Case 2: t=0.18, 0.19, 0.20, .., 0.29, 0.30

To investigate the significance of the juvenile survival rate, repeat the previous calculations for several different values of t.

> PP := NULL:

> for t from 0.18 to 0.30 by 0.01 do

> A[2,1] := t:

> evalm( A );

> x := x0; # initial population

> P := evalm( x ):

> for i from 1998 to 2020 do

> x := evalm( A &* x ); # another year passes

> P := augment( P, x );

> od:

>

> yr := vector( [ $1997 .. 2020 ] ):

> ptJ := zip( (x,y) -> [x,y], yr, row(P,1) ):

> ptS := zip( (x,y) -> [x,y], yr, row(P,2) ):

> ptA := zip( (x,y) -> [x,y], yr, row(P,3) ):

>

> plotJ := plot( ptJ, color=red ):

> plotS := plot( ptS, color=blue ):

> plotA := plot( ptA, color=green ):

> PP := PP, display( [ plotJ, plotS, plotA ], title=sprintf("Spotted Owl Populations with t=%a",t) ):

> od:

>

Display, as an animation, the population curves for each of the different values of the juvenile survival rate.

> display( [ PP ], insequence=true );

Notice that at some point the solution curves appear to increase during the later years in this simulation. The transition from decreasing to increasing occurs at the critical value of the juvenile survival rate.

>