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

16 pages, 3 tables, 6 figures.-- This article is licensed under a Creative Commons Attribution 4.0 International License
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
This study is part of the ‘LarvalSources - Assessing the ecological performance of marine protected area networks’ research project, funded by Fundação para a Ciência e Tecnologia - FCT (PTDC/BIA-BIC/120483/2010). Financial support was allocated by FCT under the COMPETE Programme, which includes components from the European Regional Development Fund and from the Ministério da Ciência, Tecnologia e Ensino Superior. IG was supported by a MARES Ph. D. fellowship. MARES is an Erasmus Mundus Joint Doctorate programme coordinated by Ghent University. LGP was supported by fellowships POS-A/2012/189 and POS-B/2016/032 from Xunta de Galicia. RA was supported by a research assistant grant through the LarvalSources project and by a Ph. D. scholarship funded by FCT (SFRH/BD/ 84263/2012). RN was supported by a Pos-Doctoral grant also through the LarvalSources project. Thanks are due for additional funding from CESAM (UID/AMB/50017 - POCI- 01-0145-FEDER-007638), FCT/MCTES through national funds (PIDDAC), and FEDER within the PT2020 Partnership Agreement and Compete 2020.