title: Reducing bias and improving precision in species extinction forecasts.
abstract: Forecasting the risk of population decline is crucial in the realm of biological conservation and figures prominently in population viability analyses (PVA). A common form of available data for a PVA is population counts through time. Previous research has suggested that improving estimates of population trends and risk from count data depends on longer observation periods, but that is often impractical or undesirable. Making multiple observations within a single time step is an alternative way to gather more data without extending the observation period. In this paper, we examine the trade-off between the length of the time period over which observations of the population have been taken and the total number of observations or samples that have been recorded through an analysis of simulated data. We found that when the ratio of process error to measurement error variance is high, more precise estimates of quasi-extinction risks can be obtained if replicated observations are taken at each time step, but when the ratio is low, replicated observations add little benefit in improving precision. These results can be used to efficiently design effective monitoring schemes for species of conservation concern.
authors.forename: Kevin E
authors.initials: KE
authors.lastname: See
authors.forename: Elizabeth E
authors.initials: EE
authors.lastname: Holmes
mesh_terms: D000818:Animals; D003198:Computer Simulation; D053476:Extinction, Biological; D005544:Forecasting / Q000379:methods*; D008954:Models, Biological; D011157:Population Dynamics / Q000639:trends; D013997:Time Factors