This dissertation develops and evaluates a vulnerability assessment termed the Livelihood Vulnerability Index (LVI). The LVI estimates the relative ability of California commercial sea urchin fishermen to cope with the change associated with proposed marine protected areas (MPAs). A key goal in establishing MPAs is to minimize the potential negative impacts and maximize conservation benefits to local fishing communities. However, current efforts to assess the impact of proposed MPAs upon fisherman livelihoods largely assume a linear relationship between percent of fishing area or revenue lost and magnitude of impact to fishermen and do not consider the differential abilities of fishermen to cope with change.

The three manuscripts included in this dissertation develop the vulnerability assessment framework and methodology (Livelihood Vulnerability Index), implement and evaluate the vulnerability assessment across multiple scales, and explore how vulnerability assessment scores may be used to spatially weight and thus better represent the importance of fishing areas in marine spatial planning.

The major contribution of this research is the operationalization of a method to systematically assess the vulnerability of fisherman livelihoods---an understudied population group. In applying this vulnerability assessment methodology I found that: 1) the historical and current socioeconomic, environmental, and regulatory conditions fishermen experience in a given place predicted vulnerability levels; 2) the use of weights applied to vulnerability measures within the vulnerability assessments did not significantly impact vulnerability scores and rankings; and 3) fisherman perceptions of what factors may be important to measure vulnerability levels may not coincide with the best predictors of vulnerability levels.

Furthermore, in applying the vulnerability assessment output (LVI scores) to spatial fishing data I found that: 1) the use of spatial data weights may be negligible if spatial data across fishermen are highly clustered as observed in spatial fishing patterns; 2) it is important to consider the scale of analyses since analyzing data at coarser resolutions may exclude the importance of fishing grounds from smaller ports or underrepresented fishermen groups ; and 3) to suitably address the issue of scale, data aggregation techniques may be usefully applied to better represent the importance of fishing areas from underrepresented fishermen groups.