Improved connectivity analysis using multiple low-cost paths to evaluate habitat for the endangered San Martin titi monkey (Callicebus oenanthe) in north-central Peru

Graph theoretic evaluations of habitat connectivity often rely upon least cost path analyses to determine the connectedness of any two habitat patches, based on an underlying cost surface. I present two improvements upon these commonly used methods. First, rather than using a single least-cost path, I use multiple low-cost paths. This allows me to differentiate between habitat patches that are connected only through a single narrow corridor, and habitat patches that are connected through a wide swath of traversable lands. The ability to identify habitat patches with greater numbers of possible routes to other patches is of interest for resiliency planning and prioritization in the face of continued habitat loss and climate change. The second improvement I present is that instead of relying upon a single cost surface to evaluate connectivity, I iteratively generate landscapes with spatially varying costs. By testing a variety of alternative cost surfaces, I can better account for spatial uncertainty in my input data. As a case study to test these methods, I am evaluating habitat connectivity for the endangered San Martin titi monkey (Callicebus oenanthe) in north-central Peru.