Résumé
1. Curbing habitat loss, reducing fragmentation, and restoring
connectivity are frequent concerns of conservation planning. In this
respect, the incorporation of spatial constraints, fragmentation, and
connectivity indices into optimization procedures is an important
challenge for improving decision support. 2. Here we present a novel
optimization approach developed to accurately represent a broad range of
conservation planning questions with spatial constraints and landscape
indices. Relying on constraint programming, a technique from artificial
intelligence based on automatic reasoning, this approach provides both
constraint satisfaction and optimality guarantees. 3. We applied this
approach in a real case study to support managers of the ``Côte Oubliée --
`Woen Vùù -- Pwa Pereeù'' provincial park project, in the
biodiversity hotspot of New Caledonia. Under budget, accessibility, and
equitable allocation constraints, we identified restorable areas optimal
for reducing forest fragmentation and improving inter-patch structural
connectivity, respectively measured with the effective mesh size and the
integral index of connectivity. 4. Synthesis and applications. Our work
contributes to more effective and policy-relevant conservation planning by
providing a spatially-explicit and problem-focused optimization approach.
By allowing an exact representation of spatial constraints and landscape
indices, it can address new questions and ensure whether the solutions
will be socio-economically feasible, through optimality and satisfiability
guarantees. Our approach is generic and flexible, thus applicable to a
wide range of conservation planning problems such as ecological
restoration planning, reserve or corridor design.