Population monitoring must be accurate and reliable to correctly classify population status. Green sea turtles, Chelonia mydas, have endangered and threatened populations globally, but multiple nesting beaches have shown substantial increases. Sea turtle nesting beach surveys are commonly used as a population index for assessment because nesting turtles are easily accessed and quantified. However, process and observation errors, compounded by delayed maturity, obscure the relationship between trends on the nesting beach and the population as a whole. We present a new simulation-based tool, Monitoring Strategy Evaluation (MoSE), to explore the relationships between monitoring data and assessment accuracy. MoSE has three main components: the simulated “true” biological operating model, an observation model, and an estimation model. To explore this first use of MoSE, we apply different treatments of population impacts, sampling, and detection of the virtual “true” population, and then sample the nests or nesters to test if the observation “data” accurately diagnose population status indicators. Based on the observed data, we estimate population trend and compare this to the known values from the operating model (virtual “true” population). We ran a series of scenarios with different process and measurement errors, including harvest impacts, cyclical breeding probability, sampling bias, and varying monitoring durations to see how these realistic factors impact accuracy in estimating status indicators.
Results/Conclusions
We found that disturbance type and severity have important and persistent effects on the accuracy of population assessments drawn from monitoring rookeries. The accuracy of estimating population trend is influenced by the underlying population trajectory, age classes disturbed, and disturbance severity. At least 10 years of monitoring data are necessary to accurately estimate population trend, and longer if juvenile age classes were disturbed and trend estimates occur during the recovery phase. The probability of incorrectly classifying population trend as either increasing or decreasing depends on impact type and population trajectory. Monitoring strategies for specific populations can be tailored based on the impact history, population trajectory, and environmental drivers. The MoSE is an important tool to comprehensively test multiple population status indicators for accuracy and allows biologists to make informed decisions regarding the best monitoring strategies to employ.