Protected areas (PAs) are designated spaces where human activities are
restricted to preserve critical habitats. Decision-makers are challenged with
balancing a trade-off of financial feasibility with ecological benefit when
establishing PAs. Given the long-term ramifications of these decisions and the
constantly shifting environment, it is crucial that PAs are carefully selected
with long-term viability in mind.
Using AI tools like simulation and optimization is common for designating
PAs, but current decision models are primarily linear. In this paper, we
propose a derivative-free optimization framework paired with a nonlinear
component, population viability analysis (PVA). Formulated as a mixed integer
nonlinear programming (MINLP) problem, our model allows for linear and
nonlinear inputs. Connectivity, competition, crowding, and other similar
concerns are handled by the PVA software, rather than expressed as constraints
of the optimization model. In addition, we present numerical results that serve
as a proof of concept, showing our models yield PAs with similar expected risk
to that of preserving every parcel in a habitat, but at a significantly lower
cost.
The overall goal is to promote interdisciplinary work by providing a new
mathematical programming tool for conservationists that allows for nonlinear
inputs and can be paired with existing ecological software.Comment: 8 pages, 2 figure