GLM Binomial

## Loading required package: ggplot2
##  [1] "Foto"                    "Nivel.Intermareal"      
##  [3] "Año"                     "Cob.T"                  
##  [5] "num_lapas"               "RG_T"                   
##  [7] "RM_T"                    "G_T"                    
##  [9] "Monostroma.hariotti"     "Adenocystis.utricularis"
## [11] "Ascoseira.mirabilis"     "Desmarestia.menziesii"  
## [13] "Desmarestia.antarcticus" "Phaeurus.antarcticus"   
## [15] "Corallinaceae"           "Gigartina.skottsbergii" 
## [17] "Iridaea.cordata"         "Palmaria.decipiens"     
## [19] "Plocamium.cartilagineum" "Pyropya.endiviifolia"   
## [21] "Ulvophyceae.NI"          "H"                      
## [23] "S"                       "J"
## Warning: non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = Cob.T ~ Año + Nivel.Intermareal + num_lapas, family = "binomial", 
##     data = sp)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.18260  -0.27216   0.03981   0.43489   0.97714  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)         0.71346    0.55810   1.278 0.201122    
## Año2008             1.02048    0.65103   1.567 0.116999    
## Año2009            -0.59887    0.56138  -1.067 0.286069    
## Año2013             0.27069    0.56831   0.476 0.633855    
## Nivel.IntermarealM -0.79776    0.40323  -1.978 0.047878 *  
## Nivel.IntermarealS -2.10358    0.59542  -3.533 0.000411 ***
## num_lapas          -0.02239    0.07671  -0.292 0.770402    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65.416  on 146  degrees of freedom
## Residual deviance: 38.860  on 140  degrees of freedom
## AIC: 162.25
## 
## Number of Fisher Scoring iterations: 4
## Warning: non-integer #successes in a binomial glm!

## Warning: non-integer #successes in a binomial glm!

## Warning: non-integer #successes in a binomial glm!
## Single term deletions
## 
## Model:
## Cob.T ~ Año + Nivel.Intermareal + num_lapas
##                   Df Deviance    AIC     LRT  Pr(>Chi)    
## <none>                 38.860 162.25                      
## Año                3   47.515 164.91  8.6546 0.0342545 *  
## Nivel.Intermareal  2   54.031 173.43 15.1710 0.0005078 ***
## num_lapas          1   38.945 160.34  0.0854 0.7701124    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = Cob.T ~ Nivel.Intermareal + Año, family = "binomial", 
##     data = sp)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1721  -0.2847   0.0395   0.4470   0.9759  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)          0.6352     0.4887   1.300 0.193680    
## Nivel.IntermarealM  -0.8108     0.4008  -2.023 0.043087 *  
## Nivel.IntermarealS  -2.0794     0.5902  -3.523 0.000427 ***
## Año2008              1.0950     0.5989   1.828 0.067477 .  
## Año2009             -0.5601     0.5450  -1.028 0.304053    
## Año2013              0.3385     0.5187   0.652 0.514108    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 65.416  on 146  degrees of freedom
## Residual deviance: 38.945  on 141  degrees of freedom
## AIC: 160.43
## 
## Number of Fisher Scoring iterations: 4

## Loading required package: multcomp
## Loading required package: mvtnorm
## Loading required package: survival
## Loading required package: TH.data
## Loading required package: MASS
## 
## Attaching package: 'TH.data'
## The following object is masked from 'package:MASS':
## 
##     geyser
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Multiple Comparisons of Means: Tukey Contrasts
## 
## 
## Fit: glm(formula = Cob.T ~ Nivel.Intermareal + Año, family = "binomial", 
##     data = sp)
## 
## Linear Hypotheses:
##            Estimate Std. Error z value Pr(>|z|)   
## M - I == 0  -0.8108     0.4008  -2.023  0.10384   
## S - I == 0  -2.0794     0.5902  -3.523  0.00109 **
## S - M == 0  -1.2686     0.5841  -2.172  0.07380 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Multiple Comparisons of Means: Tukey Contrasts
## 
## 
## Fit: glm(formula = Cob.T ~ Nivel.Intermareal + Año, family = "binomial", 
##     data = sp)
## 
## Linear Hypotheses:
##                  Estimate Std. Error z value Pr(>|z|)  
## 2008 - 2007 == 0   1.0950     0.5989   1.828   0.2580  
## 2009 - 2007 == 0  -0.5601     0.5450  -1.028   0.7314  
## 2013 - 2007 == 0   0.3385     0.5187   0.652   0.9140  
## 2009 - 2008 == 0  -1.6552     0.5651  -2.929   0.0179 *
## 2013 - 2008 == 0  -0.7566     0.5407  -1.399   0.4975  
## 2013 - 2009 == 0   0.8986     0.4643   1.935   0.2116  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)

## 
## Call:
## glm(formula = H ~ Nivel.Intermareal + Año, family = "gaussian", 
##     data = sp)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80750  -0.31103  -0.03443   0.27443   1.13391  
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         0.87410    0.09553   9.150 5.81e-16 ***
## Nivel.IntermarealM  0.16671    0.07710   2.162 0.032293 *  
## Nivel.IntermarealS -0.48572    0.10085  -4.816 3.73e-06 ***
## Año2008            -0.41452    0.11218  -3.695 0.000314 ***
## Año2009            -0.27123    0.10584  -2.563 0.011437 *  
## Año2013            -0.25606    0.10201  -2.510 0.013194 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1722746)
## 
##     Null deviance: 34.491  on 146  degrees of freedom
## Residual deviance: 24.291  on 141  degrees of freedom
## AIC: 166.52
## 
## Number of Fisher Scoring iterations: 2

## Single term deletions
## 
## Model:
## H ~ Nivel.Intermareal + Año
##                   Df Deviance    AIC scaled dev.  Pr(>Chi)    
## <none>                 24.291 166.52                          
## Nivel.Intermareal  2   31.413 200.32      37.800 6.192e-09 ***
## Año                3   26.706 174.46      13.937  0.002992 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Multiple Comparisons of Means: Tukey Contrasts
## 
## 
## Fit: glm(formula = H ~ Nivel.Intermareal + Año, family = "gaussian", 
##     data = sp)
## 
## Linear Hypotheses:
##            Estimate Std. Error z value Pr(>|z|)    
## M - I == 0   0.1667     0.0771   2.162   0.0764 .  
## S - I == 0  -0.4857     0.1008  -4.816   <0.001 ***
## S - M == 0  -0.6524     0.1016  -6.420   <0.001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Multiple Comparisons of Means: Tukey Contrasts
## 
## 
## Fit: glm(formula = H ~ Nivel.Intermareal + Año, family = "gaussian", 
##     data = sp)
## 
## Linear Hypotheses:
##                  Estimate Std. Error z value Pr(>|z|)   
## 2008 - 2007 == 0 -0.41452    0.11218  -3.695   0.0015 **
## 2009 - 2007 == 0 -0.27123    0.10584  -2.563   0.0505 . 
## 2013 - 2007 == 0 -0.25606    0.10201  -2.510   0.0579 . 
## 2009 - 2008 == 0  0.14329    0.10200   1.405   0.4944   
## 2013 - 2008 == 0  0.15846    0.09782   1.620   0.3655   
## 2013 - 2009 == 0  0.01517    0.08864   0.171   0.9982   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
## 
## Call:
## glm(formula = S ~ Nivel.Intermareal + Año, family = "poisson", 
##     data = sp)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.50657  -0.40965  -0.09035   0.45408   1.77691  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)         1.32615    0.12670  10.467  < 2e-16 ***
## Nivel.IntermarealM -0.08361    0.10533  -0.794    0.427    
## Nivel.IntermarealS -1.06341    0.20610  -5.160 2.48e-07 ***
## Año2008            -0.23464    0.15810  -1.484    0.138    
## Año2009            -0.13622    0.14648  -0.930    0.352    
## Año2013            -0.17583    0.13962  -1.259    0.208    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 91.34  on 146  degrees of freedom
## Residual deviance: 52.48  on 141  degrees of freedom
## AIC: 477.5
## 
## Number of Fisher Scoring iterations: 4

## Single term deletions
## 
## Model:
## S ~ Nivel.Intermareal + Año
##                   Df Deviance    AIC    LRT  Pr(>Chi)    
## <none>                 52.480 477.50                     
## Nivel.Intermareal  2   88.270 509.29 35.789 1.692e-08 ***
## Año                3   54.939 473.95  2.459    0.4828    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Multiple Comparisons of Means: Tukey Contrasts
## 
## 
## Fit: glm(formula = S ~ Nivel.Intermareal + Año, family = "poisson", 
##     data = sp)
## 
## Linear Hypotheses:
##            Estimate Std. Error z value Pr(>|z|)    
## M - I == 0 -0.08361    0.10533  -0.794    0.697    
## S - I == 0 -1.06341    0.20610  -5.160   <1e-04 ***
## S - M == 0 -0.97980    0.20770  -4.717   <1e-04 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
## 
## Call:
## glm(formula = J ~ Nivel.Intermareal + Año, family = "gaussian", 
##     data = sp)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.51663  -0.15487  -0.03525   0.15975   0.78778  
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         0.44739    0.05913   7.566 4.53e-12 ***
## Nivel.IntermarealM  0.13887    0.04773   2.910 0.004204 ** 
## Nivel.IntermarealS -0.22745    0.06243  -3.644 0.000377 ***
## Año2008            -0.18469    0.06944  -2.660 0.008724 ** 
## Año2009            -0.08942    0.06551  -1.365 0.174455    
## Año2013            -0.06963    0.06314  -1.103 0.271969    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06600477)
## 
##     Null deviance: 12.1616  on 146  degrees of freedom
## Residual deviance:  9.3067  on 141  degrees of freedom
## AIC: 25.492
## 
## Number of Fisher Scoring iterations: 2

## Single term deletions
## 
## Model:
## J ~ Nivel.Intermareal + Año
##                   Df Deviance    AIC scaled dev.  Pr(>Chi)    
## <none>                 9.3067 25.492                          
## Nivel.Intermareal  2  11.5769 53.579      32.087 1.077e-07 ***
## Año                3   9.7954 27.015       7.524   0.05696 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Multiple Comparisons of Means: Tukey Contrasts
## 
## 
## Fit: glm(formula = J ~ Nivel.Intermareal + Año, family = "gaussian", 
##     data = sp)
## 
## Linear Hypotheses:
##            Estimate Std. Error z value Pr(>|z|)    
## M - I == 0  0.13887    0.04773   2.910  0.00994 ** 
## S - I == 0 -0.22745    0.06243  -3.644  < 0.001 ***
## S - M == 0 -0.36632    0.06291  -5.823  < 0.001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:MASS':
## 
##     select
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## Loading required package: pander
## 
## ---------------------------------------------------------------------
##  Nivel.Intermareal   H_mean   S_mean   J_mean   H_se    S_se   J_se  
## ------------------- -------- -------- -------- ------- ------ -------
##          I           0.6218   3.238    0.3636  0.05412 0.1496 0.02907
## 
##          M           0.8054   3.033    0.5003  0.06129 0.1301 0.03577
## 
##          S           0.1316   1.125    0.1316  0.05967 0.1096 0.05967
## ---------------------------------------------------------------------

NMDS of macroalgal species

##  [1] "Foto"                    "Nivel.Intermareal"      
##  [3] "Año"                     "Cob.T"                  
##  [5] "num_lapas"               "RG_T"                   
##  [7] "RM_T"                    "G_T"                    
##  [9] "Monostroma.hariotti"     "Adenocystis.utricularis"
## [11] "Ascoseira.mirabilis"     "Desmarestia.menziesii"  
## [13] "Desmarestia.antarcticus" "Phaeurus.antarcticus"   
## [15] "Corallinaceae"           "Gigartina.skottsbergii" 
## [17] "Iridaea.cordata"         "Palmaria.decipiens"     
## [19] "Plocamium.cartilagineum" "Pyropya.endiviifolia"   
## [21] "Ulvophyceae.NI"          "H"                      
## [23] "S"                       "J"
##  [1] "Foto"                    "Nivel.Intermareal"      
##  [3] "Año"                     "Cob.T"                  
##  [5] "num_lapas"               "RG_T"                   
##  [7] "RM_T"                    "G_T"                    
##  [9] "Monostroma.hariotti"     "Adenocystis.utricularis"
## [11] "Ascoseira.mirabilis"     "Desmarestia.menziesii"  
## [13] "Phaeurus.antarcticus"    "Gigartina.skottsbergii" 
## [15] "Iridaea.cordata"         "Palmaria.decipiens"     
## [17] "H"                       "S"                      
## [19] "J"

## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.4-0
## Run 0 stress 0.1527 
## Run 1 stress 0.1324 
## ... New best solution
## ... Procrustes: rmse 0.04369  max resid 0.3022 
## Run 2 stress 0.1568 
## Run 3 stress 0.1525 
## Run 4 stress 0.1536 
## Run 5 stress 0.1484 
## Run 6 stress 0.1585 
## Run 7 stress 0.1488 
## Run 8 stress 0.1448 
## Run 9 stress 0.1609 
## Run 10 stress 0.1515 
## Run 11 stress 0.1495 
## Run 12 stress 0.1482 
## Run 13 stress 0.1465 
## Run 14 stress 0.1759 
## Run 15 stress 0.143 
## Run 16 stress 0.1543 
## Run 17 stress 0.142 
## Run 18 stress 0.1484 
## Run 19 stress 0.1605 
## Run 20 stress 0.1411 
## Run 21 stress 0.1697 
## Run 22 stress 0.1416 
## Run 23 stress 0.1436 
## Run 24 stress 0.1762 
## Run 25 stress 0.1621 
## Run 26 stress 0.1492 
## Run 27 stress 0.1517 
## Run 28 stress 0.1342 
## Run 29 stress 0.151 
## Run 30 stress 0.1604 
## Run 31 stress 0.1402 
## Run 32 stress 0.1702 
## Run 33 stress 0.1646 
## Run 34 stress 0.1342 
## Run 35 stress 0.1496 
## Run 36 stress 0.1324 
## ... Procrustes: rmse 0.001855  max resid 0.01701 
## Run 37 stress 0.1547 
## Run 38 stress 0.1472 
## Run 39 stress 0.15 
## Run 40 stress 0.1448 
## Run 41 stress 0.1602 
## Run 42 stress 0.1324 
## ... Procrustes: rmse 0.001707  max resid 0.01709 
## Run 43 stress 0.1549 
## Run 44 stress 0.1505 
## Run 45 stress 0.1389 
## Run 46 stress 0.1718 
## Run 47 stress 0.1453 
## Run 48 stress 0.1617 
## Run 49 stress 0.1604 
## Run 50 stress 0.1519 
## Run 51 stress 0.1402 
## Run 52 stress 0.1378 
## Run 53 stress 0.1501 
## Run 54 stress 0.1662 
## Run 55 stress 0.1519 
## Run 56 stress 0.1761 
## Run 57 stress 0.15 
## Run 58 stress 0.1441 
## Run 59 stress 0.1324 
## ... Procrustes: rmse 0.001704  max resid 0.01706 
## Run 60 stress 0.1694 
## Run 61 stress 0.1535 
## Run 62 stress 0.1431 
## Run 63 stress 0.1481 
## Run 64 stress 0.1697 
## Run 65 stress 0.1556 
## Run 66 stress 0.155 
## Run 67 stress 0.1389 
## Run 68 stress 0.1564 
## Run 69 stress 0.1343 
## Run 70 stress 0.1631 
## Run 71 stress 0.1578 
## Run 72 stress 0.1525 
## Run 73 stress 0.163 
## Run 74 stress 0.142 
## Run 75 stress 0.1493 
## Run 76 stress 0.1491 
## Run 77 stress 0.1478 
## Run 78 stress 0.1633 
## Run 79 stress 0.1506 
## Run 80 stress 0.1675 
## Run 81 stress 0.1545 
## Run 82 stress 0.1735 
## Run 83 stress 0.1593 
## Run 84 stress 0.1324 
## ... Procrustes: rmse 0.00171  max resid 0.01704 
## Run 85 stress 0.1552 
## Run 86 stress 0.1324 
## ... Procrustes: rmse 0.001856  max resid 0.01702 
## Run 87 stress 0.168 
## Run 88 stress 0.1552 
## Run 89 stress 0.1483 
## Run 90 stress 0.1505 
## Run 91 stress 0.1584 
## Run 92 stress 0.157 
## Run 93 stress 0.1579 
## Run 94 stress 0.1516 
## Run 95 stress 0.1672 
## Run 96 stress 0.1535 
## Run 97 stress 0.1481 
## Run 98 stress 0.1389 
## Run 99 stress 0.1547 
## Run 100 stress 0.159 
## *** No convergence -- monoMDS stopping criteria:
##      1: no. of iterations >= maxit
##     96: stress ratio > sratmax
##      3: scale factor of the gradient < sfgrmin
##                           NMDS1   NMDS2
## Monostroma.hariotti      1.4868  0.2805
## Adenocystis.utricularis  1.4122  0.3861
## Ascoseira.mirabilis     -1.1242  0.5874
## Desmarestia.menziesii   -0.3740 -0.1389
## Phaeurus.antarcticus    -1.3912  1.0168
## Gigartina.skottsbergii  -0.6848 -0.6945
## Iridaea.cordata         -0.5724 -0.3939
## Palmaria.decipiens       1.9000 -1.3233

## Loading required package: RColorBrewer
## Warning in brewer.pal(ngrp, "Set1"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in match.fun(FUN)(...): "kind" is not a graphical parameter
## Warning in match.fun(FUN)(...): "conf" is not a graphical parameter
## Warning in match.fun(FUN)(...): "kind" is not a graphical parameter
## Warning in match.fun(FUN)(...): "conf" is not a graphical parameter

Adonis and envfit

## 
## Call:
## adonis(formula = spo[, 9:16] ~ Nivel.Intermareal * Año * num_lapas *      Cob.T, data = spo) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                                        Df SumsOfSqs MeanSqs F.Model    R2
## Nivel.Intermareal                       1       4.1    4.13   31.11 0.131
## Año                                     3       3.6    1.19    8.94 0.113
## num_lapas                               1       0.2    0.17    1.26 0.005
## Cob.T                                   1       2.2    2.23   16.79 0.071
## Nivel.Intermareal:Año                   3       3.2    1.07    8.03 0.101
## Nivel.Intermareal:num_lapas             1       0.2    0.17    1.27 0.005
## Año:num_lapas                           3       0.6    0.21    1.59 0.020
## Nivel.Intermareal:Cob.T                 1       0.7    0.70    5.27 0.022
## Año:Cob.T                               3       1.9    0.64    4.83 0.061
## num_lapas:Cob.T                         1       0.2    0.21    1.55 0.007
## Nivel.Intermareal:Año:num_lapas         3       0.8    0.25    1.91 0.024
## Nivel.Intermareal:Año:Cob.T             3       0.6    0.21    1.57 0.020
## Nivel.Intermareal:num_lapas:Cob.T       1       0.2    0.18    1.35 0.006
## Año:num_lapas:Cob.T                     3       0.7    0.23    1.74 0.022
## Nivel.Intermareal:Año:num_lapas:Cob.T   3       0.4    0.12    0.91 0.011
## Residuals                              91      12.1    0.13         0.382
## Total                                 122      31.6                 1.000
##                                       Pr(>F)    
## Nivel.Intermareal                      0.001 ***
## Año                                    0.001 ***
## num_lapas                              0.284    
## Cob.T                                  0.001 ***
## Nivel.Intermareal:Año                  0.001 ***
## Nivel.Intermareal:num_lapas            0.279    
## Año:num_lapas                          0.089 .  
## Nivel.Intermareal:Cob.T                0.003 ** 
## Año:Cob.T                              0.001 ***
## num_lapas:Cob.T                        0.152    
## Nivel.Intermareal:Año:num_lapas        0.028 *  
## Nivel.Intermareal:Año:Cob.T            0.097 .  
## Nivel.Intermareal:num_lapas:Cob.T      0.223    
## Año:num_lapas:Cob.T                    0.057 .  
## Nivel.Intermareal:Año:num_lapas:Cob.T  0.561    
## Residuals                                       
## Total                                           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "Analisis de la dispersion por nivel intermareal"
## Analysis of Variance Table
## 
## Response: Distances
##            Df Sum Sq Mean Sq F value   Pr(>F)    
## Groups      1   0.95   0.954    18.3 0.000038 ***
## Residuals 121   6.31   0.052                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = distances ~ group, data = df)
## 
## $group
##       diff    lwr    upr p adj
## M-I 0.1762 0.0946 0.2577     0

## [1] "Analisis de la dispersion por Año"
## Analysis of Variance Table
## 
## Response: Distances
##            Df Sum Sq Mean Sq F value  Pr(>F)    
## Groups      3   0.89  0.2957    5.92 0.00084 ***
## Residuals 119   5.94  0.0499                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = distances ~ group, data = df)
## 
## $group
##           diff      lwr      upr  p adj
## V2-V1  0.11931 -0.04887  0.28750 0.2561
## V3-V1 -0.02378 -0.18511  0.13755 0.9806
## V4-V1 -0.11349 -0.26270  0.03571 0.2006
## V3-V2 -0.14309 -0.30073  0.01454 0.0895
## V4-V2 -0.23281 -0.37801 -0.08760 0.0003
## V4-V3 -0.08971 -0.22692  0.04749 0.3263
## [1] "Analisis de la dispersion por Lapas"
## Analysis of Variance Table
## 
## Response: Distances
##            Df Sum Sq Mean Sq F value Pr(>F)  
## Groups     11   1.32  0.1205    1.76  0.069 .
## Residuals 111   7.58  0.0683                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "Analisis de la dispersion por Cobertura"
## Analysis of Variance Table
## 
## Response: Distances
##            Df Sum Sq Mean Sq F value      Pr(>F)    
## Groups    106   3.99  0.0377      18 0.000000039 ***
## Residuals  16   0.03  0.0021                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ***VECTORS
## 
##            NMDS1  NMDS2   r2 Pr(>r)    
## num_lapas  0.481  0.877 0.02  0.256    
## Cob.T     -0.207 -0.978 0.46  0.001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
## 
## ***FACTORS:
## 
## Centroids:
##                    NMDS1 NMDS2
## Nivel.IntermarealI -0.53 -0.09
## Nivel.IntermarealM  0.55  0.10
## AñoV1               0.25  0.03
## AñoV2               0.74 -0.30
## AñoV3              -0.07  0.38
## AñoV4              -0.49 -0.10
## 
## Goodness of fit:
##                     r2 Pr(>r)    
## Nivel.Intermareal 0.21  0.001 ***
## Año               0.19  0.001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
## png 
##   2
## png 
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