Consider the problem of protecting endangered species by
selecting patches of land to be used for conservation purposes.
Typically, the availability of patches changes over time, and
recommendations must be made dynamically. This is a challenging
prototypical example of a sequential optimization
problem under uncertainty in computational sustainability. Existing
techniques do not scale to problems of realistic size. In
this paper, we develop an efficient algorithm for adaptively
making recommendations for dynamic conservation planning,
and prove that it obtains near-optimal performance. We further
evaluate our approach on a detailed reserve design case study
of conservation planning for three rare species in the Pacific
Northwest of the United States.