbZesUV7l7GqdYAW/559/vgUjvlo/+Le//a3deeedKW+qgm2NWKuuWqFAAW/k6HXKK8QFEUAAgSQIEPAmAZVTIoBA9QWU1hCMokZ+zF/9M8d/Bo26RltaTIHsoEGD3NJpChZVfvzxR5s9e7adcsop1r17d9PKEdOmTbMvv/zSlJqx//77uxFgrS4RuYyTliXTMmZq52GHHebOtXHjRvfzDjvs4PJ49f2pp55yI8FnnHFGwtaK1Uh0hw4dbP78+a7OkfVyFeEfBBBAIAMECHgzoBNpAgKZKKCJayrffvutCxxT2UaNeg4ePNhtBKGR2b/85S/Wp08f++yzz0ybTCjVomPHjm7DCAXmWmlBKQHvvPOOKVDt16+f/elPf3LBqY7XpDWds0WLFta+ffsSwW7QrtJBvYJgjXIrr/bmm292O8kpP1jXU2qERpkTVTRCHTlKnajzch4EEEDAFwFyeH3pCeqBAAIlBLRU14477piW5cluvfVWVxcFsCNHjrTbb7/djd6effbZbp1fLRumAPW8885zG15oZ7R3333XBb7K2X3llVfc9sgTJ050aQxPP/20G51Vvmy8pVWrVm4JM51Lo79ab1hbJo8YMcIt2xbveTgOAQQQQMCMEV5eBQhkuMAll1ziVhHQR/O6rdxRTaJSrqZGDn3O19RoptajVW5pKreg1dJiSldQqoJ2ftO2v8qv1YYYmnCmj/9Vt9GjRztHWWqTiRNPPNGtcqDJa1r5QCPCuk+juiq9e/eO+9WmdXz/9re/ucD5vvvuc7vPaSMK7SqnoDdVG2zEXWEORAABBDwWYITX486haghUV0CTkZRXut9++9lZZ53lJidpBQEFuvp4XR+1+1wUVGoL0yVLlqS0mgp0g7xcXVjB9lFHHeVWXtCbBq3Bqw0iFOhqfd0GDRq4wFw7rilYVn1lLN/INxTB8mXxNEYpFdqMYtmyZXbqqae6Xd2USqERXwXCFAQQQACB+AUIeOO34kgEQieggGz48OFuxy5NlFIeqfJAgxJtQlbweLq/KyhXMLlgwYKUVuX3v/+9KYdWRastnHzyyS6HVvm82jVNeblacUHBrO7TagtKOfjggw/ckmSaYKYJaxoNDibeyT5YviyexrRu3dq0S5tGiIORY+3gpu2TmzdvHs8pqnSM3iQphYKCAAIIZJIAKQ2Z1Ju0BYFSAhqZ1MfxyoVVTqiW+1KKgJbNUlATLItV6mle/ahRXgW8GmFNVYCu1IF//vOfdswxx7hRXK3IIDutoKDUBq3eoJ81OW3RokXuTYSCX73BUCqCcnU1qq4RWaUelLd8mVfI/1cZ1V/bHyuFRGkYSs2gIIAAApkgQMCbCb1IGxCIIqAAMcg91ciuVhLQx/FXX311iY/sozzdi7u1Ru0XX3zhPtrXiG8yiwJV5d5qd7SLL77YjaRqcppGeZVHq+XF+vfv7wJdHauJanrspptuco+9/vrrboRXI7IaAVYAWd7yZbHaoK2INRqcjjcj3333nVtlQusEz507l4A3VkfxGAIIhEqAgDdU3UVlEaiaQJDGoPQALWcVmZ9atTOm7lmaNKbRVI3yJjvgVWB9yy232Msvv+wmoWkNXQWgF1xwgWmZNG0acfrpp5tWkNBIru5Trq1K27Zt3ZestTRZ5M5pdevWLQNW3kS8UaNG2UMPPWQaYU5HwKtAXxPylJeskXUKAgggkCkCBLyZ0pO0A4E4BDRypxHeMBWlZeijdaUHaDOHRJfIzSWUfqBgNyjacvf555+3pUuX2rXXXuvSKpS6oIl0ShNR2kJ5RW8otKJDtLJ27VqXaqLNKDSJUDm5Wn1BAfawYcPcCHLwXOXU1qhRdrpFZL2DY/U92v2Rx0S7rWBdq02sWbPGmjRpEu0w7kcAAQRCJ1D2t2jomkCFEUAg0wWU1rB8+XIXiCWqrVpFQdv5HnrooS59QUGs1rn94x//6EZolVbw5JNPulQFpSY89thjxSsuaMRZE9s0sSyy6JzaKOI3v/lN8Tk/+ugjt1ZvcJxGcR9++GE3Wnz//fe7CXDKBVYKhSa1Kd1EwfX48eNd7m+PHj2sc+fObk1enUP10rGHHHKIq0MwuW7WrFlu2TNtxaygVSPTVSkK6Al2qyLHcxBAwGcBAl6fe4e6IYCAEwgCS43yJqKsXLnSBbMawdWIroLI5557zrRBhAJHbTShlAON4Go7Xy0npiXCYpXgnFp7V4GzRtM1oU3pDatWrSp+qiYL6j4FsRrp1eoOmlioIDUY8c3Pz7d77rnHpTeojlpp46677nLnKG9TDNVfecSXXXaZGyW+9NJL7bbbbiu+JjcQQACBbBcgpSHbXwG0H4EQCCgHdpdddine0rc6VVZ+qrbmVT5z165drU2bNhZMjFOgumLFCjdhS6O4f/7zn13+cDzX0/JkSr3QahLHHXec2+BD5/7hhx/KPF3B9OzZs12grUBZucNKkVAqhJYz07bEAwcOtKlTp5omwinVQUucqZTeFEMjwZpopzWANbqsyXIqCpTVVgXeFAQQQCDbBQh4s/0VQPsRCImAJlEpiNNoZl5eXpVrrecqqL3mmmvcqhUakVWerpbh0kisAt2LLrrIBZk6NnLyWayLakWMsWPHumXfxo0b59bOvfvuu11dlRKhemvinTajUI6s8mV1buVUd+zY0bQ9sa6npc+Us6td2ZQaoZ8VBCvVQUVBceSkQ7VFK0ao3jpPUHRb+b8UBBBAAAEzUhp4FSCAQCgEFPAqgFu4cGG16qvAUOkDSiM45ZRTXB6vRme//fZbt/qCRmm7detWfA0dr2C1osBXS74pF1c5wUon0EoLGrlVEK2VD7RZhQJh3d5tt93cyg8KXDUqq+XNfvrpJzdxTSPP2qlNz1NqhTa90HMUNKuU3hRDawUr51aj1BoJV9vUhpEjR5bYZKS4QdxAAAEEslCAEd4s7HSajEAYBRSUKrBTYKolwKpTtLyZlv3S6KlGRzU6qyBXgahSDSKL1uFVMKkJbkonUABcXtE5dQ4tXaYRWp23T58+9vXXX5t2XlMusOqvUVutyBBsM6zd2XSMAlnlD6vss88+7ly6T0Gs6qp8X+X/lrcphoJkjVhrRFk5vsoR1o5s0epaXv25DwEEEMhkgZztoxbbMrmBtA0B5UhqhC5aUVCgkbYgRzLacam8v3Hjxi5Y0cfVvhR93K4lwtK57axGQufMmeNWPVBQqcldWlVAfVyZsmj7ZDKtd6vcWAWLZ5xxhum+nj17uslk5Z1L7VbwW17R6Kuer40pNAKt15JychWY77vvvm77YY3YKsiNXGJMr0tNlFPKggLq0kXnkbu+ShfVR8Fw6d3n9By1KbLozYJ+1SvX15ciS7kpOPelaNKg8qtXr17tS5VcDrZWEIn1OyzVldUyeuo3peYERa9R+VEQ8FWAEV5fe4Z6IYBAGQGlNWhrZE0E04YUVSmff/65+7hf+bTaOOKSSy5xwYR2GVNwGq2UDnaVXrF48WKXbqBzKQBQAK7AtV27di49IvJceqx0UZBw1llnRR2JLR24Rj6/dH2Cx2I9JziG7wgggEC2CRDwZluP014EQiygncA0qqnlySob8GqEUysaaGRXI8Jax3bQoEFu9FW5tUpneGz7WrsKfDXy+I9//MOlBWhUVjm12llNo6m6/dVXX7k0BKUt6HHl4Wo0V2kNmoym4/RYPIW0g3iUOAYBBBCongABb/X8eDYCCKRQQOkAWpNXAa9yZeMt+kj4mWeecWvpKhhVCsOAAQOKR1aVK6tUAC1Dps0mlJaglAGNviqt5I477rBXX33VfvzxRxfIahRVo7gaEVY+bumiXFqN+ipwVlCt/N0rrrjCrr/+erdWbnmjvaXPwc8IIIAAAokTIOBNnCVnQgCBFAgorWHu3Ln2yy+/uBSCii6pQFabOLz11lsux1ApDMrVLV2Ux63caRUFu7qGVnLQ5hMKXBXAdurUyY3kak3g0rmzwfmUa6mJaBotVuCsLYaDbYY16stSYYEU3xFAAIHUCRDwps6aKyGAQAIE9ti+5JZGejXKqxzcWEVLhd1www1ueTCNxmolg913373cpyi1QKPH2vRh6dKlbvKaJpppApNybU888US3ekK5T464U4FwEDgrwFUqhQJmFW0EQUEAAQQQSL0AAW/qzbkiAghUQ0DBZ6tWrVzAq215o5V58+a5AFfr2/bq1cuGDRtW7ioLy5cvd6sraIUFFY3o9u3b143kXnvttW5EVteMtyhw7tChg/397393ucLa8lc5v/fee2+5u67Fe16OQwABBBCougABb9XteCYCCKRJQGkNkyZNcqkHDRs2LFMLpS9oPVqNrCovVzm7kSkImrSmAFdfSo1QQKtzKtVBI8ha6qw6RdsCa8mmYELa6NGj3XJXF198cXVOy3MRQAABBKooQMBbRTiehkA2CSxbtsytUjB8+HAvmh0EvMqrDbbT1cQ0BaojRoxwk9OUVqD6aqMHlWDHMwW5moimNYWVEqGd0ZTKoJ8TWYJgV+dUsB35cyKvw7kQQAABBCoWSOxv+IqvxxEIJERAQYuWj9IkIu1iRUmugCZaaVT0kUcecSOhWmtWu4O98cYbttNOO7kteiM3VEhubcwtBabF75UqEAS8L7/8sgt0lX+rtASN8GqJMG0uoa15FbQrIFaQ27VrV9cOgtBk9xTnRwABBPwQIOD1ox+oRSUE7rzzTrfbVps2bey+++6zf//7324N1UqcgkOrIPDxxx/bpZde6pbXUjCpj+n/3//7fy43VRPBFESmsmiUV7uuaWcs5elqOTFtO6z8Xv2sYFwjuQrElaagkV49pzL5uKlsD9dCAAEEEEieQI3knZozI5B4AW3fOnXqVBs1apSbhPSHP/zBbc2a+CtxxtIC3bt3d6sd7L333m65LW1Xe/vtt7vR03SsPqB6KI3hpZdesjPPPNMFuUcddZQLajVh7aOPPnKB7oUXXmgnn3yyWzM3k4Jdrf6ggF//HyKXOtO6vxQEEEAAgZICjPCW9OAnzwU0gqddrYKPz5XSoF2yIssXX3xhb775ZvFdxx9/fMwRYJ1L+ZuRk5qKn5ymG/qoXfUqb0JWOqqktIAJEya4ncTUB507d7aHH37YrUagrXqVKpCMumrSmbbs1ZfWsg2+B7c/++wzF3QrdaFLly6m9XHfe+89FwDOnDnTbRKhNXiV/qAUGO3U9uyzz7p2JMsxyAUOvifrOgroldqjoF8rQWiEXYG9bCZPnuwm9J1++ummVSi0EYbqo8l7vmw9rNe4AnWf3oTodazfA8l4LVf1dSAnfWmdaF+KfjepTpFOkW+6fKkn9UAgUoCAN1KD294LaKerYI1TVVbbuiq3NLIohzMy4O3Ro4fL5Yw8JvK2/sDpKwiiIx9L123VRXVSIJeKotFCXa900Rq0j23fQGHs2LFWv359F0z95S9/cRs5qC9++OEHa9++ve2xPWWgvLpqEwYFYMFXZPAaeV/p20Fgq2AuWtEau6qDgmLtXKbr6zw9t6+0oJHP0047zd59912Xa6wlyc4++2wX8I0ZM8aGDh0a7bTVvj94PSU74NX/A71O1J4TTjjBBbhqt/KalcM8cuRItxucgt/AQrvNaeMNH4rqrtedvnwpqf5/F0+7fXTSa1yvb9UtKPq/TkHAZwECXp97h7qVEdAITLCIvx5U/mbdunVLHKdtYvUVFAXEGuWKVoLRE+3I5UtRMKN6KQc1kUVrw65du9ZefPFFu/766+2ggw6yyy+/3AoKCkwpCnfddZcLkpSicP7555tGb7V5gt5Y/O1vf3Nb7Gpkcfr06dakSRN3rt12281mzJhhyvENAtrge6xgJnDXyJWCVX3XaJ/OG/lz5OPBMRrFVz0UuE6ZMsVmzZrl3gjptaDVGPQa0QoOmqCmoPjYY491r4F99tnHbSoR6/VQXW/VXQGB3JJZNJqtXGUZa5vkIPjQm48WLVrY/vvv7ww0uq28d/W7npPMtlemvfXq1XP9pNeKL6VZs2bud4re6PlS9EZTb/x8Cij1mlK/aem9oOj/rvqUgoCvAgS8vvYM9SpXQL9o9VF1ULSG6s477xz8yPcKBPQHSoGgglP9gdKuYpdddpkLCP/zn//Ybbfd5lIVFPgqD1YbKGjEUIG3RgcVNM2ePdsFTgrqgp3I9MdPgZ4C9SAojQxUda3SPwcBWgVVLvGwgruJEye6XGJ9hH/ccce5VRruuOMO9wdYo75BAKXAWe3Vc7Qur3J+dVtfmVDkr4l4Gs1VmonenOj/h94URhalcLz++ut2//33u+A/8jFuI4AAAtkiQMCbLT2dIe3UcljKR1yyZIkLdF977TWXu5khzUtJM7TrmEayNKr7ySefuJzo4E2E8l81uqsRWy33pmOUm6cg6vDDD3eBlXKodYwCqCOOOMItDaePzJNdNJqvoFzLoQXX1TU1iqsJdU888YSrgtIbFExfd911Lr9bo796vShQ//TTT93I+dy5c93au6U/HahuGzR6+uijj7prB+f617/+ldQUCqVrvPDCCy43V32kNyUq7dq1c28yNIL/j3/8wxlp5JeCAAIIZKMAAW829nqI26yP1rUUlj5u10fwWg5LE3GSUTQSqEBQqQ7dunUr/sg4GddK5TllqFJYWOiCv2AdW92n2wpwlQurvNAdd9zR5UMrAH7++efdmwsFk8rfUy7o7373Oz0t6UUf52qdXeUMK11l3333LXFNBXrBCKdG/N9//32XrqFl6zTqrNeLglF9Ldq+0oeWLNMI6a677upGfjX6G7iUOHEcP3z//fduBFXpIdrAQp86aIk02Sl/XBP8kllU7/POO6/4EpqkqaKR36D07t3bjWzrDQwFAQQQyEaB/2acZ2PraXMoBfQxtj6iffDBB+3mm292wVcyGqKP/b/55hu3YYFGCTOtKJ9TgZ5GORW47rE991MTnZSaoKBJk//69+9vmumv2djKE9Xo6QcffOBSIDSqqI/Sk100mqw1f7VxRN++fcsEu7q+0iVOPfVUNyKtlRi0gsT//M//uAA5GPnVx/16vehcF1xwgfXs2dNV/Z133nGvJQWpH374YaVzXIcMGWJaDk25xEqhUOCtQPfee+91bxw0qSxTi940URBAAIEwCDDCG4Zeoo5lBDThSV/JLJosEuR7JnsCUjLbEevc11xzjUsR0UYSyn1V8CbXU045xW655RY75phj3MQiBXSDBw92ubyaEKbNPpTSoBH2yhY9X5POlFYR+XwFtBq1j1zdQCOSmmCnSTtKm4j1kbxSG/QxvgJPrcigEdZXX33VnnrqKTdarUA4KBoV1ZJ2+tJotgJ35TYrxUMBvUaF9WZAX3petHxjBXzKFdaGFppApxxi5RarXaqLcpwztSiwV6qI3jApTSaaUaa2n3YhgEC4BAh4w9Vf1DaFAprlrkBXM491OxOK8kkji9auffrpp13aRuT6rAo8FfAqGFQAGgSh2kZYwa5cFOBoZLWyRZPOdF6ti6vJbEpBUBqFRozlrPQEFQWPCnZ1DW0wotHlisqRRx7pzjt+/Hjr16+fG+VV0KtRVj2mtIPSRSPa+vhfXwqsFy9e7IJf5fkq51cBnQJaBb9akSLyjZZeG6q7JvVpMxTlmGtt3Ewf+VT/6RMAvSnUqLbSONjiu/Qri58RQMAnAQJen3qDungloIBOAWE2lMhgN7K9CgbLK9Wd7CVbBZcKnFQ0UqggWkGlyqLtebYKVDV6qjQGBVbxFNVXo42azKg1aRVAKy1DQbY2zlAQrcA3COBLn1PBrIJbfSmQVc6wRn71pUlvep7ydFVPjSjreldffbXbzOKQQw4pDoqfe+45N0lMrkFObelrhflntVt9o0BfrwW9QaIggAACPgsQ8PrcO9QNgQwUUH6r0g4UVCp4VNHKCwpqlT6g5dC0cYgmlJ144omV3olLa+3qSxsuKLVAQadylJUOofsUpCkIjdwlqjxmBeWqg756bs/31YS3IPgtPelNKRTBpLcrr7zS5QlrUpwCQ11LwbzSMyLTKsq7ZpjuO/TQQ12b1G5ZURBAAAGfBXK25yhmxqKUPitTt7QKaOOJWIu2a1RPH5tnw8YT1ekIpR9ohDPRH9frV5Amy2nUVKOGmkTWtm1blz9cek3Z0vVXmoOOKb3bnur42PYd4jQxT0FzULSigkaOVbQKhR6vSgnWM1YArI/21QblDKsNBxxwgDPSusUK6jXJUhMglSf90ksvVeVy1X6ORmBVR59WaWDjifi6VW8Ew7LxhPLyKQj4KsDbcl97hnohEFIBvbnQ9sPxFu2EpjcbmuClYFdLo2npMQWyCiyrUhRMaa1eBaQaMQ6KAlyt3qBJaUo70MSrqpRg0psm0g0cONAF5zqnJr098sgjboRaAbBygN9+++0Sl4h3jEGT+5TnHOvNWokT8wMCCCCAQFQBUhqi0vAAAghURUAf3ytntnRRoKeP+UuX+fPnu4li2lhCKxsoDeCVV15xu4LpXBdeeKFLd4j2/NLnC37WuTSBLEhtUBCsc2gymlIUlOM7adIkl9erVShK5/Uq2A7SFIJzlvdd9Q0mvWnrZgXwGtHVKLMm5KkOup4mdmmJN+1cp8lvWjot1oiYJt5p5QzlDqt+VZkgWF59uQ8BBBDIRgFGeLOx12kzAkkU0CQu7fallRVUlFqgLXA1AfDiiy92+ay6X5shKEBUQDtu3DgXgF5//fVukwiNirZv395NDPvzn//s0g+U+6tNR4Ktg3WOiooCRU0+CwJwBdxadkypDUcffbT70giw1uaNHE3WMmVa5zneFABNvtOazRpRnjZtmp155pmu/VrxQasXaPRXt2+88UabPn26W3EiVnqD2qiPsfVxtkZ4I+tWUZt5HAEEEECgrAABb1kT7kEAgWoIKIjVhLBnn33WBYza7eytt94ybVusUVRNSFPRCgj62F+5rQpuFQgqzWDq1KnuWK16oGXQtLqCzql0B42m3nHHHXHXThPWNLqqUVVNjFPRSGkQNGubZI26KqVC6/VqtQiVVq1aucly8W44ovNrVFq5xFrbV9fURLZjjz3Wbd2sTT00ka5NmzZugtfBBx/sgm53sXL+UR1lonrqudo0I9NK6bzrTGsf7UEAAb8ECHj96g9qg0BGCSi41cQyjZbeeeedLoDTLnlB0SirJpWdfPLJbjkxBYT6mF+pB8GmDdpGWIGpcnpPOukkt21w8Px4vmsSmVZrUOCskVgFk5F5sUo7UF6vJnZp++TPPvvMrbWr6yqdQLm0FRXV+9xzz3UT1rTxhIJ1pS1Erl6glIqg6H61MVZRoKtc5sitn2Mdn6zHNOquvii9hnN1r6dNTigIIIBAqgQIeFMlzXUQyEIBpTZoOTAFbfpSwKrAMChBjqxSDaKttRu5RrCOKy8PWOdbtH3tXqUvBOX99993o8LamljpE1pWTKkNWm0iGOHVsVqmTM/T1sQaVVXOr5YdU16ucoiVqlBRiVyLVkG+8pE1aqzRagXAYSyacDdixAj76aefXHDeuXPnKjdD9npjc8UVV7jd/GSqNxN6E0JBAAEEUiFAwJsKZa6BQBYJaJk3jaBqBFMjlJq01b17d7cWrgIdpS5UpmglBU0E0/mU66t0gPKK1tqNXFpOo6gzZ860hx56yI3aapkwjd4qHzZyhFc7yo0dO9aNIGvEWas7KG1CE9qUmhFvWkNQJ01WUx11nvPPP7/c3d2CY339rhznBx54wE2Wk6GKdquratEbCqWn3Hzzze4NkEaM9YZCOdYUBBBAIBUCrNKQCmWugUAGCmgEUOvOakJWZNFH97pf2+x++OGHblLaKaec4tIElE6gNXcrU5RyoNFXpTQot1b5vPEUBZ0dOnRwk8hmzJjhJoEpGFUwV3qbYuUTa31epTVoBFjLgSkwVqCtdXYVCCv4jTa6HFkf5QSfccYZ7no6l0q3bt1KrMGryXc+F62coY0llKqhNyuJKMq/Dko8jsGxfEcAAQQSIUDAmwhFzoFAFgro42gFjqUDXlHoY30Flxrt/eMf/+i+lFoQmbagj7SDMmDAgOCm+x75mHY6e+GFF9zobZACUeLgUj9o9Hbw4MFuDVwFsFoTWCOtmjyn0WWNMiu1QYG0RmGDovoqneHll192I8AajTzooINMwd/ChQtdEKxgPphkFzyvvO86TukQYS3a+EP5zBqh1xubRBTZX3XVVe51IVe9FnQNbQBCQQABBJItQEpDsoU5PwIZLKD81+uuu8618O6773aBYdDcyElaui8y2A2Oife70hPiCXZ1PgWkKu9sX9VBo8m33367Wx1Cwbd2PVO6glZQ0CS6oGjLX20ioWXFtFKErqXgLFhJQKskvPvuuzZo0CD705/+FDytzHcF1sccc0yZ+8N2h95kaCKfAlK9edDorLZIrk458sgj7dprr3XuGq3XGxBNVqQggAACqRAg4E2FMtdAIEMFtMasPvbXJDCNhLZu3TqhLVWep1YJqExRGoUCKaVPKHC+hoaYAAAjrElEQVTTBg5BakHfvn3dxLlzzjnHLYsWeV4F1QrwlN6gUWJNOFNRsKfd1PQxvHJOFQTrmPKKthJW8JwJRakMWgNZbxI0Ka86Aa8mHmo0XX0SWUpv9hH5GLcRQACBRAoQ8CZSk3MhkIUCmiymUdNkrEagdAktSVaZoqAqMrDSKHSwtFgw6lzesmBKQdBqDkrV0ONaTSJYOixydFqBLzmolemR/x3d1wgvBQEEEEiXAAFvuuS5LgIZIqDNFTQyqmWnfCjKOw02t9B2vkoxiCefVptORK43qx3YgkBZy5SpTJkyxeUtK/+XggACCCAQHgEmrYWnr6gpAl4JDBs2zNVHW+ZqZLRJkyZe1E9r/f7zn/90ga4C1iuvvNIi1/KNVknthKbVFLSihEZw77nnHjfSq9zf1157zbSsmZY90yYa2VbkqAl9SvVQqkN5ExWzzYT2IoBAuARytn9kF3u7n3C1h9oiUEZAOZeR666WPkABjT4Cj1zDtfQxqf5ZAYXqpY/jfSnKg1XOpVYziCxazUA5sVpnNdVFq0RoAlQwuSzy+qqnck8rm36g14Haqs0jlAKhndo0YU1tjyfQU86vrhnPqHJkfZN5W6tV6Fd9QUFBlS6j9Yv1xiZYWqxPnz5VOk/kk2SrQDpyE5DIx9NxW7v8qd9Xr16djsuXe02l02zevDnm77Byn5jEOzWJU/2mpfuCov8z8qMg4KsAKQ2+9gz1QiANAqVXWoinCto9K9HBrjZ9iCylf458LNptBVSVDXZ1Lo0G6493kO8bnD+eYDc4NtO+B9sk601Eovs606xoDwII+ClASoOf/UKtEEiJgFYiUJ6r1sjt37+/bdiwwW2z+/XXX7vtdRXc6CN+TeJKZRk9erRFTnKKd0myZNRRO7Wl8/rJaFNlz6nVONQf2iqa/OXK6nE8Agj4IJDav2I+tJg6IIBAsYCCOaUEKKUjcgc0BZwKgLWt7rRp04qPT9UNTSDTOrnatUxpHU899VSqLl3mOtqxTekl2V408k2wm+2vAtqPQHgFCHjD23fUHIFqCyjQ1SoL+vriiy+Kz6ecz3vvvdftSqbR31QXpQ9okwetAaugu3TecKrrw/UQQAABBMItQMAb7v6j9ghUS0ApDNqoQZszBOvdKrjUqK52UNNH2bEm/FXr4jGeHKQQaNKZJuxQEEAAAQQQqI4AAW919HguAiEX0AjvV199ZcuWLbMLL7zQtFOY0gmUt6vNJLRjGR9jh7yTqT4CCCCAgDFpjRcBAlksoFUMtKOYyqJFi9yqBhrRbdGihQ0dOjRtMqNGjXLX7tevn/uudX4pCCCAAAIIVFWAEd6qyvE8BDJAIHKi2tatW12LtCKDZuNTEEAAAQQQyBQBRngzpSdpBwJVEIhcp7Z169Zua11NUuvatWsVzsZTEEAAAQQQ8FOAgNfPfqFWCKRcQMFvp06dUn5d3y+oNwDPPfecaXcprRqhXbi01bAm+QUT/XxvA/VDAAEEsl2AgDfbXwFZ0H59RB9rHVVtGVvRMalmUn0UgMaqd6rrJCet2etTnVLhdNVVV9k527dOnj9/vs2dO9ceeOAB++tf/2qPP/6425Wtbdu2JbpCRr71XTCS71PfBU5BKk0JxDT9ICfffhfISdtC68unUtpJP1MQ8FmAgNfn3qFuCRHQHwwFa9GKflHrca1Y4EtRnfTH16c6yVH18qlOqXBat26dHXrooe5Lo70Kep9++mm3XJpWtCg9yhvrtZau15ecVHzqOzkpiAuC8XTZRF7XV6cgEI+sazpvy0m/DyJfT74F5On04dp+CkSPAvysL7VCoNICWsc11lqyGvXSL24FNr4U/THRHzmf6pSXl+feGKR6E4iVK1das2bNyu0aBU2ySqaTVqyYMWOGff755y6tQbnO559/vk2cONG0QUfpa9epU8f13fr168utczruVN8pICld13TUJbhmvXr1rKioyDZu3Bjclfbv+j2wZcsWr5zq16/v3lzF+h2WarhgfezI15NeYxQEfBbgMwife4e6IYCAXX311WlVGDZsmE2ZMsUFjIcffrgNHz7cxowZ41I72rdvn9a6ZdrFtbteQUGBBcvSZVr7aA8CCKRPgBHe9NlzZQQQKEfgX//6l61Zs8ZthqG1gOfMmWOTJk2yI488spyjk3+Xdn0777zzii/UsmVLu/jii4t/5kbiBJQyolFW3kgkzpQzIYDA/wowwssrAQEEvBJQXuyuu+5qAwcONKUPtGvXLm3BrlcwWVIZfUw+bty4LGktzUQAgVQJEPCmSprrIIBAXAIKdA844ADTSK+2PaYggAACCCBQXQEC3uoK8nwEEEiogNa4nTp1qmlijNa+1eSmF198MaHX4GQIIIAAAtklQA5vdvU3rUXAewHl7W7YsMG02oHKnXfe6WbOe19xKlhtgeOPP960KkG68rWr3QBOgAAC3goQ8HrbNVQMgewVCILdQMDHtW2DuvE9cQIKeFUIeBNnypkQQOB/BUhp4JWAAAIIIIAAAgggkNECBLwZ3b00DgEEEEAAAQQQQICAl9cAAggggAACCCCAQEYLEPBmdPfSOAQQQAABBBBAAAECXl4DCCCAAAIIIIAAAhktQMCb0d1L4xBAAAEEEEAAAQQIeHkNIIAAAggggAACCGS0AAFvRncvjUPgvwKrV6+2rVu3/veODLl1wQUXZEhLaAYCCCCAQLIECHiTJct5EfBIYPbs2fbOO+/YW2+9ZZs2bfKoZtWvyq+//upO8v3339ujjz5qS5curf5JOQMCCCCAQEYJEPBmVHfSGATKF/juu+/clq0Kdn/88cfyDwrxvWrX8OHDrVevXnbTTTdlXFAf4q6h6ggggIAXAgS8XnQDlUAguQJt2rSxwsJCy8vLs1atWiX3Ymk4++eff24FBQVuhLd27dq2cOHCNNSCSyKAAAII+CqQ62vFqBcCCCROYJ999rGddtrJjfLWqJF573Pbt29v+fn5NmjQIHvooYesRYsWicPjTAgggAACoRfIvL98oe8SGoBAcgQaNWpkNWvWTM7J03jWfv36Wb169Wzw4MEu2P3tb3/rgt80VikUly4qKrL333/f3nzzTVJAEthjyimXLQUBBPwSYITXr/6gNgggUEkBBbwrV660du3aua9KPj1rD//www9txYoVlpuba7p9+OGHZ61Fohq+ZMkSU3qNTI844girW7duok7NeRBAoJoCjPBWE5CnI4AAAmEU2HXXXd0ydZrwp9H/TCtagu+bb76xtWvXpqxpWg1FOeQa4f32229Tdl0uhAACFQswwluxEUcggAACGSewxx57WK1atWzz5s2m25lWJkyYYEov0Ghrjx49rHHjxklv4v777188wrvXXnsl/XpcAAEE4hcg4I3fiiMRQACBjBLYZZddMqo9kY3ZsmWLm6SpEd5ffvklJQHvbrvtZk2aNHGjvFoRhYIAAv4IkNLgT19QEwQQQACBBAlo8qKCXi3D17p16wSdteLTNGzY0C3/V/GRHIEAAqkUYIQ3ldpcCwEEEEAgJQLNmze3Y489NiXX4iIIIOC/ACO8/vcRNUQAAQQQQAABBBCohgABbzXweCoCCCCAAAIIIICA/wIEvP73ETVEAAEEEEAAAQQQqIYAAW818HgqAggggAACCCCAgP8CBLz+9xE1RAABBBBAAAEEEKiGAAFvNfB4anoF5s+fb4sWLUpvJbg6AggggAACCHgvQMDrfRdRwfIEFi5caJdffrnNmzevvIe5DwEEEIgpoK2HtctcZJk8ebJ98cUX9uCDD1phYaFt27bNpkyZYi+++KJt3Lgx8lBuI4BAyAQIeEPWYVTXbOzYsTZo0KCU7JyENwIIZJ6Agt2JEyfa22+/bcuWLStu4H333Wdff/21NWrUyJ555hl74YUXbOnSpaZtgm+77bbi47iBAALhE2DjifD1WdbXuG7duvbwww/bPffcYzk5OWU8pk+fbq+88krx/f3793d/sIrvKHWjRo0apq+aNWuWeiR9P2pbUtWpcePG6atEqSsHTrVq1Sr1SPp+VF30GvDJKXgd+bS1bG5urhut9MlJdVLgWadOnZS/gGbNmuVGcBXYzpgxw0499VRXB9Xp9NNPt5kzZ9rLL7/sPkFq2rSpLV68OG1+qpNeS/q950vR7wLVKfL1VFRU5Ev1qAcC5QoQ8JbLwp0+C/Tu3bu4evrIsXT55Zdf3B+s4P4TTjjBYgVpCpiCr+A56f6uPyiqU6x6p7qOgZG++1LkpOKjU1A3H6yCvvPJST76Ku//cLLN9txzT5szZ46tWbPGdDtw0ZsVBZj60u1DDz3UGjRoYD169LCnn366+Lhk1y/y/Ol0iqxH5G29nlSvwE2PpaMfI+vEbQQqEiDgrUiIx9MqMH78eNPkNJVOnTpZ9+7dK6yPAuLIoHjlypW2YsWKqM/TL+3atWvbunXroh6T6gc0cqJ6xap3quukER0FAspt9KXk5+e7wER97EvRiKUCgvXr1/tSJdMopQKSgoICb+pUr14906hgunJjjzjiCFu9erW1aNGi+P9Z3759bcOGDe513qFDB/vNb35jb775po0cOdJOO+204uNSiVi/fn2Xa7xp06ZUXjbmtbRts/pNbxiCot8PqisFAV8FCHh97Rnq5QT0RzH42CwdH33SDQggkJkC+n1S+ndKv379bMuWLdasWTPr1q2ba/jxxx+fmQC0CoEsEyDgzbIOD1tzNaIbz6hu2NpFfRFAAAEEEEAgdQKs0pA6a66EAAIIIIAAAgggkAYBRnjTgM4lEyMwfPjwxJyIsyCAAAIIIIBARgswwpvR3UvjEEAAAQQQQAABBAh4eQ0ggAACCCCAAAIIZLQAAW9Gdy+NQwABBBBAAAEEECDg5TWAAAIIIIAAAgggkNECBLwZ3b00DgEEEEAAAQQQQICAl9cAAggggAACCCCAQEYLEPBmdPfSOAQQQAABBBBAAAECXl4DCCCAAAIIIIAAAhktQMCb0d1L4xBAAAEEEEAAAQQIeHkNIIAAAggggAACCGS0AAFvRncvjUMAAQQQQAABBBAg4OU1gAACCCCAAAIIIJDRAgS8Gd29NA4BBBBAAAEEEECAgJfXAAIIIIAAAggggEBGCxDwZnT30jgEEEAAAQQQQAABAl5eAwgggAACCCCAAAIZLUDAm9HdS+MQQAABBBBAAAEECHh5DSCAAAIIIIAAAghktAABb0Z3L41DAAEEEEAAAQQQIODlNYAAAggggAACCCCQ0QIEvBndvTQOAQQQQAABBBBAgICX1wACCCCAAAIIIIBARgsQ8GZ099I4BBBAAAEEEEAAAQJeXgMIIIAAAggggAACGS1AwJvR3UvjEEAAAQRSLbB27VobP368ff3116m+NNdDAIEoAgS8UWC4GwEEEEAAgcoKFBUV2dSpU23z5s02e/ZsW7FiRWVPwfEIIJAEAQLeJKBySgQQQACB7BTIyckxfW3ZssVq1KhheXl52QlBqxHwTICA17MOoToIIIAAAuEVUJDbq1cva968uR188MHWqFGj8DaGmiOQQQK5GdQWmoIAAggggEDaBTSq261bt7TXgwoggMB/BRjh/a8FtxBAAAEEEEAAAQQyUIAR3gzsVJpUUkAfMdauXbvknRE/1axZ0/QV65iIw1NyU3VWHqBPdapVq5bLSfSpTuo3H530IvHJSa+nbdu2eVWn3Nxc93pKyX+oOC8iJ99+F8hJRa9zX4rqUtrJp/r54kQ9/BIg4PWrP6hNEgT0RyxW0S/u4A9drONS+ZjqE/xRSeV1Y11LdfLNSUY+OslRryufCk7x9YZvTqqPb//vJOmbU3y9y1HZLEDAm829nyVt12zpTZs2RW2tRi41GldYWBj1mFQ/oDrpD4pPdVJeokabfKuTAkuf6lSnTh3Xd+vXr0/1yybq9VQnjfD65KTKagmvjRs3Rq13qh+oW7euW13BJyf9HtASZ7F+h6XaqX79+mWcWI0i1b3A9SorEHvoq7Jn43gEEEAAAQQQQAABBDwTIOD1rEOoDgIIIIAAAggggEBiBQh4E+vJ2RBAAAEEEEAAAQQ8EyCH17MOoToIpFJAuYo333yzyzlt27atHX300XbhhRdamzZtbM2aNXbAAQfYtGnT7IEHHmDHqFR2DNdCAAEEEEioACO8CeXkZAiES2DixIluMlPHjh3t1VdfdZNjdtllFxcEL1u2zM4//3y3W9ScOXPC1TBqiwACCCCAQIQAAW8EBjcRyDYBrSTQsmVLa926tZ1yyim2detWa9y4sWNo2rSpG9XVKgha6YKCAAIIIIBAWAUIeMPac9QbgQQI9OnTxxYsWGDjxo2zuXPnmpZDoyCAQPYJfPfdd/btt99mX8NpcdYI5Gxfm3Fb1rSWhmalwMqVK2OuYRmsw7tu3TpvfDTKqnqtWLEiJXXSOp8VBbs+rsObn5/vNnhQH/tSfFyHV6P1+lVfUFDgC5PVq1fPu3V4mzVr5j7NWL16tTdOWvM22evwzp4927788kvX5gMPPNDl8McCaN68uVs/WXn+QdHvB/lREPBVgBFeX3uGeiGQQoGKgt0UVoVLIYBAigX0Zl+/A7SxzOLFi1N8dS6HQGoECHhT48xVEEAAAQQQ8FKgc+fOpk8mdthhBzv88MO9rCOVQqC6AixLVl1Bno8AAggggECIBTS627t37xC3gKojULEAI7wVG3EEAggggAACCCCAQIgFCHhD3HlUHQEEEEAAAQQQQKBiAQLeio04AgEEEEAAAQQQQCDEAgS8Ie48qo4AAggggAACCCBQsQABb8VGHIEAAggggAACCCAQYgEC3hB3HlVHAAEEEEAAAQQQqFiAgLdiI45AAAEEEEAAAQQQCLEAAW+IO4+qI4AAAggggAACCFQsQMBbsRFHIIAAAggggAACCIRYgIA3xJ1H1RFAAAEEEEAAAQQqFiDgrdiIIxBAAAEEEEAAAQRCLEDAG+LOo+oIIIAAAggggAACFQsQ8FZsxBEIIIAAAggggAACIRYg4A1x51F1BBBAAAEEEEAAgYoFCHgrNuIIBBBAAAEEEEAAgRAL5Ia47lQdAQSqKbBx40Z76qmnbNu2bXbGGWdY3bp17cUXXzTdf/LJJ1u9evWqeQWejgACCCCAQPoFGOFNfx9QAwTSJvDSSy9ZnTp17IADDrCPP/7YHnzwQSssLLS99trL3njjjbTViwsjgAACCCCQSAEC3kRqci4EQiZw9NFH26effmojRoxwo7szZ860vn372sEHH2z9+vULWWuoLgIIIIAAAuULkNJQvgv3IpAVArNmzbKTTjrJdt55Zxf0KtCdNGmSG+F99913beDAgVnhQCMRQAABBDJbgBHezO5fWodATIFu3brZN998Y6+88or97W9/s/79+1tRUZF98MEHNmDAgJjP5UEEEEAAAQTCIsAIb1h6inqWEFiwYIEtWbLEffSuiVaUqgkof/fcc88t8WRNVqMggAACCCCQSQKM8GZSb2ZJWwYPHmwjR460L7/80s455xz75JNPsqTlNBMBBBBAAAEEqiLACG9V1HhO2gRmz55ty5cvtyeffNLVoW3btjZ69Gjr0qVL2urEhRFAAAEEEEDAbwECXr/7h9qVEthvv/1s1KhRxfeuXr3aNmzYUPyzbij/dMyYMcX36SN7BcbRSo0aNUxfubn+/HeoVauWq1N+fn60aqf8/sApLy8v5deOdsGgLj451axZ01W3du3a0aqd8vuD17ZPTqqT1n/2KSVJddLr3DcnvZa2bt2a8tdNtAvKSHWKdFLuPwUBnwX8+QvvsxJ180ZAv2iDP5DLli1zI71DhgwpUT9tmrBixYri+7Zs2eL+iBXfUeqGzpmTkxPzmFJPSfqPqo+K6paMsmnTJnfuIBCK5xo+OqnePvad6qRgzpcS1CdZr6eqtDPZr/Gq1EnP8fH15FO/Ba6lnXwKyIM68h2BSIGc7b+U/fmtHFkzbiOwXWD8+PE2f/58Z9GpUyfr3r27u71w4UIbNmyYW0nguOOOi2m1cuVKU4AXrWg0VaMV69ati3ZIyu9v3LixqV6RgXuiKrF27VqbMmWKO12PHj2sYcOGcZ1ao6kKkLUxhS9FI0waUVUf+1I0EVDBwPr1632pkjVt2tQF4AUFBd7USbv4aVRQb1B9Kc2aNTO9QdYnR76U+vXr2+bNm2P+Dkt1XZs3b+76bc2aNcWX1u8H+VEQ8FUgOcNHvraWeoVOQH8UFfzpS4GEyrx582zQoEF20UUXWUXBbuganIIKT5s2rfgqn3/+efFtbiCAAAIIIJCpAqQ0ZGrPZki7NKIbjOqqSRrxHDp0qN1www3WsWPHDGllapuhbYQ/+ugjd9F99903tRfnaggggAACCKRBgIA3DehcsuoCzz33nK1atcouvfTS4pPo41ptnECJT2DHHXe0Xr16uUkw8aYzxHdmjkIAAQQQQMBPAQJeP/uFWkUR0Fa3bHcbBacSdysvkIIAAggggEC2CJDDmy09TTsRQAABBBBAAIEsFSDgzdKOp9kIIIAAAggggEC2CBDwZktP004EEEAAAQQQQCBLBQh4s7TjaTYCCCCAAAIIIJAtAgS82dLTtBMBBBBAAAEEEMhSAQLeLO14mo0AAggggAACCGSLAAFvtvQ07UQAAQQQQAABBLJUgIA3SzueZiOAAAIIIIAAAtkiQMCbLT1NOxFAAAEEEEAAgSwVIODN0o6n2QgggAACCCCAQLYIEPBmS0/TTgQQQAABBBBAIEsFCHiztONpNgIIIIAAAgggkC0CBLzZ0tO0EwEEEEAAAQQQyFIBAt4s7XiajQACCCCAAAIIZItAzrbtJVsaSzsRCIvAtddeazNnzrRXXnklLFVOSz0HDRpkK1assCeffDIt1w/LRS+44ALLy8uz++67LyxVTks9+/fvb3vssYfdcsstabl+WC567LHH2iGHHGJXX311WKpMPREwRnh5ESDgocDWrVtNX5TYAjIqKiqKfRCPOiOcKn4hyIgxoIqd+P1UsRFH+CdAwOtfn1AjBBBAAAEEEEAAgQQK5CbwXJwKAQQSJNC+fXtr2LBhgs6Wuafp0KGDrV27NnMbmKCW/fa3v7XcXH7dV8TZrVs3a9GiRUWHZf3jhx12mLVt2zbrHQAIlwA5vOHqL2qLAAIIIIAAAgggUEkBUhoqCcbhCCCAAAIIIIAAAuESIOANV39R2ywUGD9+vG3ZsiULWx67yRs3brT333/fPvjgA9u8eXPsg3nUeB3FfhEUFBTYhAkT7Mcff4x9YJY/umzZMnvzzTdt4cKFWS5B88MmQMAbth6jvlkl8OKLL9oNN9zASgSlen39+vV2zjnn2OTJk+2pp56yoUOHMru+lFHkj7yOIjXK3h47dqxdcsklLogbPny43XXXXWUP4h57/fXXbciQIbZ06VK3JNm4ceNQQSA0AsxiCE1XUdFsEtCIrtbiXblyZTY1O+62jhkzxrp27WqXXXaZe86f/vQn+/jjj+3ggw+O+xzZcCCvo4p7WUuRaR3nW2+91Vq3bm1nnnmmnXbaaXb22WdbkyZNKj5Blhyh5drefvtt0xsCrVV80EEH2b///W/7/e9/nyUCNDPsAozwhr0HqX9GCuiPsII3Ngoov3sXLFhgnTp1Kn5Qt+fOnVv8Mzf+V4DXUcWvhJo1a9rjjz/ugl0dvWHDBlu3bh2fqpSiy8nJsXvuuccFu0ohUjqRAl8KAmERYIQ3LD1FPbNKoHbt2nbCCSdkVZsr09iffvrJGjVqVPwU3dbHrJSSAryOSnpE+6l+/fruIW2ocPfdd1ufPn1shx12iHZ4Vt+/Zs0aNwqutKIHH3wwqy1ofLgECHjD1V/UNkMFRowYUTwxTR+nshZo7I7WqJxGL4Oij+7r1q0b/Mh3BCotoEmQypfXR/dXXXVVpZ+fLU/Qm8tXX33VpkyZYkolUv4z//eypffD3U5SGsLdf9Q+QwQaN25swZeCOUpsAY2+/fLLL8UH6XbLli2Lf+YGApURKCwstL/+9a9us5cbb7zR8vLyKvP0rDh206ZN9tFHH7m2Kr3h8MMPt/z8fJszZ05WtJ9Ghl+AEd7w9yEtyACBM844IwNakbomaKenN954w/Rd+ZZamuzmm29OXQW4UkYJXHfddW7nsIsvvjij2pXIxtSqVcseeOABq1GjhnXp0sW+/PJLN6l29913T+RlOBcCSRMg4E0aLSdGAIFkCRx11FFu0swf/vAH9wf49NNPL550lKxrct7MFJg3b54budTo5XPPPVfcyHvvvdcOPPDA4p+z/YZGdbUqioLeUaNGmfLDtWJD8+bNs52G9odEgK2FQ9JRVBMBBMoKrF271uUP5uby3r2sDvcgkBwBfarSoEGD5JycsyKQJAEC3iTBcloEEEAAAQQQQAABPwSYtOZHP1ALBBBAAAEEEEAAgSQJEPAmCZbTIoAAAggggAACCPghQMDrRz9QCwQQQAABBBBAAIEkCRDwJgmW0yKAAAIIIIAAAgj4IcDUZj/6gVoggECGCGgHuOnTp9s777xjc+fOtd12283OPvts23vvvcu0UNu0ascqHbtq1Srr0KGDnXvuuRZsdRv5hF9//dVuu+0269Spkx1//PGRD5W4revfdNNNNnDgQGvatGmJx/gBAQQQyFYBVmnI1p6n3QggkHCBzZs3W79+/ezNN9+07t27u80MJkyYYIsWLbLx48fbEUccUXzNr7/+2nr06GHawep3v/ud2yr5rbfeMu0iN2PGjDLLPj3++OOmjRG0tet3331n0ZZiGzx4sN1555327bffsjZxsTY3EEAg2wVIacj2VwDtRwCBhAmcddZZ9sknn7gAd/LkyW6R/gULFph20vvjH/9oGqVVWbp0qfXq1cvtFLds2TJ75plnbMyYMe55OuaKK64oU6eHH37YLr/8cneOsWPHlnl8yZIl9vvf/95GjBhR5jHuQAABBLJdgIA3218BtB8BBBIioFFc7dT14IMPWsuWLUucUyOuRx99tAto9cCzzz7rRnZ1bM2aNYuPzc/Pt4ceesiN7mrkNygaDZ46daqdfPLJ7qu8oFYB9datW+21114LnsZ3BBBAAIH/EyClgZcCAgggkACB0aNHm0Z4tQtVnTp1Yp6xb9++tm3bNnv55ZdjHhc8eOWVV7pAdtasWaaRY40Oz58/3/bZZ5/gEFu8eLHLF9b97dq1I6WhWIYbCCCAgBkjvLwKEEAAgQQIfPbZZ9aqVasKg11dSse2adMmrqtqEpryd8855xx3fM+ePW333Xe3kSNHlni+JsdREEAAAQTKFyDgLd+FexFAAIFKCSiNQfm4SiuoqOyyyy72008/VXSYe1wT4H744QeX5qDc3VdffdXat29vjz32mG3YsCGuc3AQAgggkO0CBLzZ/gqg/QggkBABLRdWWFhYnKcbeVKlLwwdOtQmTZrk7taxc+bMiTyk+LZSEoYMGWLff/+9u++RRx6xZs2a2X333WfXXHON+1K+cEFBgZvoVvxEbiCAAAIIRBUg4I1KwwMIIIBA/AIKYps3b2433nhjmSdpFYZbb73Vli9f7h7TMmRaeuz1118vc+ywYcPcSgsNGzZ0x2sS2l133WUzZ84s8aUlzh544IEyz+cOBBBAAIGyAmw8UdaEexBAAIFKCzRo0MCef/55O+qoo1z6wYABA6xFixb2wgsvuGD3sMMOM01WUznuuOPskksusTPPPNNtJqEAWGkLTz/9tEtZuP322916u1rdIS8vz63MULpC5513npskp8BZG1ZQEEAAAQSiC7BKQ3QbHkEAAQQqLaAAV0uLvf/++27Fhlq1arkJZ7fccos1adKk+HyajKYUBW02oaBVub8KkK+77jr785//7I5Trm7Hjh3tiSeeKH5ecEP5uzvvvLOddtppJSawsUpDIMR3BBBA4L8CBLz/teAWAgggkDCBLVu2mDaD2HXXXU1Bb6yibYWV/1t6/d5Yz+ExBBBAAIH4BQh447fiSAQQQAABBBBAAIEQCjBpLYSdRpURQAABBBBAAAEE4hcg4I3fiiMRQAABBBBAAAEEQihAwBvCTqPKCCCAAAIIIIAAAvELEPDGb8WRCCCAAAIIIIAAAiEUIOANYadRZQQQQAABBBBAAIH4BQh447fiSAQQQAABBBBAAIEQChDwhrDTqDICCCCAAAIIIIBA/AIEvPFbcSQCCCCAAAIIIIBACAX+Px2MpbLYBSVqAAAAAElFTkSuQmCC\" />\n\n<!-- rnb-plot-end -->\n"} -->


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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Combine panels


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNEVThMWEF4WVN0d01tRXJjRE5oSzNBMFlWeHVZR0JnWEc1Z1lHQnlYRzV3TlR3dGNEVW1kR2hsYldWZmNXc29LU3QwYUdWdFpTaHNaV2RsYm1RdWNHOXphWFJwYjI0OVhGeHViMjVsWEZ3cFhHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcbnA1PC1wMWErcDJhK3AzYStwNGFcbmBgYFxuYGBgclxucDU8LXA1JnRoZW1lX3FrKCkrdGhlbWUobGVnZW5kLnBvc2l0aW9uPVxcbm9uZVxcKVxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucDU8LXAxYStwMmErcDNhK3A0YVxuYGBgXG5gYGByXG5wNTwtcDUmdGhlbWVfcWsoKSt0aGVtZShsZWdlbmQucG9zaXRpb249XFxub25lXFwpXG5gYGBcbmBgYCJ9 -->

```r
```r
p5<-p1a+p2a+p3a+p4a
p5<-p5&theme_qk()+theme(legend.position=\none\)

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\" />\n\n<!-- rnb-plot-end -->\n"} -->


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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dUkyZG5jMkYyWlNoY1hDNHVMMGxzYkhWemRISmhkRzl5TDFGTElFWXhOVjh3TlM1d1pHWmNYQ3h3Ykc5MElEMGdjRFVzSUdobGFXZG9kQ0E5SUhCb2FDeDNhV1IwYUNBOUlIQjNkeXdnZFc1cGRITTlKMk50SnlsY2JtQmdZRnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbmBgYHJcbiNnZ3NhdmUoXFwuLi9JbGx1c3RyYXRvci9RSyBGMTVfMDUucGRmXFwscGxvdCA9IHA1LCBoZWlnaHQgPSBwaGgsd2lkdGggPSBwd3csIHVuaXRzPSdjbScpXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuI2dnc2F2ZShcXC4uL0lsbHVzdHJhdG9yL1FLIEYxNV8wNS5wZGZcXCxwbG90ID0gcDUsIGhlaWdodCA9IHBoaCx3aWR0aCA9IHB3dywgdW5pdHM9J2NtJylcbmBgYFxuYGBgIn0= -->

```r
```r
#ggsave(\../Illustrator/QK F15_05.pdf\,plot = p5, height = phh,width = pww, units='cm')

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Colour version of LH panels


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
```r
p1b<-ggplot()+
  geom_point(aes(x=RDA1,y=RDA2, color=lemminvert$manag), data=filter(l1rt, Score==\sites\), 
             alpha=0.7, size=1)+
  scale_color_viridis_d(
                     name=\Management\,
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), size=2, data=filter(l1rt, Score==\species\), 
                  )+
  geom_segment(aes(x=0,y=0,xend=RDA1,yend=RDA2), arrow=arrow(length=unit(0.2,\cm\)),
               data=filter(l1rt, Score==\centroids\))+
  geom_text_repel(aes(x=RDA1,y=RDA2, label=Label), size=3, data=filter(l1rt, Score==\centroids\), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position=\none\)
p3b<-ggplot()+
  geom_point(aes(x=CCA1,y=CCA2, color=lemminvert$manag), data=filter(l1ct, Score==\sites\), alpha=0.7, size=1)+
  scale_color_viridis_d(
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), size=2, data=filter(l1ct, Score==\species\), 
                  )+
  geom_segment(aes(x=0,y=0,xend=CCA1,yend=CCA2), arrow=arrow(length=unit(0.2,\cm\)),
               data=filter(l1ct, Score==\centroids\))+
  geom_text_repel(aes(x=CCA1,y=CCA2, label=Label), size=3, data=filter(l1ct, Score==\centroids\), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position=\none\)

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
```r
p5a<-p1b+p2a+p3b+p4a
p5a

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<div data-pagedtable="false">
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<!-- rnb-frame-end -->


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\" 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" />

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```r
```r
p5a<-p5a&theme_qk()+theme(legend.position=\none\)
#ggsave(\QK F15_05c.pdf\,plot = p5a, height = phh,width = pww, units='cm')

```

---
title: "Q & K Box 15.3"
output:
  html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

We described the study by Lemmens et al. (2015) who sampled various biotic communities in artificial ponds in Belgium that were classified into one of four management types in Box 15.2. In addition to the biotic sampling, they recorded 15 environmental variables (e.g. depth, salinity, pH etc.) for each pond and covers of submerged, floating, and emergent vegetation. They used RDA to comprehensively examine the patterns in the various biotic communities (ordination) constrained by either management variables, drainage strategy, environmental variables, or the fish community. We will focus on the macroinvertebrate community (abundances of families) and do two RDAs, one constrained by the four management categories and a second constrained by the continuous environmental variables; we did not include management type with the environmental variables in one analysis as some of the predictors varied with management type (i.e. collinearity). We will use a covariance matrix for the invertebrate data and a Hellinger transformation that converts raw abundances to proportional abundances. This provides a comparison to the CCA we will do as a second analysis below.

The paper is [here](https://doi.org/10.1371/journal.pone.0139371) and the pond subset for this example is [lemminvert2.csv](../data/lemminvert2.csv) and we also use the environmental variables [lemmenv.csv](../data/lemmenv.csv)

Lemmens, P., Mergeay, J., Van Wichelen, J., De Meester, L. & Declerck, S. A. (2015). The impact of conservation management on the community composition of multiple organism groups in eutrophic interconnected man-made ponds. *PLoS One*, 10, e0139371.

### Preliminaries

Load required packages (vegan) plus tidyverse, etc

```{r results='hide'}
# packages: vegan
devtools::source_url("https://raw.githubusercontent.com/mjkeough/mjkeough.github.io/refs/heads/main/R/libraries.R")   #This loads a set of packages used often
library(vegan)
```

Read in data files

```{r}
lemminvert <- read_csv("../data/lemminvert2.csv")
lemmenv <- read_csv("../data/lemmenv.csv")
```

## Do RDA on management first

Set no management as reference Do Hellinger transformation

```{r}
lemmenv$manag <- factor(lemmenv$manag,levels=c("nm","nf","yf","li"))
lemminvert1 <- lemminvert[,-(1:3)]
lemminvert1h <- decostand(lemminvert1, method = "hellinger")
lemmens1.rda <- rda(lemminvert1h~manag, scale=FALSE, data=lemmenv)
summary(lemmens1.rda)
RsquareAdj(lemmens1.rda)
coef(lemmens1.rda)
anova.cca(lemmens1.rda, permutations=1000)
anova.cca(lemmens1.rda, permutations=1000, by="margin")
anova.cca(lemmens1.rda, permutations=1000, by="axis")
```

Do triplot; category scaling type = 2 probably the best plots to display

```{r}
ordiplot(lemmens1.rda,scaling=1,type="text")
ordiplot(lemmens1.rda,scaling=2,type="text")
```

## Do RDA on env variables

Preliminary look at scatterplot; look for variables with unusual distributions and check for correlations

```{r}
scatterplotMatrix(~depth+silt+ph+temp+surface+cond+chla+tn+tp+susps+subm+reed+emerg,data=lemmenv,diagonal=list(method='boxplot'))
cor(lemmenv[,c('depth','silt','ph','o2','temp','surface','trans','cond','chla','tn','tp','susps','subm','reed','emerg')])
# transform chla and susps
lemmenv$lchla <- log10(lemmenv$chla)
lemmenv$lsusps <- log10(lemmenv$susps)
scatterplotMatrix(~depth+silt+ph+o2+temp+surface+trans+cond+lchla+tn+tp+lsusps+subm+reed+emerg,data=lemmenv,diagonal=list(method='boxplot'))
cor(lemmenv[,c('depth','silt','ph','o2','temp','surface','trans','cond','lchla','tn','tp','lsusps','subm','reed','emerg')])
# exclude o2, trans, susps and subm
# standardize remaining env variables
lemmenv$depth <- scale(lemmenv$depth)
lemmenv$silt <- scale(lemmenv$silt)
lemmenv$ph <- scale(lemmenv$ph)
lemmenv$temp <- scale(lemmenv$temp)
lemmenv$surface <- scale(lemmenv$surface)
lemmenv$cond <- scale(lemmenv$cond)
lemmenv$clhla <- scale(lemmenv$lchla)
lemmenv$tn <- scale(lemmenv$tn)
lemmenv$tp <- scale(lemmenv$tp)
lemmenv$reed <- scale(lemmenv$reed)
lemmenv$emerg <- scale(lemmenv$emerg)
```

### Now do RDA

```{r}
lemmens2.rda <- rda(lemminvert1h~depth+silt+ph+temp+surface+cond+lchla+tn+tp+reed+emerg, scale=FALSE, data=lemmenv)
summary(lemmens2.rda)
RsquareAdj(lemmens2.rda)
coef(lemmens2.rda)
anova.cca(lemmens2.rda, permutations=1000)
anova.cca(lemmens2.rda, permutations=1000, by="margin")
anova.cca(lemmens2.rda, permutations=1000, by="axis")
# ideally would like ponds represented by symbols, a different one for each management category
ordiplot(lemmens2.rda,scaling=1,type="text")
ordiplot(lemmens2.rda,scaling=2,type="text")
```

## Do CCA without hellinger transform for comparison

## Management first

```{r}
lemmens1.cca <- cca(lemminvert1~manag, scale=FALSE, data=lemmenv)
summary(lemmens1.cca)
RsquareAdj(lemmens1.cca)
coef(lemmens1.cca)
anova.cca(lemmens1.cca, permutations=1000)
anova.cca(lemmens1.cca, permutations=1000, by="margin")
anova.cca(lemmens1.cca, permutations=1000, by="axis")
ordiplot(lemmens1.cca,scaling=1,type="text")
ordiplot(lemmens1.cca,scaling=2,type="text")
```

### Now env variables

```{r}
lemmens2.cca <- cca(lemminvert1~depth+silt+ph+temp+surface+cond+lchla+tn+tp+reed+emerg, scale=FALSE, data=lemmenv)
summary(lemmens2.cca)
RsquareAdj(lemmens2.cca)
coef(lemmens2.cca)
anova.cca(lemmens2.cca, permutations=1000)
anova.cca(lemmens2.cca, permutations=1000, by="margin")
anova.cca(lemmens2.cca, permutations=1000, by="axis")
# ideally would like ponds represented by symbols, a different one for each management category
ordiplot(lemmens2.cca,scaling=1,type="text")
ordiplot(lemmens2.cca,scaling=2,type="text")
```

## Nice graphics

Plots used for QK use the ggplot classic theme, with some tweaks. Tweaks are consolidated into theme_QK; use this theme for figures and tweak the theme to avoid repetitive code changes.

```{r echo= FALSE, results='hide'}
devtools::source_url("https://raw.githubusercontent.com/mjkeough/mjkeough.github.io/refs/heads/main/R/appearance.R")  #Graphics tweaks
```

```{r}
library(ggvegan)
p1<-autoplot(lemmens1.rda)
p2<-autoplot(lemmens2.rda)
p3<-autoplot(lemmens1.cca)
p4<-autoplot(lemmens2.cca)
l1rt<-fortify(lemmens1.rda)
l1rt$label <- l1rt$label %>%
  str_replace_all("managnm", "nm") %>%
  str_replace_all("managli", "li") %>%
  str_replace_all("managnf", "nf") %>%
  str_replace_all("managyf", "yf")
l2rt<-fortify(lemmens2.rda)
l1ct<-fortify(lemmens1.cca)
l1ct$label <- l1ct$label %>%
  str_replace_all("managnm", "nm") %>%
str_replace_all("managli", "li") %>%
str_replace_all("managnf", "nf") %>%
str_replace_all("managyf", "yf")
l2ct<-fortify(lemmens2.cca)
p5<-p1+p2+p3+p4
p5

```

```{r}
library(ggrepel)
br=c("nm","li","nf","yf")  #Set order of groups; define as object to avoid repeated code
la=c("None", "Light", "No fish", "Young fish")  #Set legend text
p1a<-ggplot()+
  geom_point(aes(x=RDA1,y=RDA2, shape=lemminvert$manag), color=sc, data=filter(l1rt, Score=="sites"), 
             alpha=0.7, size=1)+
  scale_shape_manual(values=c(21,22,23,25),
                     name="Management",
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), size=2, data=filter(l1rt, Score=="species"), 
                  )+
  geom_segment(aes(x=0,y=0,xend=RDA1,yend=RDA2), arrow=arrow(length=unit(0.2,"cm")),
               data=filter(l1rt, Score=="centroids"))+
  geom_text_repel(aes(x=RDA1,y=RDA2, label=Label), size=3, data=filter(l1rt, Score=="centroids"), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position="none")
p1a
```

```{r}
p2a<-ggplot()+
  geom_point(aes(x=RDA1,y=RDA2, ), data=filter(l2rt, Score=="sites"), alpha=0.7, color=sc, size=0.5)+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), size=2, data=filter(l2rt, Score=="species"), 
                  )+
  geom_segment(aes(x=0,y=0,xend=RDA1,yend=RDA2), alpha=0.5, arrow=arrow(length=unit(0.2,"cm")),
               data=filter(l2rt, Score=="biplot"))+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), nudge_y=-0.05,color=lc, size=3.5, data=filter(l2rt, Score=="biplot"), 
                  )+
  labs(y=NULL)+
                 coord_fixed()
p2a
```

```{r}
p3a<-ggplot()+
  geom_point(aes(x=CCA1,y=CCA2, shape=lemminvert$manag), color= sc, data=filter(l1ct, Score=="sites"), alpha=0.7, size=1)+
  scale_shape_manual(values=c(21,22,23,25),
                     name="Management",
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), size=2, data=filter(l1ct, Score=="species"), 
                  )+
  geom_segment(aes(x=0,y=0,xend=CCA1,yend=CCA2), arrow=arrow(length=unit(0.2,"cm")),
               data=filter(l1ct, Score=="centroids"))+
  geom_text_repel(aes(x=CCA1,y=CCA2, label=Label), size=3, data=filter(l1ct, Score=="centroids"), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position="none")
p3a
```

```{r}
p4a<-ggplot()+
  geom_point(aes(x=CCA1,y=CCA2, ), data=filter(l2ct, Score=="sites"), alpha=0.7, color=sc, size=0.5)+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), size=2, data=filter(l2ct, Score=="species"), 
                  )+
  geom_segment(aes(x=0,y=0,xend=CCA1,yend=CCA2), alpha=0.5, arrow=arrow(length=unit(0.2,"cm")),
               data=filter(l2ct, Score=="biplot"))+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), color=lc, size=3, data=filter(l2ct, Score=="biplot"), 
                  )+
  labs(y=NULL)+
                 coord_fixed()
p4a
```

Combine panels

```{r}
p5<-p1a+p2a+p3a+p4a
p5<-p5&theme_qk()+theme(legend.position="none")
p5
#ggsave("../Illustrator/QK F15_05.pdf",plot = p5, height = phh,width = pww, units='cm')
```

Colour version of LH panels

```{r}
p1b<-ggplot()+
  geom_point(aes(x=RDA1,y=RDA2, color=lemminvert$manag), data=filter(l1rt, Score=="sites"), 
             alpha=0.7, size=1)+
  scale_color_viridis_d(
                     name="Management",
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=RDA1,y=RDA2, label=Label), size=2, data=filter(l1rt, Score=="species"), 
                  )+
  geom_segment(aes(x=0,y=0,xend=RDA1,yend=RDA2), arrow=arrow(length=unit(0.2,"cm")),
               data=filter(l1rt, Score=="centroids"))+
  geom_text_repel(aes(x=RDA1,y=RDA2, label=Label), size=3, data=filter(l1rt, Score=="centroids"), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position="none")
p3b<-ggplot()+
  geom_point(aes(x=CCA1,y=CCA2, color=lemminvert$manag), data=filter(l1ct, Score=="sites"), alpha=0.7, size=1)+
  scale_color_viridis_d(
                     breaks=br,
                     labels=la
                     )+
  geom_text(aes(x=CCA1,y=CCA2, label=Label), size=2, data=filter(l1ct, Score=="species"), 
                  )+
  geom_segment(aes(x=0,y=0,xend=CCA1,yend=CCA2), arrow=arrow(length=unit(0.2,"cm")),
               data=filter(l1ct, Score=="centroids"))+
  geom_text_repel(aes(x=CCA1,y=CCA2, label=Label), size=3, data=filter(l1ct, Score=="centroids"), 
                  max.overlaps=Inf)+
                 coord_fixed()+
  theme(legend.position="none")
```

```{r}
p5a<-p1b+p2a+p3b+p4a
p5a
p5a<-p5a&theme_qk()+theme(legend.position="none")
#ggsave("QK F15_05c.pdf",plot = p5a, height = phh,width = pww, units='cm')
```
