Graph-theoretic evaluations of habitat connectivity often rely upon least-cost path analyses to evaluate connectedness of habitat patches, based on an underlying cost surface. We present two improvements upon these methods.

As a case study to test these methods, we evaluated habitat connectivity for the endangered San Martin titi monkey (Plecturocebus oenanthe) in north-central Peru, to prioritize habitat patches for conservation.

First, rather than using a single least-cost path between habitat patches, we analyzed multigraphs made up of multiple low-cost paths. This allows us to differentiate between patches connected through a single narrow corridor, and patches connected by a wide swath of traversable land. We evaluate potential movement pathways by iteratively removing paths and recomputing connectivity metrics. Second, instead of performing a sensitivity analysis by varying costs uniformly across the landscape, we generated landscapes with spatially varying costs.

This approach produced a more informative assessment of connectivity than standard graph analyses. Of the 4340 habitat patches considered across the landscape, we identified the most important 100, those frequently ranked highly through repeated network modifications, for multiple metrics and cost surfaces.

These methods represent a novel approach for assessing connectivity, better accounting for spatial configurations of habitat patches and uncertainty in cost surfaces. The ability to identify habitat patches with more possible routes to other patches is of interest for resiliency planning and prioritization in the face of continued habitat loss and climate change. These methods should be broadly applicable to conservation planning for other wildlife species.