Lets start with a toy example system of equations
\begin{align} \frac{dx}{dt}&=x(1-x)-xy\\ \frac{dy}{dt}&=-y \end{align}
I'd like to show $(1,0)$ has a basin of attraction that includes all of $B:=\{(x,y): 0<x<1, y\geq 0 \}$. The x-axis is obviously attracting on $B$, given $dy/dt=-y$. And on the x-axis, the point x=1 attracts all positive $x$. How can we prove that all trajectories with initial conditions in $B$ flow to $(1,0)$. And does this concept generalize to systems as follows?
The general question in 2D, given a dynamical system
\begin{align} \frac{dx}{dt}=f(x,y)\\ \frac{dy}{dt}=g(x,y) \end{align}
with a globally attracting curve $C:=\{(x,y): y=h(x)\}$, such that all points $(x,y)$ not on $C$ approach $C$. My question is, if there exists a unique fixed point $(x^*,y^*)$ on $C$ such that it attracts all points on the curve $C$, can we call that fixed point globally stable. Intuitively it seems like it has to be. I'm wondering if anyone has a proof for this proposition or a counterexample?
So for the example, one need only choose the Lyapunov function
\begin{equation} V(x,y)=y^2+(x+y-1)^2 \end{equation}
Clearly, $V(k,0)=0$ and $V(x,y)>0$ for all $(x,y)\neq(k,0)$ in $B$. Note
\begin{align} \dot{V}(x,y)&=2y\dot{y}+2(x+y-1)\dot{x}+2(x+y-1)\dot{y}\\ \end{align}
Case (1) $x>y-1$: the first and third therm are always negative because $\dot{y}$ is negative. The second term is negative because $x>y-1$ only includes points above the $dx/dt$ nullcline and hence, $\dot{x}>0$.
Case (2) $x<y-1$: The second term is negative because $x<y-1$ means $(x+y-1)<0$ sincle it also means $(x,y)$ is below the $dx/dt$ nullcline and hence, $\dot{x}>0$. Note that the third term is positive. However because $|x+y-1|<y$, (note $0<x \leq 1$ required to be in $B$) it is less positive than the first term is negative.
Case (3) $x=y-1$: $\dot{V}=-2y^2<0$.
Therefore $(k,0)$ attracks all trajectories on $B$. Here I exploited the fact that trajectories always flow down to the x axis, and the fact that in the x direction they flow towards the $dx/dt$ nullcline, which is why I chose $[x-x_{nullcline}(y)]^2$ in the Lyapunov function.
Unfortunately I don't think you will always be able to exploit this, so it doesn't help with the general proposition, but it might provide some insight.