Libraries and settings

# Libraries
    library(tidyverse)

#Plots
    library(ggpubr)
    library(ggthemes)
    library(ggplot2)

#Models     
    library(emmeans)
    library(multcomp)

# Plots theme
MyTheme<-theme_bw() +  
theme(legend.position="top",
          plot.background=element_blank(),
          #axis.text.x = element_text(angle = 90, vjust = 0.5),
          axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x = element_blank(),
          panel.grid.major.y = element_blank(),
          panel.grid.major.x = element_blank(),
          panel.grid.minor.x = element_blank(),
          panel.grid.minor.y = element_blank(),
          legend.box.background = element_rect(),
          #legend.title = element_blank(),
          panel.background =element_rect(fill = NA, 
                                         color = "black"))

Import and format data

# 1. Get general cover data
  General.data<-read.csv("Datos/Datos_Categorias_generales.csv", header = T)
  summary(General.data)
##             Metodo         Zona        Sitio        Transecto  Buzo  
##  Cadenas       :36   Planicie:54   Min.   :1   Planicie_1:18   A:36  
##  Cuadrantes    :36   Talud   :54   1st Qu.:1   Planicie_2:18   B:36  
##  Fotocuadrantes:36                 Median :2   Planicie_3:18   C:36  
##                                    Mean   :2   Talud_1   :18         
##                                    3rd Qu.:3   Talud_2   :18         
##                                    Max.   :3   Talud_3   :18         
##     Replica    Tiempo.Campo..min. Tiempo.Laboratorio..min. Tiempo.Total..min.
##  Min.   :1.0   Min.   : 7.0       Min.   : 2.0             Min.   :10.0      
##  1st Qu.:1.0   1st Qu.:13.0       1st Qu.: 6.8             1st Qu.:20.8      
##  Median :1.5   Median :18.5       Median :10.0             Median :31.5      
##  Mean   :1.5   Mean   :19.2       Mean   :15.6             Mean   :34.9      
##  3rd Qu.:2.0   3rd Qu.:23.2       3rd Qu.:27.0             3rd Qu.:46.2      
##  Max.   :2.0   Max.   :49.0       Max.   :42.0             Max.   :86.0      
##       Día            Coral          Esponja             Alga     
##  Min.   : 4.00   Min.   : 11.3   Min.   :0.00000   Min.   : 0.0  
##  1st Qu.: 6.00   1st Qu.: 55.2   1st Qu.:0.00000   1st Qu.: 2.1  
##  Median : 8.00   Median : 76.0   Median :0.00000   Median :11.2  
##  Mean   : 7.53   Mean   : 68.5   Mean   :0.00028   Mean   :18.7  
##  3rd Qu.: 9.25   3rd Qu.: 84.7   3rd Qu.:0.00000   3rd Qu.:21.0  
##  Max.   :10.00   Max.   :100.0   Max.   :0.02000   Max.   :88.7  
##     Sustrato       arco..C         arco..S           arco..A     
##  Min.   : 0.0   Min.   :0.343   Min.   :0.00000   Min.   :0.000  
##  1st Qu.: 0.1   1st Qu.:0.838   1st Qu.:0.00000   1st Qu.:0.144  
##  Median : 6.8   Median :1.059   Median :0.00000   Median :0.342  
##  Mean   :12.9   Mean   :0.999   Mean   :0.00022   Mean   :0.376  
##  3rd Qu.:20.0   3rd Qu.:1.169   3rd Qu.:0.00000   3rd Qu.:0.476  
##  Max.   :68.0   Max.   :1.571   Max.   :0.01400   Max.   :1.228  
##    arco...SI        X                                 Notas.de.los.datos
##  Min.   :0.000   Mode:logical                                  :103     
##  1st Qu.:0.020   NA's:108       A=ALGA                         :  1     
##  Median :0.263                  C=CORAL                        :  1     
##  Mean   :0.290                  p=datos transformados(arcoseno):  1     
##  3rd Qu.:0.464                  S= ESPONJA                     :  1     
##  Max.   :0.969                  SI=SUSTRATO INERTE             :  1
  #General.data<-General.data[, (1:14) ]
  Factors<-c("Metodo", "Zona", "Sitio", "Buzo", "Transecto", "Replica")
  General.data<-General.data %>% mutate_at(Factors, factor)

# 2. Riqueza
  Riqueza.data<-read.csv("Datos/Riqueza_Bentos.csv", header = T)
  summary(Riqueza.data)
##             Metodo         Zona        Sitio        Transecto  Buzo  
##  Cadenas       :36   Planicie:54   Min.   :1   Planicie_1:18   A:36  
##  Cuadrantes    :36   Talud   :54   1st Qu.:1   Planicie_2:18   B:36  
##  Fotocuadrantes:36                 Median :2   Planicie_3:18   C:36  
##                                    Mean   :2   Talud_1   :18         
##                                    3rd Qu.:3   Talud_2   :18         
##                                    Max.   :3   Talud_3   :18         
##                                                                      
##     Replica       X..PCLA          X..PAVSP        X..PVAR         X..PCAP    
##  Min.   :1.0   Min.   :0.0000   Min.   :0.000   Min.   :0.000   Min.   : 3.4  
##  1st Qu.:1.0   1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0.000   1st Qu.:27.5  
##  Median :1.5   Median :0.0000   Median :0.000   Median :0.000   Median :44.5  
##  Mean   :1.5   Mean   :0.0017   Mean   :0.015   Mean   :0.025   Mean   :47.6  
##  3rd Qu.:2.0   3rd Qu.:0.0000   3rd Qu.:0.000   3rd Qu.:0.000   3rd Qu.:66.2  
##  Max.   :2.0   Max.   :0.1790   Max.   :1.071   Max.   :0.893   Max.   :99.4  
##                                                                               
##     X..PDAM        X..PEYD        X..POSP        X..PSTE        X..ENSP       
##  Min.   : 0.0   Min.   : 0.0   Min.   :0.00   Min.   :0.00   Min.   :0.00000  
##  1st Qu.: 0.5   1st Qu.: 0.0   1st Qu.:0.00   1st Qu.:0.00   1st Qu.:0.00000  
##  Median : 9.5   Median : 0.0   Median :0.00   Median :0.00   Median :0.00000  
##  Mean   :15.7   Mean   : 4.3   Mean   :0.21   Mean   :0.54   Mean   :0.00028  
##  3rd Qu.:24.8   3rd Qu.: 1.4   3rd Qu.:0.00   3rd Qu.:0.36   3rd Qu.:0.00000  
##  Max.   :66.9   Max.   :39.6   Max.   :5.18   Max.   :6.60   Max.   :0.02000  
##                                                                               
##     X..FALG         X..TALG         X..CALG         X..EALG        X..BOUL     
##  Min.   :0.000   Min.   : 0.00   Min.   :0.000   Min.   : 0.0   Min.   :0.000  
##  1st Qu.:0.000   1st Qu.: 0.00   1st Qu.:0.000   1st Qu.: 1.4   1st Qu.:0.000  
##  Median :0.000   Median : 0.36   Median :0.000   Median : 8.4   Median :0.000  
##  Mean   :0.036   Mean   : 2.60   Mean   :0.132   Mean   :15.9   Mean   :0.008  
##  3rd Qu.:0.000   3rd Qu.: 3.48   3rd Qu.:0.005   3rd Qu.:15.8   3rd Qu.:0.000  
##  Max.   :1.500   Max.   :24.29   Max.   :2.321   Max.   :86.1   Max.   :0.893  
##                                                                                
##     X..RUBB         X.DCOR        X..NOID         X..ROCK        X..SAND     
##  Min.   : 0.0   Min.   : 0.0   Min.   : 0.00   Min.   :0.00   Min.   : 0.00  
##  1st Qu.: 0.0   1st Qu.: 0.0   1st Qu.: 0.00   1st Qu.:0.00   1st Qu.: 0.00  
##  Median : 0.9   Median : 0.0   Median : 0.00   Median :0.00   Median : 0.00  
##  Mean   : 7.1   Mean   : 4.1   Mean   : 0.86   Mean   :0.07   Mean   : 0.74  
##  3rd Qu.:10.7   3rd Qu.: 0.4   3rd Qu.: 0.22   3rd Qu.:0.00   3rd Qu.: 0.00  
##  Max.   :67.1   Max.   :68.0   Max.   :13.93   Max.   :4.46   Max.   :13.99  
##                                                                              
##      p.PCLA          p.PAVSP           p.PVAR           p.PCAP     
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.185  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.552  
##  Median :0.0000   Median :0.0000   Median :0.0000   Median :0.730  
##  Mean   :0.0004   Mean   :0.0016   Mean   :0.0049   Mean   :0.763  
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.950  
##  Max.   :0.0420   Max.   :0.1040   Max.   :0.0950   Max.   :1.493  
##                                                                    
##      p.PDAM          p.PEYD          p.POSP           p.PSTE      
##  Min.   :0.000   Min.   :0.000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.068   1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.313   Median :0.000   Median :0.0000   Median :0.0000  
##  Mean   :0.329   Mean   :0.108   Mean   :0.0165   Mean   :0.0351  
##  3rd Qu.:0.522   3rd Qu.:0.118   3rd Qu.:0.0000   3rd Qu.:0.0600  
##  Max.   :0.958   Max.   :0.681   Max.   :0.2300   Max.   :0.2600  
##                                                                   
##      P.ENSP            p.FALG           p.TALG          p.CALG      
##  Min.   :0.00000   Min.   :0.0000   Min.   :0.000   Min.   :0.0000  
##  1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0.0000  
##  Median :0.00000   Median :0.0000   Median :0.060   Median :0.0000  
##  Mean   :0.00022   Mean   :0.0061   Mean   :0.105   Mean   :0.0153  
##  3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.188   3rd Qu.:0.0035  
##  Max.   :0.01400   Max.   :0.1230   Max.   :0.515   Max.   :0.1530  
##                                                                     
##      p.EALG          p.BOUL           p.RUBB          p.DCOR     
##  Min.   :0.000   Min.   :0.0000   Min.   :0.000   Min.   :0.000  
##  1st Qu.:0.119   1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0.000  
##  Median :0.294   Median :0.0000   Median :0.095   Median :0.000  
##  Mean   :0.334   Mean   :0.0009   Mean   :0.179   Mean   :0.090  
##  3rd Qu.:0.409   3rd Qu.:0.0000   3rd Qu.:0.332   3rd Qu.:0.066  
##  Max.   :1.188   Max.   :0.0950   Max.   :0.960   Max.   :0.969  
##                                                                  
##      p.NOID          p.ROCK           p.SAND         X                X.1    
##  Min.   :0.000   Min.   :0.0000   Min.   :0.000   Mode:logical          :88  
##  1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:0.000   NA's:108       BOUL   : 1  
##  Median :0.000   Median :0.0000   Median :0.000                  CALG   : 1  
##  Mean   :0.042   Mean   :0.0052   Mean   :0.036                  DCOR   : 1  
##  3rd Qu.:0.046   3rd Qu.:0.0000   3rd Qu.:0.000                  EALG   : 1  
##  Max.   :0.382   Max.   :0.2130   Max.   :0.383                  ENSP   : 1  
##                                                                  (Other):15  
##                         X.2      Riqueza..S.  
##                           :88   Min.   :1.00  
##  Alga calcárea erecta     : 1   1st Qu.:2.00  
##  Alga calcárea incrustante: 1   Median :2.50  
##  Alga frondosa            : 1   Mean   :2.73  
##  Alga tapete              : 1   3rd Qu.:4.00  
##  Arena                    : 1   Max.   :6.00  
##  (Other)                  :15
  Riqueza.data<-Riqueza.data[, -(7:47) ]
  #Factors<-c("Metodo", "Zona", "Sitio", "Buzo", "Transecto", "Replica")
  Riqueza.data<-Riqueza.data %>% mutate_at(Factors, factor)
  
# 3. Time
  Time.data<<-read.csv("Datos/Datos_tiempo.csv", header = T)
  summary(Time.data)
##             Metodo         Zona        Sitio   Buzo      Replica   
##  Cadenas       :36   Planicie:54   Min.   :1   A:36   Min.   :1.0  
##  Cuadrantes    :36   Talud   :54   1st Qu.:1   B:36   1st Qu.:1.0  
##  Fotocuadrantes:36                 Median :2   C:36   Median :1.5  
##                                    Mean   :2          Mean   :1.5  
##                                    3rd Qu.:3          3rd Qu.:2.0  
##                                    Max.   :3          Max.   :2.0  
##                                                                    
##   Tiempo.toma.de.datos Tiempo.digitalización.de.datos      Tiempo.Total
##  12:11:00 AM:12        12:09:00 AM:11                 12:15:00 AM: 8   
##  12:12:00 AM: 8        12:07:00 AM: 8                 12:18:00 AM: 5   
##  12:20:00 AM: 7        12:04:00 AM: 7                 12:26:00 AM: 5   
##  12:22:00 AM: 7        12:05:00 AM: 7                 12:53:00 AM: 5   
##  12:14:00 AM: 6        12:10:00 AM: 7                 12:22:00 AM: 4   
##  12:13:00 AM: 5        12:03:00 AM: 6                 12:23:00 AM: 4   
##  (Other)    :63        (Other)    :62                 (Other)    :77   
##       Fecha    Minutos_campo   Minutos_lab   Tiempo.Total.1      Día       
##  04/10/09:12   Min.   : 7.0   Min.   : 2.0   Min.   :10.0   Min.   : 4.00  
##  05/10/09:14   1st Qu.:13.0   1st Qu.: 6.8   1st Qu.:20.8   1st Qu.: 6.00  
##  06/10/09:12   Median :18.5   Median :10.0   Median :31.5   Median : 8.00  
##  07/10/09: 9   Mean   :19.2   Mean   :15.6   Mean   :34.9   Mean   : 7.53  
##  08/10/09:16   3rd Qu.:23.2   3rd Qu.:27.0   3rd Qu.:46.2   3rd Qu.: 9.25  
##  09/10/09:18   Max.   :49.0   Max.   :42.0   Max.   :86.0   Max.   :10.00  
##  10/10/09:27                                                               
##       Transecto 
##  Planicie_1:18  
##  Planicie_2:18  
##  Planicie_3:18  
##  Talud_1   :18  
##  Talud_2   :18  
##  Talud_3   :18  
## 
  #Factors<-c("Metodo", "Zona", "Sitio", "Buzo", "Transecto", "Replica")
  Time.data<-Time.data %>% mutate_at(Factors, factor)
  #Time.data <- gather(Time.data, Fase, minutos, Tiempo.Campo..min.:Tiempo.Laboratorio..min., factor_key=TRUE)

1. Coral cover

Model

# Transformacion de los datos
General.data$T_Coral<- asin(sqrt(General.data$Coral/100))

Coral_Cover_model <- lm(T_Coral ~1 +  Metodo + Buzo + 
                          Zona + Sitio%in%Zona + 
                          Metodo*Buzo +
                          Metodo*Zona + Buzo*Zona +
                          Metodo*Buzo*Zona + 
                          Metodo*Sitio%in%Zona +
                          Buzo*Sitio%in%Zona + 
                          Metodo*Buzo*Sitio%in%Zona,
                        data = General.data)

# summary(Coral_Cover_model)                          
  anova(Coral_Cover_model)
  par(mfrow=c(1,2))
  plot(Coral_Cover_model)

  par(mfrow=c(1,1))

  Comp1<-emmeans(Coral_Cover_model, ~Metodo|Zona)
  pairs(Comp1, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate     SE df t.ratio p.value
##  Cadenas - Cuadrantes          0.0703 0.0166 54   4.230  <.0001
##  Cadenas - Fotocuadrantes     -0.1021 0.0166 54  -6.140  <.0001
##  Cuadrantes - Fotocuadrantes  -0.1725 0.0166 54 -10.370  <.0001
## 
## Zona = Talud:
##  contrast                    estimate     SE df t.ratio p.value
##  Cadenas - Cuadrantes         -0.0018 0.0166 54  -0.110  0.9930
##  Cadenas - Fotocuadrantes      0.1957 0.0166 54  11.770  <.0001
##  Cuadrantes - Fotocuadrantes   0.1975 0.0166 54  11.880  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp2<-emmeans(Coral_Cover_model, ~Buzo|Metodo)
  pairs(Comp2, adj="tukey")      
## Metodo = Cadenas:
##  contrast estimate     SE df t.ratio p.value
##  A - B     -0.0732 0.0204 54  -3.600  0.0020
##  A - C     -0.0137 0.0204 54  -0.670  0.7790
##  B - C      0.0595 0.0204 54   2.920  0.0140
## 
## Metodo = Cuadrantes:
##  contrast estimate     SE df t.ratio p.value
##  A - B     -0.0173 0.0204 54  -0.850  0.6740
##  A - C     -0.0116 0.0204 54  -0.570  0.8370
##  B - C      0.0057 0.0204 54   0.280  0.9570
## 
## Metodo = Fotocuadrantes:
##  contrast estimate     SE df t.ratio p.value
##  A - B      0.0376 0.0204 54   1.850  0.1640
##  A - C     -0.0234 0.0204 54  -1.150  0.4900
##  B - C     -0.0610 0.0204 54  -2.990  0.0110
## 
## Results are averaged over the levels of: Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates

Plot

Coral_Cover<- ggplot(General.data) + facet_grid(~Transecto)+
  MyTheme+ scale_shape_manual(values=c(21,22,25))+ 
  stat_summary(aes(x=Metodo, y=Coral, fill=Metodo, group=Metodo),
                 fun.data = "mean_cl_boot", geom = "bar", 
                 position=position_dodge(width=0.8), alpha=0.7)+
   stat_summary(aes(x=Metodo, y=Coral, group=Metodo, color=Metodo),
                 fun.data = "mean_cl_boot", geom = "errorbar", 
                 position=position_dodge(width=0.8))+
   geom_point(aes (x=Metodo, y=Coral, shape=Buzo, 
                   fill=Metodo), position = position_dodge(0.8))+
   #geom_jitter( aes (x=Metodo, y=Coral, shape=Buzo, 
  #                   fill=Metodo, group=Metodo))+
    scale_y_continuous(limits = c(0,99),
                      expand = c(0.03, 0.03),
                      breaks = seq(0, 99, 20),
                      name=expression("Cobertura de coral (%)"))
Coral_Cover

Coral_Cover<-Coral_Cover +
    theme(strip.background = element_blank(),
    strip.text.x = element_blank())

2. Algae cover

Model

General.data$T_Alga<- asin(sqrt((General.data$Alga/100)))

Alga_Cover_model<-lm(T_Alga~ 1 + Metodo + Buzo +
                          Zona + Sitio%in%Zona + 
                          Metodo*Buzo +
                          Metodo*Zona + Buzo*Zona +
                          Metodo*Buzo*Zona + 
                          Metodo*Sitio%in%Zona +
                          Buzo*Sitio%in%Zona + 
                          Metodo*Buzo*Sitio%in%Zona,
                        data = General.data)

  #summary(Alga_Cover_model)                          
  anova(Alga_Cover_model)
  par(mfrow=c(1,2))
  plot(Alga_Cover_model)

  par(mfrow=c(1,1))

  Comp2<-emmeans(Alga_Cover_model, ~Metodo|Zona)
  pairs(Comp2, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate     SE df t.ratio p.value
##  Cadenas - Cuadrantes         -0.0940 0.0245 54  -3.840  0.0010
##  Cadenas - Fotocuadrantes      0.1472 0.0245 54   6.020  <.0001
##  Cuadrantes - Fotocuadrantes   0.2412 0.0245 54   9.860  <.0001
## 
## Zona = Talud:
##  contrast                    estimate     SE df t.ratio p.value
##  Cadenas - Cuadrantes          0.0105 0.0245 54   0.430  0.9040
##  Cadenas - Fotocuadrantes     -0.1311 0.0245 54  -5.360  <.0001
##  Cuadrantes - Fotocuadrantes  -0.1416 0.0245 54  -5.790  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp3<-emmeans(Alga_Cover_model, ~Buzo|Metodo)
  pairs(Comp3, adj="tukey")   
## Metodo = Cadenas:
##  contrast estimate   SE df t.ratio p.value
##  A - B       0.114 0.03 54   3.790  0.0010
##  A - C       0.474 0.03 54  15.820  <.0001
##  B - C       0.360 0.03 54  12.030  <.0001
## 
## Metodo = Cuadrantes:
##  contrast estimate   SE df t.ratio p.value
##  A - B       0.048 0.03 54   1.610  0.2520
##  A - C       0.454 0.03 54  15.140  <.0001
##  B - C       0.406 0.03 54  13.540  <.0001
## 
## Metodo = Fotocuadrantes:
##  contrast estimate   SE df t.ratio p.value
##  A - B      -0.039 0.03 54  -1.310  0.3940
##  A - C       0.268 0.03 54   8.940  <.0001
##  B - C       0.307 0.03 54  10.250  <.0001
## 
## Results are averaged over the levels of: Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates

Plot

Algae_Cover<- ggplot(General.data) + facet_grid(~Transecto)+
  MyTheme+ scale_shape_manual(values=c(21,22,25))+ 
  stat_summary(aes(x=Metodo, y=Alga, fill=Metodo),
                 fun.data = "mean_cl_boot", geom = "bar", 
                 position=position_dodge(width=0.8), alpha=0.7)+
  
  stat_summary(aes(x=Metodo, y=Alga, colour=Metodo),
                 fun.data = "mean_cl_boot", geom = "errorbar", 
                 position=position_dodge(width=0.8))+
  geom_point(aes (x=Metodo, y=Alga, shape=Buzo, 
                   fill=Metodo), position = position_dodge(0.5))+
   #geom_jitter( aes (x=Metodo, y=Alga, shape=Buzo, 
  #                   fill=Metodo, group=Metodo))+
  scale_y_continuous(limits = c(0, 90),
                      expand = c(0.03, 0.3),
                      breaks = seq(0, 100, 20),
                      name=expression("Cobertura de algas (%)"))
Algae_Cover

Algae_Cover<- Algae_Cover +
    theme(legend.position="none",
        strip.background = element_blank(),
        strip.text.x = element_blank())

3. Substrate cover

Model

General.data$T_Sustrato<- acos(General.data$Sustrato /100)

Sus_Cover_model<-lm(T_Sustrato ~  1 + Metodo + Buzo +
                          Zona + Sitio%in%Zona + 
                          Metodo*Buzo +
                          Metodo*Zona + Buzo*Zona +
                          Metodo*Buzo*Zona + 
                          Metodo*Sitio%in%Zona +
                          Buzo*Sitio%in%Zona + 
                          Metodo*Buzo*Sitio%in%Zona,
                        data = General.data)

  #summary(Sus_Cover_model)                          
  anova(Sus_Cover_model)
  par(mfrow=c(1,2))
  plot(Sus_Cover_model)

  par(mfrow=c(1,1))

  Comp5<-emmeans(Sus_Cover_model, ~Metodo|Zona)
  pairs(Comp5, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate     SE df t.ratio p.value
##  Cadenas - Cuadrantes         -0.0106 0.0159 54  -0.670  0.7830
##  Cadenas - Fotocuadrantes      0.0402 0.0159 54   2.540  0.0370
##  Cuadrantes - Fotocuadrantes   0.0508 0.0159 54   3.200  0.0060
## 
## Zona = Talud:
##  contrast                    estimate     SE df t.ratio p.value
##  Cadenas - Cuadrantes          0.0002 0.0159 54   0.010  1.0000
##  Cadenas - Fotocuadrantes      0.0846 0.0159 54   5.330  <.0001
##  Cuadrantes - Fotocuadrantes   0.0844 0.0159 54   5.320  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp6<-emmeans(Sus_Cover_model, ~Buzo|Metodo)
  pairs(Comp6, adj="tukey")   
## Metodo = Cadenas:
##  contrast estimate     SE df t.ratio p.value
##  A - B      0.0233 0.0194 54   1.200  0.4590
##  A - C      0.2835 0.0194 54  14.590  <.0001
##  B - C      0.2602 0.0194 54  13.390  <.0001
## 
## Metodo = Cuadrantes:
##  contrast estimate     SE df t.ratio p.value
##  A - B      0.0112 0.0194 54   0.580  0.8330
##  A - C      0.2755 0.0194 54  14.180  <.0001
##  B - C      0.2643 0.0194 54  13.600  <.0001
## 
## Metodo = Fotocuadrantes:
##  contrast estimate     SE df t.ratio p.value
##  A - B      0.0015 0.0194 54   0.080  0.9970
##  A - C      0.1398 0.0194 54   7.190  <.0001
##  B - C      0.1383 0.0194 54   7.110  <.0001
## 
## Results are averaged over the levels of: Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates

Plot

Substrate_Cover<- ggplot(General.data) + facet_grid(~Transecto)+
  MyTheme+ scale_shape_manual(values=c(21,22,25))+ 
  stat_summary(aes(x=Metodo, y=Sustrato, fill=Metodo),
                 fun.data = "mean_cl_boot", geom = "bar", 
                 position=position_dodge(width=0.8), alpha=0.7)+
  
  stat_summary(aes(x=Metodo, y=Sustrato, colour=Metodo),
                 fun.data = "mean_cl_boot", geom = "errorbar", 
                 position=position_dodge(width=0.8))+
  geom_jitter( aes (x=Metodo, y=Sustrato, shape=Buzo, fill=Metodo, group=Buzo))+
  scale_y_continuous(limits = c(0,80),
                      expand = c(0.03, 0.3),
                      breaks = seq(0, 100, 20),
                      name=expression("Cobertura de sustrato (%)"))
Substrate_Cover

4. Coral richness

hist(Riqueza.data$Riqueza..S.)

Modelo Gaussiano

Richness_model<-lm(Riqueza..S. ~  1 + Metodo + Buzo +
                          Zona + Sitio%in%Zona + 
                          Metodo*Buzo +
                          Metodo*Zona + Buzo*Zona +
                          Metodo*Buzo*Zona + 
                          Metodo*Sitio%in%Zona +
                          Buzo*Sitio%in%Zona + 
                          Metodo*Buzo*Sitio%in%Zona,
                          data = Riqueza.data)

 # summary(Richness_model)                          
  anova(Richness_model)
  par(mfrow=c(1,2))
  plot(Richness_model)

  par(mfrow=c(1,1))
  
  Comp7<-emmeans(Richness_model, ~Metodo|Zona)
  pairs(Comp7, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate    SE df t.ratio p.value
##  Cadenas - Cuadrantes          -0.667 0.195 54  -3.420  0.0030
##  Cadenas - Fotocuadrantes      -1.611 0.195 54  -8.260  <.0001
##  Cuadrantes - Fotocuadrantes   -0.944 0.195 54  -4.840  <.0001
## 
## Zona = Talud:
##  contrast                    estimate    SE df t.ratio p.value
##  Cadenas - Cuadrantes           0.056 0.195 54   0.280  0.9560
##  Cadenas - Fotocuadrantes      -1.333 0.195 54  -6.830  <.0001
##  Cuadrantes - Fotocuadrantes   -1.389 0.195 54  -7.120  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp8<-emmeans(Richness_model, ~Buzo|Metodo)
  pairs(Comp8, adj="tukey")   
## Metodo = Cadenas:
##  contrast estimate    SE df t.ratio p.value
##  A - B       0.250 0.239 54   1.050  0.5510
##  A - C       0.083 0.239 54   0.350  0.9350
##  B - C      -0.167 0.239 54  -0.700  0.7660
## 
## Metodo = Cuadrantes:
##  contrast estimate    SE df t.ratio p.value
##  A - B       0.333 0.239 54   1.390  0.3510
##  A - C       0.333 0.239 54   1.390  0.3510
##  B - C       0.000 0.239 54   0.000  1.0000
## 
## Metodo = Fotocuadrantes:
##  contrast estimate    SE df t.ratio p.value
##  A - B       0.583 0.239 54   2.440  0.0460
##  A - C       1.583 0.239 54   6.630  <.0001
##  B - C       1.000 0.239 54   4.180  <.0001
## 
## Results are averaged over the levels of: Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates

Modelo Poisson

Richness_model_2<-glm(Riqueza..S. ~  1 + Metodo + Buzo +
                          Zona + Sitio%in%Zona + 
                          Metodo*Buzo +
                          Metodo*Zona + Buzo*Zona +
                          Metodo*Buzo*Zona + 
                          Metodo*Sitio%in%Zona +
                          Buzo*Sitio%in%Zona + 
                          Metodo*Buzo*Sitio%in%Zona,
                          data = Riqueza.data, 
                          family = poisson(link = "log"))
  
  
  # summary(Richness_model)                          
  anova(Richness_model_2)
  par(mfrow=c(1,2))
  plot(Richness_model_2)

  par(mfrow=c(1,1))
  
  Comp7<-emmeans(Richness_model_2, ~Metodo|Zona)
  pairs(Comp7, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate    SE  df z.ratio p.value
##  Cadenas - Cuadrantes          -0.301 0.225 Inf  -1.337  0.3740
##  Cadenas - Fotocuadrantes      -0.564 0.215 Inf  -2.621  0.0240
##  Cuadrantes - Fotocuadrantes   -0.263 0.196 Inf  -1.342  0.3720
## 
## Zona = Talud:
##  contrast                    estimate    SE  df z.ratio p.value
##  Cadenas - Cuadrantes           0.049 0.237 Inf   0.207  0.9770
##  Cadenas - Fotocuadrantes      -0.489 0.209 Inf  -2.340  0.0500
##  Cuadrantes - Fotocuadrantes   -0.538 0.213 Inf  -2.523  0.0310
## 
## Results are averaged over the levels of: Buzo, Sitio 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp8<-emmeans(Richness_model_2, ~Buzo|Metodo)
  pairs(Comp8, adj="tukey")   
## Metodo = Cadenas:
##  contrast estimate    SE  df z.ratio p.value
##  A - B       0.093 0.291 Inf   0.320  0.9450
##  A - C       0.030 0.288 Inf   0.106  0.9940
##  B - C      -0.063 0.293 Inf  -0.215  0.9750
## 
## Metodo = Cuadrantes:
##  contrast estimate    SE  df z.ratio p.value
##  A - B       0.138 0.272 Inf   0.506  0.8680
##  A - C       0.133 0.272 Inf   0.490  0.8760
##  B - C      -0.005 0.281 Inf  -0.017  1.0000
## 
## Metodo = Fotocuadrantes:
##  contrast estimate    SE  df z.ratio p.value
##  A - B       0.164 0.207 Inf   0.793  0.7070
##  A - C       0.483 0.228 Inf   2.118  0.0860
##  B - C       0.318 0.236 Inf   1.346  0.3700
## 
## Results are averaged over the levels of: Sitio, Zona 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Plot

Coral_Richness<- ggplot(Riqueza.data) + facet_grid(~Transecto)+
  MyTheme+ scale_shape_manual(values=c(21,22,25))+ 
  stat_summary(aes(x=Metodo, y=Riqueza..S.,
                   fill=Metodo, group=Metodo),
                 fun.data = "mean_cl_boot", geom = "bar", 
                 position=position_dodge(width=0.8), alpha=0.7)+
   stat_summary(aes(x=Metodo, y=Riqueza..S., group=Metodo, color=Metodo),
                 fun.data = "mean_cl_boot", geom = "errorbar", 
                 position=position_dodge(width=0.8))+
   geom_point(aes (x=Metodo, y=Riqueza..S., shape=Buzo, 
                   fill=Metodo), position = position_dodge(0.8))+
   #geom_jitter( aes (x=Metodo, y=Riqueza..S., shape=Buzo, 
  #                   fill=Metodo, group=Metodo))+
    scale_y_continuous(limits = c(0,6),
                      expand = c(0.01, 0.01),
                      breaks = seq(0, 6, 1),
                      name=expression("Riqueza de corales (S)"))

Coral_Richness 

Coral_Richness<-Coral_Richness +
  theme(legend.position="none",
        strip.background = element_blank(),
        strip.text.x = element_blank())

5. Time

Model diving time

Time_model_underwater<-lm(Minutos_campo ~  1 + Metodo + Buzo + Buzo*Metodo +
                          Zona + Sitio%in%Zona + 
                          Zona*Metodo + Zona*Buzo +
                          Zona*Metodo*Buzo + 
                          Sitio%in%Zona*Metodo +
                          Sitio%in%Zona*Buzo + 
                          Sitio%in%Zona*Metodo*Buzo,
                          data = Time.data)

 # summary(Time_model_underwater)                          
  anova(Time_model_underwater)
  par(mfrow=c(1,2))
  plot(Time_model_underwater)

  par(mfrow=c(1,1))
  
  Comp9<-emmeans(Time_model_underwater, ~Metodo)
  Comp9
##  Metodo         emmean   SE df lower.CL upper.CL
##  Cadenas          16.6 1.39 54     13.9     19.4
##  Cuadrantes       19.4 1.39 54     16.6     22.2
##  Fotocuadrantes   21.7 1.39 54     18.9     24.5
## 
## Results are averaged over the levels of: Buzo, Sitio, Zona 
## Confidence level used: 0.95
  pairs(Comp9, adj="tukey")
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -2.75 1.96 54  -1.403  0.3470
##  Cadenas - Fotocuadrantes       -5.08 1.96 54  -2.593  0.0320
##  Cuadrantes - Fotocuadrantes    -2.33 1.96 54  -1.190  0.4640
## 
## Results are averaged over the levels of: Buzo, Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp10<-emmeans(Time_model_underwater, ~Metodo|Zona)
  pairs(Comp10, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -4.83 2.77 54  -1.743  0.1990
##  Cadenas - Fotocuadrantes       -4.56 2.77 54  -1.643  0.2370
##  Cuadrantes - Fotocuadrantes     0.28 2.77 54   0.100  0.9940
## 
## Zona = Talud:
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -0.67 2.77 54  -0.240  0.9690
##  Cadenas - Fotocuadrantes       -5.61 2.77 54  -2.024  0.1160
##  Cuadrantes - Fotocuadrantes    -4.94 2.77 54  -1.783  0.1850
## 
## Results are averaged over the levels of: Buzo, Sitio 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp11<-emmeans(Time_model_underwater, ~Buzo|Metodo)
  pairs(Comp11, adj="tukey") 
## Metodo = Cadenas:
##  contrast estimate  SE df t.ratio p.value
##  A - B        3.17 3.4 54   0.932  0.6220
##  A - C        2.92 3.4 54   0.859  0.6680
##  B - C       -0.25 3.4 54  -0.074  0.9970
## 
## Metodo = Cuadrantes:
##  contrast estimate  SE df t.ratio p.value
##  A - B        2.00 3.4 54   0.589  0.8270
##  A - C       -2.42 3.4 54  -0.712  0.7580
##  B - C       -4.42 3.4 54  -1.301  0.4010
## 
## Metodo = Fotocuadrantes:
##  contrast estimate  SE df t.ratio p.value
##  A - B       -6.25 3.4 54  -1.840  0.1660
##  A - C        3.58 3.4 54   1.055  0.5460
##  B - C        9.83 3.4 54   2.896  0.0150
## 
## Results are averaged over the levels of: Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates

Model proccessing time

Time_model_lab<-lm(Minutos_lab ~  1 + Metodo + Buzo + Buzo*Metodo +
                          Zona + Sitio%in%Zona + 
                          Zona*Metodo + Zona*Buzo +
                          Zona*Metodo*Buzo + 
                          Sitio%in%Zona*Metodo +
                          Sitio%in%Zona*Buzo + 
                          Sitio%in%Zona*Metodo*Buzo,
                          data = Time.data)

 # summary(Time_model_lab)                          
  anova(Time_model_lab)
  par(mfrow=c(1,2))
  plot(Time_model_lab)

  par(mfrow=c(1,1))
  
  Comp12<-emmeans(Time_model_lab, ~Metodo)
  Comp12
##  Metodo         emmean    SE df lower.CL upper.CL
##  Cadenas          7.61 0.625 54     6.36      8.9
##  Cuadrantes       8.81 0.625 54     7.55     10.1
##  Fotocuadrantes  30.53 0.625 54    29.27     31.8
## 
## Results are averaged over the levels of: Buzo, Sitio, Zona 
## Confidence level used: 0.95
  pairs(Comp12, adj="tukey")
##  contrast                    estimate    SE df t.ratio p.value
##  Cadenas - Cuadrantes           -1.19 0.884 54  -1.350  0.3740
##  Cadenas - Fotocuadrantes      -22.92 0.884 54 -25.920  <.0001
##  Cuadrantes - Fotocuadrantes   -21.72 0.884 54 -24.570  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp13<-emmeans(Time_model_lab, ~Metodo|Zona)
  pairs(Comp13, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -1.28 1.25 54  -1.020  0.5660
##  Cadenas - Fotocuadrantes      -19.83 1.25 54 -15.860  <.0001
##  Cuadrantes - Fotocuadrantes   -18.56 1.25 54 -14.840  <.0001
## 
## Zona = Talud:
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -1.11 1.25 54  -0.890  0.6500
##  Cadenas - Fotocuadrantes      -26.00 1.25 54 -20.790  <.0001
##  Cuadrantes - Fotocuadrantes   -24.89 1.25 54 -19.900  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp14<-emmeans(Time_model_lab, ~Buzo|Metodo)
  pairs(Comp14, adj="tukey") 
## Metodo = Cadenas:
##  contrast estimate   SE df t.ratio p.value
##  A - B        3.08 1.53 54   2.010  0.1190
##  A - C        1.08 1.53 54   0.710  0.7600
##  B - C       -2.00 1.53 54  -1.310  0.3980
## 
## Metodo = Cuadrantes:
##  contrast estimate   SE df t.ratio p.value
##  A - B        4.33 1.53 54   2.830  0.0180
##  A - C        3.50 1.53 54   2.290  0.0660
##  B - C       -0.83 1.53 54  -0.540  0.8500
## 
## Metodo = Fotocuadrantes:
##  contrast estimate   SE df t.ratio p.value
##  A - B       -5.50 1.53 54  -3.590  0.0020
##  A - C        1.67 1.53 54   1.090  0.5250
##  B - C        7.17 1.53 54   4.680  <.0001
## 
## Results are averaged over the levels of: Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates

Model total time

Time_model<-lm(Tiempo.Total.1 ~  1 + Metodo + Buzo + Buzo*Metodo +
                          Zona + Sitio%in%Zona + 
                          Zona*Metodo + Zona*Buzo +
                          Zona*Metodo*Buzo + 
                          Sitio%in%Zona*Metodo +
                          Sitio%in%Zona*Buzo + 
                          Sitio%in%Zona*Metodo*Buzo,
                          data = Time.data)

 # summary(Time_model)                          
  anova(Time_model)
  par(mfrow=c(1,2))
  plot(Time_model)

  par(mfrow=c(1,1))
  
  Comp15<-emmeans(Time_model, ~Metodo)
  Comp15
##  Metodo         emmean   SE df lower.CL upper.CL
##  Cadenas          24.3 1.74 54     20.8     27.7
##  Cuadrantes       28.2 1.74 54     24.7     31.7
##  Fotocuadrantes   52.3 1.74 54     48.8     55.7
## 
## Results are averaged over the levels of: Buzo, Sitio, Zona 
## Confidence level used: 0.95
  pairs(Comp15, adj="tukey")
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -3.94 2.46 54  -1.610  0.2521
##  Cadenas - Fotocuadrantes      -28.00 2.46 54 -11.400  <.0001
##  Cuadrantes - Fotocuadrantes   -24.06 2.46 54  -9.790  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp16<-emmeans(Time_model, ~Metodo|Zona)
  pairs(Comp16, adj="tukey")
## Zona = Planicie:
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -6.11 3.47 54  -1.760  0.1930
##  Cadenas - Fotocuadrantes      -24.39 3.47 54  -7.020  <.0001
##  Cuadrantes - Fotocuadrantes   -18.28 3.47 54  -5.260  <.0001
## 
## Zona = Talud:
##  contrast                    estimate   SE df t.ratio p.value
##  Cadenas - Cuadrantes           -1.78 3.47 54  -0.510  0.8660
##  Cadenas - Fotocuadrantes      -31.61 3.47 54  -9.100  <.0001
##  Cuadrantes - Fotocuadrantes   -29.83 3.47 54  -8.590  <.0001
## 
## Results are averaged over the levels of: Buzo, Sitio 
## P value adjustment: tukey method for comparing a family of 3 estimates
  Comp17<-emmeans(Time_model, ~Buzo|Metodo)
  pairs(Comp17, adj="tukey") 
## Metodo = Cadenas:
##  contrast estimate   SE df t.ratio p.value
##  A - B        6.25 4.26 54   1.470  0.3140
##  A - C        4.00 4.26 54   0.940  0.6180
##  B - C       -2.25 4.26 54  -0.530  0.8580
## 
## Metodo = Cuadrantes:
##  contrast estimate   SE df t.ratio p.value
##  A - B        6.33 4.26 54   1.490  0.3050
##  A - C        1.08 4.26 54   0.250  0.9650
##  B - C       -5.25 4.26 54  -1.230  0.4390
## 
## Metodo = Fotocuadrantes:
##  contrast estimate   SE df t.ratio p.value
##  A - B      -11.75 4.26 54  -2.760  0.0210
##  A - C        5.25 4.26 54   1.230  0.4390
##  B - C       17.00 4.26 54   3.990  0.0010
## 
## Results are averaged over the levels of: Sitio, Zona 
## P value adjustment: tukey method for comparing a family of 3 estimates

Plot

Method_time<- ggplot(Time.data) +
  facet_grid(~Transecto)+
  MyTheme+ scale_shape_manual(values=c(21,22,25))+ 
  
  stat_summary(aes(x=Metodo, y=Tiempo.Total.1,
                 fill=Metodo, group=Metodo),
                 fun.data = "mean_cl_boot", geom = "bar",
                 position=position_dodge(width=0.8), alpha=0.5)+
  
   stat_summary(aes(x=Metodo, y=Minutos_campo,
                   fill=Metodo, group=Metodo),
                 fun.data = "mean_cl_boot", geom = "bar",
                 position=position_dodge(width=0.8), alpha=0.7)+
   stat_summary(aes(x=Metodo, y=Tiempo.Total.1,
                    group=Metodo, color=Metodo),
                 fun.data = "mean_cl_boot", geom = "errorbar",
                 position=position_dodge(width=0.8))+
  geom_point(aes (x=Metodo, y=Tiempo.Total.1, shape=Buzo, 
                   fill=Metodo), position = position_dodge(0.8))+
   #geom_jitter( aes (x=Metodo, y=Riqueza..S., shape=Buzo, 
  #                   fill=Metodo, group=Metodo))+
  
  scale_y_continuous(limits = c(0,60),
                      expand = c(0.01, 0.01),
                      breaks = seq(0, 60, 15),
                      name=expression("Tiempo (min) "))+
  theme(legend.position="none")
Method_time

Method_time<-Method_time+ facet_grid(~Transecto, switch = "both")

Chapter figure

Model summary

library(modelsummary) # model results in table

Summary_models<-list("Corales (%)" = Coral_Cover_model, 
                       "Algas (%)" = Alga_Cover_model, 
                        "Riqueza (S)" = Richness_model_2, 
                         "Tiempo" = Time_model)
      
modelsummary(Summary_models, stars = TRUE, 
                   #statistic = c('std.error', 'p.value', 'conf.int'),
                   title = 'Seasonal and spatial models'#,
                   #coef_map=factor_order
                   )
Seasonal and spatial models
Corales (%) Algas (%) Riqueza (S) Tiempo
(Intercept) 0.364*** 1.207*** 1.099** 37.500***
(0.035) (0.052) (0.408) (7.371)
MetodoCuadrantes 0.061 −0.061 0.000 −6.000
(0.050) (0.073) (0.577) (10.424)
MetodoFotocuadrantes 0.395*** −0.587*** 0.606 12.000
(0.050) (0.073) (0.508) (10.424)
BuzoB 0.071 −0.071 −0.405 −10.000
(0.050) (0.073) (0.645) (10.424)
BuzoC 0.083 −0.809*** −0.182 −7.500
(0.050) (0.073) (0.606) (10.424)
ZonaTalud 0.668*** −0.748*** 0.154 1.000
(0.050) (0.073) (0.556) (10.424)
ZonaPlanicie × Sitio2 0.499*** −0.499*** −0.693 −11.000
(0.050) (0.073) (0.707) (10.424)
ZonaTalud × Sitio2 0.133* −0.053 −0.560 −17.500+
(0.050) (0.073) (0.627) (10.424)
ZonaPlanicie × Sitio3 0.865*** −0.865*** −0.405 −21.000*
(0.050) (0.073) (0.645) (10.424)
ZonaTalud × Sitio3 0.297*** −0.217** −0.847 −12.500
(0.050) (0.073) (0.690) (10.424)
MetodoCuadrantes × BuzoB −0.090 0.091 0.223 13.500
(0.071) (0.104) (0.885) (14.742)
MetodoFotocuadrantes × BuzoB −0.045 0.183+ 0.310 18.000
(0.071) (0.104) (0.779) (14.742)
MetodoCuadrantes × BuzoC −0.025 0.127 0.182 15.000
(0.071) (0.104) (0.837) (14.742)
MetodoFotocuadrantes × BuzoC −0.105 0.415*** −0.270 −2.000
(0.071) (0.104) (0.775) (14.742)
MetodoCuadrantes × ZonaTalud −0.072 0.062 0.134 10.000
(0.071) (0.104) (0.775) (14.742)
MetodoFotocuadrantes × ZonaTalud −0.284*** 0.447*** −0.473 2.000
(0.071) (0.104) (0.725) (14.742)
BuzoB × ZonaTalud 0.005 −0.099 0.251 −5.000
(0.071) (0.104) (0.852) (14.742)
BuzoC × ZonaTalud −0.012 0.426*** 0.182 −3.000
(0.071) (0.104) (0.808) (14.742)
MetodoCuadrantes × BuzoB × ZonaTalud 0.078 −0.116 −0.203 −3.000
(0.100) (0.147) (1.166) (20.848)
MetodoFotocuadrantes × BuzoB × ZonaTalud −0.039 −0.034 −0.038 14.000
(0.100) (0.147) (1.074) (20.848)
MetodoCuadrantes × BuzoC × ZonaTalud 0.017 −0.196 −0.470 −18.500
(0.100) (0.147) (1.130) (20.848)
MetodoFotocuadrantes × BuzoC × ZonaTalud 0.087 −0.182 0.270 11.500
(0.100) (0.147) (1.066) (20.848)
MetodoCuadrantes × ZonaPlanicie × Sitio2 −0.206** 0.206+ 0.511 4.500
(0.071) (0.104) (0.931) (14.742)
MetodoFotocuadrantes × ZonaPlanicie × Sitio2 −0.221** 0.200+ 0.375 7.000
(0.071) (0.104) (0.846) (14.742)
MetodoCuadrantes × ZonaTalud × Sitio2 0.074 −0.064 −0.134 2.000
(0.071) (0.104) (0.876) (14.742)
MetodoFotocuadrantes × ZonaTalud × Sitio2 −0.181* 0.155 0.560 17.500
(0.071) (0.104) (0.802) (14.742)
MetodoCuadrantes × ZonaPlanicie × Sitio3 −0.164* 0.164 0.405 22.000
(0.071) (0.104) (0.866) (14.742)
MetodoFotocuadrantes × ZonaPlanicie × Sitio3 −0.618*** 0.613*** 0.205 17.000
(0.071) (0.104) (0.787) (14.742)
MetodoCuadrantes × ZonaTalud × Sitio3 0.057 −0.047 −0.134 −4.500
(0.071) (0.104) (0.967) (14.742)
MetodoFotocuadrantes × ZonaTalud × Sitio3 −0.578*** 0.449*** 0.847 15.000
(0.071) (0.104) (0.852) (14.742)
BuzoB × ZonaPlanicie × Sitio2 −0.017 −0.028 0.405 3.500
(0.071) (0.104) (1.041) (14.742)
BuzoC × ZonaPlanicie × Sitio2 −0.094 0.101 0.182 1.000
(0.071) (0.104) (1.017) (14.742)
BuzoB × ZonaTalud × Sitio2 −0.037 0.085 0.154 11.000
(0.071) (0.104) (0.900) (14.742)
BuzoC × ZonaTalud × Sitio2 −0.123+ 0.023 0.000 10.500
(0.071) (0.104) (0.886) (14.742)
BuzoB × ZonaPlanicie × Sitio3 −0.066 0.059 0.405 7.500
(0.071) (0.104) (0.957) (14.742)
BuzoC × ZonaPlanicie × Sitio3 −0.170* 0.467*** 0.182 12.000
(0.071) (0.104) (0.931) (14.742)
BuzoB × ZonaTalud × Sitio3 0.120+ −0.072 0.154 15.500
(0.071) (0.104) (0.988) (14.742)
BuzoC × ZonaTalud × Sitio3 0.007 0.141 0.000 6.500
(0.071) (0.104) (0.976) (14.742)
MetodoCuadrantes × BuzoB × ZonaPlanicie × Sitio2 0.021 0.022 −0.446 −15.500
(0.100) (0.147) (1.378) (20.848)
MetodoFotocuadrantes × BuzoB × ZonaPlanicie × Sitio2 −0.065 0.087 −0.598 −3.500
(0.100) (0.147) (1.251) (20.848)
MetodoCuadrantes × BuzoC × ZonaPlanicie × Sitio2 0.082 −0.272+ −0.405 −4.000
(0.100) (0.147) (1.348) (20.848)
MetodoFotocuadrantes × BuzoC × ZonaPlanicie × Sitio2 0.089 0.057 −0.423 5.500
(0.100) (0.147) (1.281) (20.848)
MetodoCuadrantes × BuzoB × ZonaTalud × Sitio2 −0.054 0.118 −0.308 −15.000
(0.100) (0.147) (1.289) (20.848)
MetodoFotocuadrantes × BuzoB × ZonaTalud × Sitio2 −0.043 −0.116 −0.272 −21.000
(0.100) (0.147) (1.138) (20.848)
MetodoCuadrantes × BuzoC × ZonaTalud × Sitio2 −0.035 0.085 0.000 1.000
(0.100) (0.147) (1.289) (20.848)
MetodoFotocuadrantes × BuzoC × ZonaTalud × Sitio2 0.055 −0.130 −0.693 −11.500
(0.100) (0.147) (1.188) (20.848)
MetodoCuadrantes × BuzoB × ZonaPlanicie × Sitio3 0.079 −0.072 −0.223 −18.500
(0.100) (0.147) (1.271) (20.848)
MetodoFotocuadrantes × BuzoB × ZonaPlanicie × Sitio3 0.034 −0.112 −0.898 −2.500
(0.100) (0.147) (1.191) (20.848)
MetodoCuadrantes × BuzoC × ZonaPlanicie × Sitio3 0.090 −0.046 −0.182 −18.000
(0.100) (0.147) (1.238) (20.848)
MetodoFotocuadrantes × BuzoC × ZonaPlanicie × Sitio3 0.269** −0.301* −0.318 −8.000
(0.100) (0.147) (1.188) (20.848)
MetodoCuadrantes × BuzoB × ZonaTalud × Sitio3 −0.075 0.129 −0.021 −23.500
(0.100) (0.147) (1.382) (20.848)
MetodoFotocuadrantes × BuzoB × ZonaTalud × Sitio3 −0.205* 0.065 −0.405 −15.000
(0.100) (0.147) (1.217) (20.848)
MetodoCuadrantes × BuzoC × ZonaTalud × Sitio3 −0.052 0.181 0.288 4.000
(0.100) (0.147) (1.382) (20.848)
MetodoFotocuadrantes × BuzoC × ZonaTalud × Sitio3 0.015 −0.331* −0.470 −16.000
(0.100) (0.147) (1.236) (20.848)
Num.Obs. 108 108 108 108
R2 0.984 0.973 0.796
R2 Adj. 0.968 0.946 0.597
AIC −306.0 −222.6 421.1 848.0
BIC −158.5 −75.0 565.9 995.5
Log.Lik. 207.992 166.278 −156.529 −368.978
F 61.749 36.348 0.748 3.985
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Packages used

# Creates bibliography 
#knitr::write_bib(c(.packages()), "packages.bib")

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Hothorn, Torsten. 2019. TH.data: TH’s Data Archive. https://CRAN.R-project.org/package=TH.data.

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———. 2019. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.

———. 2021a. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats.

———. 2021b. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.

———. 2021c. Tidyverse: Easily Install and Load the Tidyverse. https://CRAN.R-project.org/package=tidyverse.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2021. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.

Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2021. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.

Wickham, Hadley, and Jim Hester. 2020. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.