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

Abstract

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.

A multidisciplinary analytical framework to delineate spawning areas and quantify larval dispersal in coastal fish

“…The assumption of passive larval drift was retained here because knowledge about species-specific larval behavior in the field are sparse and very uncertain. In addition, Nolasco et al (2018) recently documented better correlations between connectivity observations and dispersal models simulating passive rather than active drift. Indeed, the diffusion applied to trajectories when adding random components to simulate active movements, as is commonly done, is of the same order of the numerical diffusion induced by the spatial discretization of the oceanic domain, including over the nearshore regions where swimming potential would be more probable (Rossi et al, 2014).…”

“…Despite these future developments, we present a general approach which allows to locate spawning areas and to better quantify larval connectivity patterns from pre-determined origins and destinations (Nolasco et al, 2018). In a context of sparse knowledge about fish spawning aggregations (Erisman et al, 2017), this information is critical for the management and conservation of marine ecosystems (see section 4.4).…”

“…Many methods, each with its own strengths and weaknesses, have been used to investigate the ins and outs of larval dispersal (Calò et al, 2013). They are principally divided into four categories: surveys of marine larvae, artificial tags, natural tags (genetics, otolith sclerochronology, otolith geochemical analyses) and numerical modelling (physical or bio-physical, Nolasco et al, 2018). To increase the accuracy of larval dispersal analyses, it seems fundamental to use a combination of complementary methodologies, maximizing the strengths of each technique.…”

“…To increase the accuracy of larval dispersal analyses, it seems fundamental to use a combination of complementary methodologies, maximizing the strengths of each technique. However, while the proportion of works that mixed different methodologies is currently growing, only few studies have used at least two methods to characterize larval dispersal, with a preferential combination of numerical biophysical models with genetic markers and/or otoliths analysis (Nolasco et al, 2018).…”

Multiple spawning events promote increased larval dispersal of a predatory fish in a western boundary current

“…High‐resolution hydrodynamic models, combined with a Lagrangian modelling framework parameterised with species‐specific information, provide a mechanism to examine the physical factors which shape observed distributions of larvae and settled juveniles and are increasingly being used to understand and manage connectivity in the marine environment (Gallego, North, & Petitgas, 2007; Hinrichsen, Dickey‐Collas, Huret, Peck, & Vikebø, 2011). More than 500 hydrodynamic models coupled with larval dispersal models have been variously applied (Nolasco et al, 2018), including globally (Doblin & Van Sebille, 2016), the Southern Ocean (Fraser et al, 2018), boundary current systems (Cetina‐Heredia et al, 2019; Cetina‐Heredia, Roughan, Van Sebille, Feng, & Coleman, 2015; Coleman et al, 2011) and regional seas (Andrello et al, 2013). These models have been used at small and large scales (Hellweger, Sebille, & Fredrick, 2014; Roughan, MacDonald, Baird, & Glasby, 2011; Schunter et al, 2019) to provide and insight into transport and connectivity for a variety of organisms such as macroalgae (Coleman et al, 2011; Fraser et al, 2018), invertebrates (Cetina‐Heredia et al, 2019; Everett et al, 2017; Munroe et al, 2018) and fish (Paris, Cowen, Claro, & Lindeman, 2005; Santos et al, 2018) and also to examine processes such as the impacts of climate change on dispersal (Cetina‐Heredia, Roughan, Van Sebille, & Coleman, 2014; Coleman, Feng, Roughan, Cetina‐Heredia, & Connell, 2013).…”