This submission is from a master's group thesis project at The Bren School of Environmental Science & Management at the University of California, Santa Barbara, and contains the final written report and associated datasets. The graduate student researchers who completed this project include: Meghan Fletcher, Alyssa Kibbe, Grace Kumaishi, Anna Talken, and Nikole Vannest.

The California landscape has been fragmented by urban development, infrastructure, and agriculture. Maintaining connectivity between areas of wildlife habitat is important for the viability of many long-ranging species, such as the mountain lion (Puma concolor). Mountain lion populations are highly susceptible to habitat fragmentation, and face reduced access to resources and decreased genetic diversity. This study explores the habitat connectivity between the Jack and Laura Dangermond Preserve (JLDP), a 24,460 acre protected property owned by The Nature Conservancy (TNC), and neighboring protected areas to identify potential pathways of movement for mountain lions along the Central and Southern California coast. In this project, we: 1) determine regional connectivity and least cost paths between core habitats by modeling suitable mountain lion habitat, 2) estimate mountain lion habitat use and movement on JLDP by performing a site-level suitability and corridor analysis and 3) create a short film focused on highlighting our research, the role that JLDP plays in conservation, and the importance of habitat connectivity. The results of our project show that JLDP contains suitable habitat for mountain lions and may play a positive role in coastal connectivity. When considering the connectivity between JLDP and other regional protected areas, our analyses indicate that urbanized coastal regions act as barriers to mountain lions and contain pinch points that channelize movement. These results can guide TNC in developing management strategies for protecting mountain lions on JLDP and in the surrounding region.

Analyses were conducted using ArcGIS, Google Earth Engine, MaxENT, Circuitscape, and Omniscape. The project began in April 2021 and ended in June 2022.

Data was collected from open source data acquired using Google Earth Engine and Esri ArcOnline from the following sources: NASA, USGS, JPL-CalTech, Conservation Science Partners, CalFish, US Census and CalFire. It was processed using Esri ArcMap, ArcGIS Pro, Maxent, Omniscape via Jupyter Notebook and the Linkage Mapper Toolkit within ArcMap.