MaxTemp: A Method to Maximise Precision of the Temporal Method for Estimating N_e in Genetic Monitoring Programs

We introduce a new software program, MaxTemp, that increases precision of the temporal method for estimating effective population size (N_e) in genetic monitoring programs, which are increasingly used to systematically track changes in global biodiversity. Scientists and managers are typically most interested in N_e for individual generations, either to match with single-generation estimates of census size (N) or to evaluate consequences of specific management actions or environmental events. Systematically sampling every generation produces a time series of single-generation estimates of temporal F ( $\hat{F})$ , which can then be used to estimate N_e; however, these estimates have relatively low precision because each reflects just a single episode of genetic drift. Systematic sampling also produces an array of multigenerational temporal estimates that collectively contain a great deal of information about genetic drift that, however, can be difficult to interpret. Here, we show how additional information contained in multigenerational temporal estimates can be leveraged to increase precision of $\hat{F}$ for individual generations. Using information from one additional generation before and after a target generation can reduce the standard deviation of $\hat{F}$ ( ${\sigma }_{\hat{F}}$ ) by up to 50%, which not only tightens confidence intervals around ${\hat{N}}_e$ but also reduces the incidence of extreme estimates, including infinite estimates of N_e. Practical application of MaxTemp is illustrated with data for a long-term genetic monitoring program for California delta smelt. A second feature of MaxTemp, which allows one to estimate N_e in an unsampled generation using a combination of temporal and single-sample estimates of N_e from sampled generations, is also described and evaluated.