White abalone (Haliotis sorenseni) supported an intense commercial fishery in southern California during the 1970s, which closed in 1996. In 2001 white abalone was listed under the Endangered Species Act (ESA), and due to their high risk of extinction, National Oceanic Atmosphere Administration (NOAA) identified the species as a "Species in the Spotlight" in 2016. Efforts are underway to develop a conservation hatchery and outplanting program to recover the species. To inform outplanting efforts, I modeled broad-scale (17 km) historical (fishery-dependent) and contemporary (fishery-independent) distributions of white abalone habitat using random forest and maximum entropy (MaxEnt), respectively. I projected models to future scenarios in 2050 and 2100 to assess the quality of habitat under climate change. Using MaxEnt, I developed fine-scale (10 m) models with fishery-independent data. I also conducted interviews with former abalone fishermen who observed white abalone during the fishery.
Fishery-dependent and -independent based models revealed differing outcomes of suitable habitat and ensuing effects of climate change. These differences in suitability resulted from differences in the spatial distribution of white abalone between the two data sets. Fine-scale fishery-independent data was limited in its spatial extent, yet in places with sufficient data, I generated high resolution suitability maps. These maps comported with oral histories from fishermen regarding fine-scale habitat, and can help guide site selection within broadly suitable geographic regions. This study provides managers with potential areas to outplant that are resistant to climate change and a framework to design experimental outplanting to adaptively manage a successful recovery effort.