Independent estimates of marine population connectivity are more concordant when accounting for uncertainties in larval origins

• Published in: Scientific reports 2018-02, Vol.8 (1), p.2641-16, Article 2641
• Main Authors: Nolasco, R., Gomes, I., Peteiro, L., Albuquerque, R., Luna, T., Dubert, J., Swearer, S. E., Queiroga, H.
• Format: Article
• Language: English
• Subjects: 631/158/2039; 704/158/2039; 704/829/826; Animals; Bivalvia; Chemistry; Connectivity; Dispersal; Ecosystem; Estimates; Humanities and Social Sciences; Larva - growth & development; Larvae; Life cycles; Literature reviews; Marine organisms; Mediterranean Sea; Migration; Models, Biological; multidisciplinary; Ocean circulation; Oceanography; Population; Population Dynamics; Population studies; Portugal; Probability; Science; Science (multidisciplinary); Shells; Uncertainty
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-19833-w

Marine larval dispersal is a complex biophysical process that depends on the effects of species biology and oceanography, leading to logistical difficulties in estimating connectivity among populations of marine animals with biphasic life cycles. To address this challenge, the application of multiple methodological approaches has been advocated, in order to increase confidence in estimates of population connectivity. However, studies seldom account for sources of uncertainty associated with each method, which undermines a direct comparative approach. In the present study we explicitly account for the statistical uncertainty in observed connectivity matrices derived from elemental chemistry of larval mussel shells, and compare these to predictions from a biophysical model of dispersal. To do this we manipulate the observed connectivity matrix by applying different confidence levels to the assignment of recruits to source populations, while concurrently modelling the intrinsic misclassification rate of larvae to known sources. We demonstrate that the correlation between the observed and modelled matrices increases as the number of observed recruits classified as unknowns approximates the observed larval misclassification rate. Using this approach, we show that unprecedented levels of concordance in connectivity estimates (r = 0.96) can be achieved, and at spatial scales (20–40 km) that are ecologically relevant.