Maintaining landscape connectivity is increasingly important
in wildlife conservation, especially for species experiencing
the effects of habitat loss and fragmentation. We propose a
novel approach to dynamically optimize landscape connectivity.
Our approach is based on a mixed integer program formulation,
embedding a spatial capture-recapture model that
estimates the density, space usage, and landscape connectivity
for a given species. Our method takes into account the
fact that local animal density and connectivity change dynamically
and non-linearly with different habitat protection
plans. In order to scale up our encoding, we propose a sampling
scheme via random partitioning of the search space using
parity functions. We show that our method scales to realworld
size problems and dramatically outperforms the solution
quality of an expectation maximization approach and a
sample average approximation approach.