AnƔlisis de datos del Informe tƩcnico final del Proyecto Sciaenidae 2018

Objetivos

El presente script estƔ diseƱado para cumplir con los siguientes objetivos:

  • Describir los estados de desarrollo ovocitario y su organizaciĆ³n en ovarios maduros de las especies de interĆ©s.

  • Estimar el tamaƱo en el cual los ovocitos son reclutados a la vitelogĆ©nesis.

  • Estimar la fecundidad y fecundidad relativa por pulso de desove para las especies de interĆ©s.

  • Verificar el nĆŗmero de pulso potenciales presentes en el ovario.

  • Determinar la talla de primera madurez de las especies analizadas.

  • Investigar la influencia de variables morfomĆ©tricas de los individuos: tamaƱo, peso y condiciĆ³n de la hembra sobre la fecundidad por pulso de desove, el tamaƱo de reclutamiento de ovocito a la vitelogĆ©nesis.

  • Comparar las variables de las estrategias reproductivas para identificar las diferencias y similitudes entre estas.

Paquetes

library(FSA)
library(magrittr)
library(dplyr)
library(lubridate)
library(car)
library(forcats)
library(cowplot)
library(ggplot2)

Bases de datos empleadas

str(Ov)
## 'data.frame':    2250 obs. of  4 variables:
##  $ Sp      : Factor w/ 5 levels "Cynoscion analis",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Estado  : Factor w/ 9 levels "CA","GVM","Hyd",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ Rec     : Factor w/ 2 levels "Previt","Vit": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Diametro: num  40.2 22.4 48.7 55.5 46.7 ...
str(Fec)
## 'data.frame':    487 obs. of  16 variables:
##  $ Sp         : Factor w/ 5 levels "Cynoscion analis",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ LT         : num  11.3 13.5 13.8 13.9 14.1 14.1 14.2 14.2 14.3 14.3 ...
##  $ LS         : num  10.7 11.4 12.2 11.9 12.2 12.2 13.3 12.3 12.2 12.5 ...
##  $ Peso       : int  28 50 48 70 55 57 54 56 55 55 ...
##  $ Peso_ev    : int  23 35 40 55 45 46 45 48 40 50 ...
##  $ P_gonad    : num  1.22 0.73 5.02 2.51 0.86 6.95 5.2 5.03 1.96 2.22 ...
##  $ FC         : num  1.94 2.03 1.83 2.61 1.96 ...
##  $ IGS        : num  5.3 2.09 12.55 4.56 1.91 ...
##  $ Fec        : num  2586 1431 11540 5626 1738 ...
##  $ Fec2       : num  2751 1918 11589 6109 1953 ...
##  $ Fec_parcial: num  2120 1961 2299 2241 2021 ...
##  $ A          : num  2356 2356 2356 2356 2356 ...
##  $ B          : num  -289 -289 -289 -289 -289 ...
##  $ ale        : int  165 487 49 483 215 481 126 -83 61 128 ...
##  $ Npulso     : num  1.62 1.67 1.77 1.69 1.63 ...
##  $ X          : Factor w/ 2 levels "","f": 1 1 1 1 1 1 1 1 1 1 ...
str(Mad)
## 'data.frame':    618 obs. of  3 variables:
##  $ Sp     : Factor w/ 5 levels "Cynoscion analis",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ LT     : num  13 13 11 12 13 14 14 14 15 15 ...
##  $ Madurez: Factor w/ 2 levels "Inmaduro","Maduro": 1 1 1 1 1 1 1 1 1 1 ...
# Ordenamos el factor Estado segpun el orden de desarrollo ovocitario
Ov_lev <- c("Pg1", "Pg2", "CA", "Vtg1", "Vtg2", "Vtg3", "Vtg4", "GVM", "Hyd")
Ov$Estado <- factor(Ov$Estado, levels = Ov_lev)

OrganizaciĆ³n del ovario

Diam_ov_sum <- Ov %>% group_by(Sp, Estado) %>% dplyr::summarize(PromDiam = mean(Diametro, 
    na.rm = TRUE), SdDiam = sd(Diametro))
Diam_ov_sum
## # A tibble: 45 x 4
## # Groups:   Sp [5]
##    Sp                        Estado PromDiam SdDiam
##    <fct>                     <fct>     <dbl>  <dbl>
##  1 Cynoscion analis          Pg1        44.4   9.31
##  2 Cynoscion analis          Pg2        50.5  13.7 
##  3 Cynoscion analis          CA         64.7  28.1 
##  4 Cynoscion analis          Vtg1       88.6  40.0 
##  5 Cynoscion analis          Vtg2      209.   57.9 
##  6 Cynoscion analis          Vtg3      345.   44.7 
##  7 Cynoscion analis          Vtg4      431.   40.1 
##  8 Cynoscion analis          GVM       490.   76.9 
##  9 Cynoscion analis          Hyd       638.   74.6 
## 10 Menticirrhus ophicephalus Pg1        29.8  11.0 
## # ... with 35 more rows
ggplot(Ov, aes(x = reorder(Estado, Diametro), y = Diametro)) + facet_grid(~Sp) + 
    geom_boxplot() + labs(x = "Estado ovocitario", y = "DiĆ”metro promedio (Āµm)") + 
    theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggplot(Ov, aes(x = Diametro, fill = Estado, colour = Estado, Shape = Estado)) + 
    facet_wrap(~Sp, nrow = 2) + geom_density(alpha = 0.1, position = "identity") + 
    scale_color_brewer(palette = "Paired") + scale_fill_brewer(palette = "Paired") + 
    labs(x = "DiĆ”metro promedio (Āµm)", y = "Frecuencia %") + theme_bw()

TamaƱo de reclutamiento a la vitelogƩnesis

lrPerc <- function(cf, p) (log(p/(1 - p)) - cf[[1]])/cf[[2]]
levels(Ov$Sp)
## [1] "Cynoscion analis"          "Menticirrhus ophicephalus"
## [3] "Paralonchurus peruanus"    "Sciaena deliciosa"        
## [5] "Stellifer minor"
# Cynoscion analis
glmRecCA <- glm(Rec ~ Diametro, data = Ov, family = binomial, subset = Sp == 
    "Cynoscion analis")
bcLRecCA <- bootCase(glmRecCA, B = 1000)
L50RecCA <- lrPerc(coef(glmRecCA), 0.5)
bL50RecCA <- apply(bcLRecCA, 1, lrPerc, p = 0.5)
L50ciRecCA <- quantile(bL50RecCA, c(0.025, 0.975))
RecCA <- c(L50RecCA, L50ciRecCA)
RecCA
##              2.5%    97.5% 
## 89.18254 81.01380 97.89742
# Menticirrhus ophicephalus
glmRecMO <- glm(Rec ~ Diametro, data = Ov, family = binomial, subset = Sp == 
    "Menticirrhus ophicephalus")
bcLRecMO <- bootCase(glmRecMO, B = 1000)
L50RecMO <- lrPerc(coef(glmRecMO), 0.5)
bL50RecMO <- apply(bcLRecMO, 1, lrPerc, p = 0.5)
L50ciRecMO <- quantile(bL50RecMO, c(0.025, 0.975))
RecMO <- c(L50RecMO, L50ciRecMO)
RecMO
##              2.5%    97.5% 
## 124.6737 121.8401 127.7982
# Paralonchurus peruanus
glmRecPP <- glm(Rec ~ Diametro, data = Ov, family = binomial, subset = Sp == 
    "Paralonchurus peruanus")
bcLRecPP <- bootCase(glmRecPP, B = 1000)
L50RecPP <- lrPerc(coef(glmRecPP), 0.5)
bL50RecPP <- apply(bcLRecPP, 1, lrPerc, p = 0.5)
L50ciRecPP <- quantile(bL50RecPP, c(0.025, 0.975))
RecPP <- c(L50RecPP, L50ciRecPP)
RecPP
##              2.5%    97.5% 
## 123.8293 116.7796 131.1347
# Sciaena deliciosa
glmRecSD <- glm(Rec ~ Diametro, data = Ov, family = binomial, subset = Sp == 
    "Sciaena deliciosa")
bcLRecSD <- bootCase(glmRecSD, B = 1000)
L50RecSD <- lrPerc(coef(glmRecSD), 0.5)
bL50RecSD <- apply(bcLRecSD, 1, lrPerc, p = 0.5)
L50ciRecSD <- quantile(bL50RecSD, c(0.025, 0.975))
RecSD <- c(L50RecSD, L50ciRecSD)
RecSD
##                2.5%     97.5% 
##  98.77860  92.14284 105.67448
# Stellifer minor
glmRecSM <- glm(Rec ~ Diametro, data = Ov, family = binomial, subset = Sp == 
    "Stellifer minor")
bcLRecSM <- bootCase(glmRecSM, B = 1000)
L50RecSM <- lrPerc(coef(glmRecSM), 0.5)
bL50RecSM <- apply(bcLRecSM, 1, lrPerc, p = 0.5)
L50ciRecSM <- quantile(bL50RecSM, c(0.025, 0.975))
RecSM <- c(L50RecSM, L50ciRecSM)
RecSM
##              2.5%    97.5% 
## 104.1730  94.5562 113.0375
RecTotal <- rbind(RecCA, RecMO, RecPP, RecSD, RecSM)
RecTotal <- as.data.frame(RecTotal)
RecTotal
##              V1      2.5%     97.5%
## RecCA  89.18254  81.01380  97.89742
## RecMO 124.67367 121.84014 127.79820
## RecPP 123.82931 116.77963 131.13472
## RecSD  98.77860  92.14284 105.67448
## RecSM 104.17305  94.55620 113.03753
binomial_smooth <- function(...) {
    geom_smooth(method = "glm", method.args = list(family = "binomial"), ...)
}
ggplot(Ov, aes(Diametro, as.numeric(Rec) - 1)) + facet_wrap(~Sp, nrow = 1, scales = "free_x") + 
    binomial_smooth() + geom_point(position = position_jitter(height = 0.03, 
    width = 0), shape = 21) + xlab("DiĆ”metro del ovocito (Āµm)") + ylab("Probabilidad de ovocito vitelogĆ©nico") + 
    theme_bw()

Fecundidad y fecundidad relativa por pulso

Fec_sum <- Fec %>% group_by(Sp) %>% dplyr::summarize(PromF = mean(Fec2, na.rm = TRUE), 
    SdF = sd(Fec2), PromFP = mean(Fec_parcial, na.rm = TRUE), SdFP = sd(Fec_parcial))
Fec_sum
## # A tibble: 5 x 5
##   Sp                         PromF    SdF PromFP  SdFP
##   <fct>                      <dbl>  <dbl>  <dbl> <dbl>
## 1 Cynoscion analis          17632. 10763.  1922.  29.1
## 2 Menticirrhus ophicephalus 14320. 12963.  2610. 538. 
## 3 Paralonchurus peruanus     6426.  4828.  1470. 265. 
## 4 Sciaena deliciosa          6813.  5875.  1235. 279. 
## 5 Stellifer minor            8132.  3921.  2251. 100.
F_A <- ggplot(Fec_sum, aes(x = Sp, y = PromF, group = Sp)) + geom_line() + geom_point(size = 1, 
    shape = 21, fill = "white") + geom_errorbar(aes(ymin = PromF - SdF, ymax = PromF + 
    SdF), width = 0.2, position = position_dodge(0.05)) + labs(x = NULL, y = "Fecundidad (NĀ° de ovocitos)") + 
    theme_bw() + theme(axis.text.x = element_text(angle = 60, hjust = 1))

F_B <- ggplot(Fec_sum, aes(x = Sp, y = PromFP, group = Sp)) + geom_line() + 
    geom_point(size = 1, shape = 21, fill = "white") + geom_errorbar(aes(ymin = PromFP - 
    SdFP, ymax = PromFP + SdFP), width = 0.2, position = position_dodge(0.05)) + 
    labs(x = NULL, y = "Fecundidad parcial (NĀ° ov/g)") + theme_bw() + theme(axis.text.x = element_text(angle = 60, 
    hjust = 1))

plot_grid(F_A, F_B, labels = "AUTO", nrow = 1, align = "v")

NĆŗmero de pulsos potenciales en el ovario

Pul_sum <- Fec %>% group_by(Sp) %>% dplyr::summarize(PromPul = mean(Npulso, 
    na.rm = TRUE), SdPul = sd(Npulso))
Pul_sum
## # A tibble: 5 x 3
##   Sp                        PromPul  SdPul
##   <fct>                       <dbl>  <dbl>
## 1 Cynoscion analis             2.17 0.173 
## 2 Menticirrhus ophicephalus    1.64 0.674 
## 3 Paralonchurus peruanus       2.59 0.341 
## 4 Sciaena deliciosa            3.17 0.807 
## 5 Stellifer minor              1.72 0.0646
ggplot(Pul_sum, aes(x = Sp, y = PromPul, group = Sp)) + geom_line() + geom_point(size = 1, 
    shape = 21, fill = "white") + geom_errorbar(aes(ymin = PromPul - SdPul, 
    ymax = PromPul + SdPul), width = 0.2, position = position_dodge(0.05)) + 
    labs(x = NULL, y = "NĀ° de pulsos potenciales") + theme_bw() + theme(axis.text.x = element_text(angle = 60, 
    hjust = 1))

Talla de primera madurez

Modelamos la madurez:

lrPerc <- function(cf, p) (log(p/(1 - p)) - cf[[1]])/cf[[2]]
levels(Mad$Sp)
## [1] "Cynoscion analis"          "Menticirrhus ophicephalus"
## [3] "Paralonchurus peruanus"    "Sciaena deliciosa"        
## [5] "Stellifer minor"
# Cynoscion analis
glmMadCA <- glm(Madurez ~ LT, data = Mad, family = binomial, subset = Sp == 
    "Cynoscion analis")
coef(glmMadCA)
## (Intercept)          LT 
## -20.6776505   0.9274051
bcLMadCA <- bootCase(glmMadCA, B = 1000)
cbind(Ests = coef(glmMadCA), confint(bcLMadCA))
##                    Ests     95% LCI    95% UCI
## (Intercept) -20.6776505 -35.7073654 -15.224538
## LT            0.9274051   0.6992819   1.551827
L50CA <- lrPerc(coef(glmMadCA), 0.5)
bL50CA <- apply(bcLMadCA, 1, lrPerc, p = 0.5)
L50ciCA <- quantile(bL50CA, c(0.025, 0.975))
MadCA <- c(L50CA, L50ciCA)
MadCA
##              2.5%    97.5% 
## 22.29625 21.38128 23.02923
# Menticirrhus ophicephalus
glmMadMO <- glm(Madurez ~ LT, data = Mad, family = binomial, subset = Sp == 
    "Menticirrhus ophicephalus")
bcLMadMO <- bootCase(glmMadMO, B = 1000)
cbind(Ests = coef(glmMadMO), confint(bcLMadMO))
##                   Ests     95% LCI    95% UCI
## (Intercept) -27.181064 -47.0125274 -19.069298
## LT            1.364313   0.9700754   2.316431
L50MO <- lrPerc(coef(glmMadMO), 0.5)
bL50MO <- apply(bcLMadMO, 1, lrPerc, p = 0.5)
L50ciMO <- quantile(bL50MO, c(0.025, 0.975))
MadMO <- c(L50MO, L50ciMO)
MadMO
##              2.5%    97.5% 
## 19.92290 19.33933 20.42198
# Paralonchurus peruanus
glmMadPP <- glm(Madurez ~ LT, data = Mad, family = binomial, subset = Sp == 
    "Paralonchurus peruanus")
bcLMadPP <- bootCase(glmMadPP, B = 1000)
cbind(Ests = coef(glmMadPP), confint(bcLMadPP))
##                    Ests      95% LCI   95% UCI
## (Intercept) -14.5396244 -105.6886761 -9.933792
## LT            0.9081075    0.6451994  5.984630
L50PP <- lrPerc(coef(glmMadPP), 0.5)
bL50PP <- apply(bcLMadPP, 1, lrPerc, p = 0.5)
L50ciPP <- quantile(bL50PP, c(0.025, 0.975))
MadPP <- c(L50PP, L50ciPP)
MadPP
##              2.5%    97.5% 
## 16.01091 14.61687 17.63844
# Sciaena deliciosa
glmMadSD <- glm(Madurez ~ LT, data = Mad, family = binomial, subset = Sp == 
    "Sciaena deliciosa")
bcLMadSD <- bootCase(glmMadSD, B = 1000)
cbind(Ests = coef(glmMadSD), confint(bcLMadSD))
##                   Ests     95% LCI    95% UCI
## (Intercept) -24.518104 -72.0540325 -16.309624
## LT            1.433693   0.9639085   4.241757
L50SD <- lrPerc(coef(glmMadSD), 0.5)
bL50SD <- apply(bcLMadSD, 1, lrPerc, p = 0.5)
L50ciSD <- quantile(bL50SD, c(0.025, 0.975))
MadSD <- c(L50SD, L50ciSD)
MadSD
##              2.5%    97.5% 
## 17.10136 16.37955 17.78957
# Stellifer minor
glmMadSM <- glm(Madurez ~ LT, data = Mad, family = binomial, subset = Sp == 
    "Stellifer minor")
bcLMadSM <- bootCase(glmMadSM, B = 1000)
cbind(Ests = coef(glmMadMO), confint(bcLMadMO))
##                   Ests     95% LCI    95% UCI
## (Intercept) -27.181064 -47.0125274 -19.069298
## LT            1.364313   0.9700754   2.316431
L50SM <- lrPerc(coef(glmMadSM), 0.5)
bL50SM <- apply(bcLMadSM, 1, lrPerc, p = 0.5)
L50ciSM <- quantile(bL50SM, c(0.025, 0.975))
MadSM <- c(L50SM, L50ciSM)
MadSM
##              2.5%    97.5% 
## 13.03965 12.04671 13.90206
MadTotal <- rbind(MadCA, MadMO, MadPP, MadSD, MadSM)
MadTotal <- as.data.frame(MadTotal)
MadTotal
##             V1     2.5%    97.5%
## MadCA 22.29625 21.38128 23.02923
## MadMO 19.92290 19.33933 20.42198
## MadPP 16.01091 14.61687 17.63844
## MadSD 17.10136 16.37955 17.78957
## MadSM 13.03965 12.04671 13.90206
binomial_smooth <- function(...) {
    geom_smooth(method = "glm", method.args = list(family = "binomial"), ...)
}
ggplot(Mad, aes(LT, as.numeric(Madurez) - 1)) + facet_wrap(~Sp, nrow = 1, scales = "free_x") + 
    binomial_smooth() + geom_point(position = position_jitter(height = 0.03, 
    width = 0), shape = 21) + xlab("Longitud total (cm)") + ylab("Probabilidad de individuo maduro") + 
    theme_bw()

AlometrĆ­a y fecundidad

LM.L_F.SM <- lm(IGS ~ FC, data = Fec, subset = Sp == "Stellifer minor")
LM.L_F.SM
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Stellifer minor")
## 
## Coefficients:
## (Intercept)           FC  
##      5.4182       0.5726
summary(LM.L_F.SM)
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Stellifer minor")
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.9973 -1.8997 -0.5665  1.0224 18.8146 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   5.4182     2.3124   2.343   0.0205 *
## FC            0.5726     1.2803   0.447   0.6554  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.019 on 143 degrees of freedom
## Multiple R-squared:  0.001397,   Adjusted R-squared:  -0.005586 
## F-statistic:   0.2 on 1 and 143 DF,  p-value: 0.6554
LM.L_F.PP <- lm(IGS ~ FC, data = Fec, subset = Sp == "Paralonchurus peruanus")
LM.L_F.PP
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Paralonchurus peruanus")
## 
## Coefficients:
## (Intercept)           FC  
##      -14.83        18.40
summary(LM.L_F.PP)
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Paralonchurus peruanus")
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4528 -2.1522 -0.4905  0.9725 19.5768 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -14.826      4.941  -3.000  0.00434 ** 
## FC            18.399      4.295   4.283 9.28e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.648 on 46 degrees of freedom
## Multiple R-squared:  0.2851, Adjusted R-squared:  0.2696 
## F-statistic: 18.35 on 1 and 46 DF,  p-value: 9.277e-05
LM.L_F.MO <- lm(IGS ~ FC, data = Fec, subset = Sp == "Menticirrhus ophicephalus")
LM.L_F.MO
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Menticirrhus ophicephalus")
## 
## Coefficients:
## (Intercept)           FC  
##       1.591        1.758
summary(LM.L_F.MO)
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Menticirrhus ophicephalus")
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7045 -1.2231 -0.3738  0.7218 10.0608 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    1.591      1.188   1.339   0.1822  
## FC             1.758      1.060   1.658   0.0991 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.882 on 182 degrees of freedom
## Multiple R-squared:  0.01487,    Adjusted R-squared:  0.009461 
## F-statistic: 2.748 on 1 and 182 DF,  p-value: 0.0991
LM.L_F.CA <- lm(IGS ~ FC, data = Fec, subset = Sp == "Cynoscion analis")
LM.L_F.CA
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Cynoscion analis")
## 
## Coefficients:
## (Intercept)           FC  
##       2.001        3.105
summary(LM.L_F.CA)
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Cynoscion analis")
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3279 -1.6646 -0.4391  1.8735  4.0865 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    2.001      1.900   1.053   0.2970  
## FC             3.105      1.791   1.733   0.0887 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.137 on 54 degrees of freedom
## Multiple R-squared:  0.05271,    Adjusted R-squared:  0.03517 
## F-statistic: 3.005 on 1 and 54 DF,  p-value: 0.08873
LM.L_F.SD <- lm(IGS ~ FC, data = Fec, subset = Sp == "Sciaena deliciosa")
LM.L_F.SD
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Sciaena deliciosa")
## 
## Coefficients:
## (Intercept)           FC  
##     -0.8975       3.6347
summary(LM.L_F.SD)
## 
## Call:
## lm(formula = IGS ~ FC, data = Fec, subset = Sp == "Sciaena deliciosa")
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6671 -0.6959 -0.1068  0.6996  3.7583 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.8975     1.4492  -0.619  0.53843   
## FC            3.6347     1.1038   3.293  0.00179 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.376 on 52 degrees of freedom
## Multiple R-squared:  0.1725, Adjusted R-squared:  0.1566 
## F-statistic: 10.84 on 1 and 52 DF,  p-value: 0.001788
Alo_L_A <- ggplot(Fec, aes(x = LT, y = Fec2, group = Sp, colour = Sp)) + # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Longitud total (cm)", 
    y = "Fecundidad (NĀ° ov)") + scale_color_brewer(palette = "Paired") + theme_bw() + 
    theme(legend.position = "none")

Alo_L_B <- ggplot(Fec, aes(x = LT, y = Fec_parcial, group = Sp, colour = Sp)) + 
    # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Longitud total (cm)", 
    y = "Fecundidad parcial (NĀ° ov/g)") + scale_color_brewer(palette = "Paired") + 
    theme_bw() + theme(legend.position = "none")


Alo_L_C <- ggplot(Fec, aes(x = LT, y = IGS, group = Sp, colour = Sp)) + # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Longitud total (cm)", 
    y = "IGS") + scale_color_brewer(palette = "Paired") + theme_bw() + theme(legend.position = c(0.65, 
    0.75))

plot_grid(Alo_L_A, Alo_L_B, Alo_L_C, labels = "AUTO", nrow = 1, align = "v")

Alo_P_A <- ggplot(Fec, aes(x = Peso, y = Fec2, group = Sp, colour = Sp)) + # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Peso (g)", y = "Fecundidad (NĀ° ov)") + 
    scale_color_brewer(palette = "Paired") + theme_bw() + theme(legend.position = "none")

Alo_P_B <- ggplot(Fec, aes(x = Peso, y = Fec_parcial, group = Sp, colour = Sp)) + 
    # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Peso (g)", y = "Fecundidad parcial (NĀ° ov/g)") + 
    scale_color_brewer(palette = "Paired") + theme_bw() + theme(legend.position = "none")


Alo_P_C <- ggplot(Fec, aes(x = Peso, y = IGS, group = Sp, colour = Sp)) + # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Peso (g)", y = "IGS") + 
    scale_color_brewer(palette = "Paired") + theme_bw() + theme(legend.position = c(0.65, 
    0.75))

plot_grid(Alo_P_A, Alo_P_B, Alo_P_C, labels = "AUTO", nrow = 1, align = "v")

Alo_C_A <- ggplot(Fec, aes(x = FC, y = Fec2, group = Sp, colour = Sp)) + # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Factor de condiciĆ³n", 
    y = "Fecundidad (NĀ° ov)") + scale_color_brewer(palette = "Paired") + theme_bw() + 
    theme(legend.position = "none")

Alo_C_B <- ggplot(Fec, aes(x = FC, y = Fec_parcial, group = Sp, colour = Sp)) + 
    # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Factor de condiciĆ³n", 
    y = "Fecundidad parcial (NĀ° ov/g)") + scale_color_brewer(palette = "Paired") + 
    theme_bw() + theme(legend.position = "none")


Alo_C_C <- ggplot(Fec, aes(x = FC, y = IGS, group = Sp, colour = Sp)) + # facet_wrap(~Sp, scales = 'free', nrow = 1)+
geom_point(shape = 21) + geom_smooth(method = lm) + labs(x = "Factor de condiciĆ³n", 
    y = "IGS") + scale_color_brewer(palette = "Paired") + theme_bw() + theme(legend.position = c(0.65, 
    0.75))

plot_grid(Alo_C_A, Alo_C_B, Alo_C_C, labels = "AUTO", nrow = 1, align = "v")

Fernando Tapia Vilchez . 29 de Febrero del 2015. ƚltima actualizaciĆ³n: 2019-06-04