Uncertainty in empirical estimates of marine larval connectivity
Despite major advances in our capacity to measure marine larval connectivity (i.e. the pattern of transport of marine larvae from spawning to settlement sites) and the importance of these measurements for ecological and management questions, uncertainty in experimental estimates of marine larval connectivity has been given little attention. We review potential uncertainty sources in empirical larval connectivity studies and develop Bayesian statistical methods for estimating these uncertainties based on standard techniques in the mark-recapture and genetics literature. These methods are implemented in an existing R package for working with connectivity data, ConnMatTools, and applied to a number of published connectivity estimates. We find that the small sample size of collected settlers at destination sites is a dominant source of uncertainty in connectivity estimates in many published results. For example, widths of 95% CIs for relative connectivity, the value of which is necessarily between 0 and 1, exceeded 0.5 for many published connectivity results, complicating using individual results to conclude that marine populations are relatively closed or open. This “small sample size” uncertainty is significant even for studies with near-exhaustive sampling of spawners and settlers. Though largely ignored in the literature, the magnitude of this uncertainty is straightforward to assess. Better accountability of this and other uncertainties is needed in the future so that marine larval connectivity studies can fulfill their promises of providing important ecological insights and informing management questions (e.g. related to marine protected area network design, and stock structure of exploited organisms). In addition to using the statistical methods developed here, future studies should consistently evaluate and report a small number of critical factors, such as the exhaustivity of spawner and settler sampling, and the mating structure of target species in genetic studies.