How do I compare 5 simple linear regressions? I'm trying to compare 5 equations of simple linear regressions. All of them have the same variables: abundance of individuals vs. year.
I want to know if the slopes are significantly different from each other, the statistic that would help me with this answer is the F value, isn't it?
Here is the result of my ANOVA, which gives me blank value. This value may be due to the fact that it is only for two samples? If the answer is yes, then what do you recommend me to use?
  Res.Df    RSS Df Sum of Sq F Pr(>F)
1     12 52.263                      
2     12 68.344  0   -16.081         
3     12 21.111  0    47.233         
4     12 30.890  0    -9.779         
5     12  6.717  0    24.173    

Here is the script.
# ZONA 1 - CRIAS 
datosC <- read.table("C:/Users/usuario/OneDrive/Nueva/OneDrive/Universidad/Tesis/2 Rol social/Zona 1/C.csv", dec = ",", sep = ";", header = T)
Y <- datosC$Año
X <- datosC$C
rC <- lm(X~Y)
plot(Y, X, pch = 10, cex = 1.3, col = "black", main = "ln(NCRIAS) vs año", xlab = "Año", ylab = "Ntotal", ylim = c(0,9), xlim = c(1990,2018))
abline(rC)
summary(rC)

# ZONA 1 - HJ
datosHJ <- read.table("C:/Users/usuario/OneDrive/Nueva/OneDrive/Universidad/Tesis/2 Rol social/Zona 1/H.J.csv", dec = ",", sep = ";", header = T)
Y <- datosHJ$Año
X <- datosHJ$H.J
rHJ <- lm(X~Y)
summary(rHJ)
plot(Y, X, pch = 8, cex = 1.3, col = "blue", main = "ln(NHEMBRA-JUVENIL) vs año", xlab = "Año", ylab = "Ntotal", ylim = c(0,9), xlim = c(1990,2018))
abline(rHJ, col = "blue")

# ZONA 1 - M
datosM <- read.table("C:/Users/usuario/OneDrive/Nueva/OneDrive/Universidad/Tesis/2 Rol social/Zona 1/M.csv", dec = ",", sep = ";", header = T)
Y <- datosM$Año
X <- datosM$M
rM <- lm(X~Y)
plot(Y, X, pch = 12, cex = 1.3, col = "green", main = "ln(NMACHOS) vs año", xlab = "Año", ylab = "Ntotal", ylim = c(0,9), xlim = c(1990,2018))
abline(rM, col = "green")
summary(rM)

# ZONA 1 - MSUB
datosMSUB <- read.table("C:/Users/usuario/OneDrive/Nueva/OneDrive/Universidad/Tesis/2 Rol social/Zona 1/MSUB.csv", dec = ",", sep = ";", header = T)
Y <- datosMSUB$Año
X <- datosMSUB$MSUB
rMSUB <- lm(X~Y)
plot(Y, X, pch = 13, cex = 1.3, col = "orange", main = "ln(NMSUB) vs año", xlab = "Año", ylab = "Ntotal", ylim = c(0,9), xlim = c(1990,2018))
abline(rMSUB, col = "orange")
summary(rMSUB)

# ZONA 1 - MH
datosMH <- read.table("C:/Users/usuario/OneDrive/Nueva/OneDrive/Universidad/Tesis/2 Rol social/Zona 1/M.H.csv", dec = ",", sep = ";", header = T)
Y <- datosMH$Año
X <- datosMH$M.H
rMH <- lm(X~Y)
plot(Y, X, pch = 14, cex = 1.3, col = "red", main = "ln(NMH) vs año", xlab = "Año", ylab = "Ntotal", ylim = c(0,9), xlim = c(1990,2018))
abline(rMH, col = "red")
summary(rMH)

summary(rMH)
summary(rMSUB)
summary(rM)
summary(rC)
summary(rHJ)

anova(rMH, rMSUB, rM, rC, rHJ)

rMH$coefficients
rMSUB$coefficients
rM$coefficients
rC$coefficients
rHJ$coefficients

Linear regressions were calculated between loge numbers of individuals for each age class and years of census. A test for equality of slopes of several regression lines was performed for each colony to determine whether the relationships obtained were similar between age classes during the study period (i.e., the colony did not change its social composition despite growing in number of individuals). 
I want to know WHICH is the test for equality of slopes of several regression lines.
I added an example of one of my tables from the raw data

 A: Your ANOVA is meaningless. What you need to do is 
1) Organize your data as following:
     Year   C             Group
     1991  9.23244802       1
     1992  0                1
    ........
     2018  x.xxxxx          5

2) Fit the model 
$$ Y = \beta_0 + \beta_1 X +\beta_2G_2 + \beta_3G_3 +\beta_4G_4 +\beta_5G_5 +\beta_6G_2X +\beta_7G_3X +\beta_8G_4X +\beta_9G_5X + \epsilon$$ where $X$ = year and $G_i = 1$ if group = $i$, otherwise = 0, for $i=2,...,5$. 
3) Test the multiple null hypothesis $H_o: \beta_6= \beta_7=\beta_8=\beta_9=0$. If $H_o$ is rejected, it means at least two slopes are different. Otherwise, there is not enough evidence to say that 5 slopes are not the same.
A: You data-set is not in the best format for doing what you want to do which is to fit a full model with a new factor defining which outcome it is (call that group) and then adding the interaction between group and outcome. Some combination of the R commands cbind() and reshape() should work here but code advice is off-topic here, ask on R-help if necessary.
There is an alternative which is to take the slopes you currently have and their standard errors and do a meta-analysis and test for heterogeneity but if you are not familiar with meta-analysis that might be a step too far.
