[R] Identifying breakpoints/inflection points?

[Charlotte Chang]

Hello!
I have a dataset with the following two vectors:

year<-c(1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009)

piproute<-c(0.733333333,0.945945946,1.886363636,1.607843137,4.245614035,3.175675676,2.169014085,2,2.136363636,2.65625,2.080645161,2.114754098,2.090909091,3.012195122,2.935897436,2.592105263,1.075757576,1.210526316,1,1.1875,1.903614458,1.385542169,1.788990826,1.163793103,1.558558559,1.595238095,1.758333333,1.858267717,2.169117647,1.403225806,2.859375,3.236220472,2.054263566,3.854166667,1.812080537,2.708029197,2.75862069,2.625954198,4.540740741,3.686567164,2.8,2.968253968,3.517730496)

Pipits is the response variable (it is the number of birds counted at
each survey site in each year) and year is the independent variable.
If you plot it in R (plot(year,piproute,pch=19)), you'll see that the
relationship looks like a quintic polynomial.

Initially I was trying to fit this curve using an iterative equation,
but it's not working. I suspect that the curve-fitting equation itself
is inappropriate (it's a modified version of the logistic growth
equation). Now what I'd like to do is identify the 3 break/inflection
points in the population trend. That way, I can make an argument that
the break points corresponded to shifts in government policy with
respect to land use management. I've been looking at the segmented
package, and initially I looked at change.pt test in the circ.stats
package (which is inappropriate b/c my data is not amenable to
circular statistical analysis). Any ideas on what I could do would be
appreciated!

Thank you!

-Charlotte