Green sea turtles, Chelonia mydas, have endangered and threatened populations globally, but several populations show signs of population recovery. In Hawaii, nesting female green turtles have increased 5.7% year-1 since 1973, but wide fluctuations in census counts of nesting females make recovery diagnosis difficult. For effective management planning, it is critical to have the best information possible on vital rates, and to determine the best tools and practices for incorporating vital rate information, particularly variability, into population models to assess population size and status. Process and observation errors, compounded by late maturity, obscure the relationship between trends on the nesting beach and the entire population. Using sea turtle nesting beach surveys as a population index for assessment is problematic, yet often pragmatic because this is the only population index that is easily accessible. It is advantageous to use a modelling approach that estimates interannual variability in life history traits, accounts for uncertainty from individual-​level variability, and allows for population dynamics to emerge from individual behaviors. To this end, I analyzed a long-term data set of marked green sea turtles to determine the degree of temporal variability in key life history traits. From this analysis, I built an agent-based model (ABM) to form the basis of a new assessment tool – Monitoring Strategy Evaluation. This modeling framework is designed to provide an evaluation of monitoring program effectiveness to assist in planning future programs for sea turtles. Altogether, my research suggests certain life history traits of green sea turtles have important temporal variation that should be accounted for in population models, understanding the relationships between nesting and the total population is essential, and basing population assessments from nesting beach data alone is likely to result in incorrect or biased estimates of status indicators. The quantitative tools employed here can be applied to other sea turtle populations and will improve monitoring, and result in better estimates of current population trends and enhance conservation for all species of sea turtles.

Piacenza, Susan E. H.
Short Description: 
A thesis on creating an agent-based model to monitor Green sea turtles more effectively.
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Entry Date: 
Wednesday, June 14, 2017
197 pp
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Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon
Doctor of Philosophy
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