I pulled together a small and simple data set to have a look at turtle nesting on South Florida’s beaches, and to see if it had any relationships to some easy-to-find socioeconomic data. I looked at nesting habits for greens, loggerheads, and leatherbacks to see if you could create linear regressions with population by county, median household income, number of households by county, and per capita income by county.
The highest correlation I got was between leatherback nests and per capita income, but it bore no statistically significant relationship upon running the regression. I put my R code here with comments for people like me who are learning to play around in R.
loggerhead.nest <- sea.turtle.nests$C..carettaNEST
loggerhead.emergence <- sea.turtle.nests$C..carettaNON.NESTING.EMERGENCE
green.nest <- sea.turtle.nests$C..mydasNEST
green.emergence <- sea.turtle.nests$C..mydasNON.NESTING.EMERGENCE
leather.nest <- sea.turtle.nests$D..coriaceaNEST
leather.emergence <- sea.turtle.nests$D..coriaceaNON.NESTING.EMERGENCE
#make latin to common name labeling
plot(loggerhead.nest ~ population , data=sea.turtle.nests)
plot(loggerhead.nest ~ log(population) , data=sea.turtle.nests)
#population alone looked off, took the log
plot(green.nest ~ log(population) , data=sea.turtle.nests)
plot(leather.nest ~ log(population) , data=sea.turtle.nests)
#took plots for all three species
leather.lm <- lm(leather.nest ~ sea.turtle.nests$per.capita.income , data=sea.turtle.nests)
#Estimate Std. Error t value Pr(>|t|)
#(Intercept) -2.083e+02 2.397e+02 -0.869 0.403
#sea.turtle.nests$per.capita.income 1.227e-02 8.385e-03 1.463 0.171
#Adjusted R-squared: 0.0868
Figure 1: leatherback nests and log of population: no linear relationship
Figure 2 Loggerhead nest and log population
Figure 3 Green nests and log population
Getting better data to reflect development along the ocean front would probably help here, also looking at nesting habits through time .