Last time I looked at correlations between actual nest building and socioeconomic variables like population in South Florida’s coastal counties, and per capita income. This time I am looking for relationships between emergence of the same three sea turtle species (loggerhead, green, and leatherbacks noted in their latin names in the code). Emergence means coming onto the beach and returning into the ocean, laying eggs or not. I thought that counties with lower populations would see more emergence because beaches would probably be emptier at night. I ran the correlations and got pretty non-remarkable correlations for population. The correlations were mild to moderate for per capita income by county. It was 0.33 for loggerhead emergence and per capita income by county, .25 for greens, and .37 for leatherbacks. I ran the linear regressions and the result was disappointing. No stars and high p-values. I have attached the .csv and the R code in case you want to play around. Best for people learning R. As I said in my last blog entry, the data could tell us more if it looked at emergence through time and socioeconomic trends related more closely to direct, shore-front development.

Excel data file here: sea.turtle.nests

R code below:

#take cor of emergence and per capita income

cor(sea.turtle.nests$C..carettaNON.NESTING.EMERGENCE , sea.turtle.nests$per.capita.income)

cor(sea.turtle.nests$C..mydasNON.NESTING.EMERGENCE , sea.turtle.nests$per.capita.income)

cor(sea.turtle.nests$D..coriaceaNON.NESTING.EMERGENCE , sea.turtle.nests$per.capita.income)

#run linear regressions of emergence as a function of per capita income

loggerhead.reg <- lm(C..carettaNON.NESTING.EMERGENCE ~ per.capita.income , data=sea.turtle.nests)

summary(loggerhead.reg)

#Coefficients:

#Estimate Std. Error t value Pr(>|t|)

#(Intercept) -5122.3617 10669.0678 -0.480 0.641

#per.capita.income 0.4263 0.3732 1.142 0.278

#Residual standard error: 9566 on 11 degrees of freedom

#Multiple R-squared: 0.1061, Adjusted R-squared: 0.02479

#F-statistic: 1.305 on 1 and 11 DF, p-value: 0.2776

green.reg <- lm(C..mydasNON.NESTING.EMERGENCE ~ per.capita.income , data=sea.turtle.nests)

summary(green.reg)

#Estimate Std. Error t value Pr(>|t|)

#(Intercept) -475.84094 1672.65952 -0.284 0.781

#per.capita.income 0.04852 0.05851 0.829 0.425

#Residual standard error: 1500 on 11 degrees of freedom

#Multiple R-squared: 0.05885, Adjusted R-squared: -0.02671

#F-statistic: 0.6878 on 1 and 11 DF, p-value: 0.4245

leather.reg <- lm(D..coriaceaNON.NESTING.EMERGENCE ~ per.capita.income , data=sea.turtle.nests)

summary(leather.reg)

#Coefficients:

#Estimate Std. Error t value Pr(>|t|)

#(Intercept) -47.376772 57.792547 -0.820 0.430

#per.capita.income 0.002672 0.002021 1.322 0.213

#Residual standard error: 51.82 on 11 degrees of freedom

#Multiple R-squared: 0.1371, Adjusted R-squared: 0.05862

#F-statistic: 1.747 on 1 and 11 DF, p-value: 0.2131