Asymmetric dispersal is a critical element of concordance between biophysical dispersal models and spatial genetic structure in Great Barrier Reef corals

Abstract

Aim: Widespread coral bleaching, crown-of-thorns seastar outbreaks, and tropical storms all threaten foundational coral species of the Great Barrier Reef, with impacts differing over time and space. Yet, dispersal via larval propagules could aid reef recovery by supplying new settlers and enabling the spread of adaptive variation among regions. Documenting and predicting spatial connections arising from planktonic larval dispersal in marine species, however, remains a formidable challenge. Location:The Great Barrier Reef, Australia.Methods: Contemporary biophysical larval dispersal models were used to predict long-distance multigenerational connections for two common and foundational coral species (Acropora tenuis and Acropora millepora). Spatially extensive genetic surveys allowed us to infer signatures of asymmetric dispersal for these species and evaluate concordance against expectations from biophysical models using coalescent genetic simulations, directions of inferred gene flow, and spatial eigenvector modelling.Results: At long distances, biophysical models predicted a preponderance of northsouth connections and genetic results matched these expectations: coalescent genetic simulations rejected an alternative scenario of historical isolation; the strongest signals of inferred gene flow were from north-south; and asymmetric eigenvectors derived from north-south connections in the biophysical models were significantly better predictors of spatial genetic patterns than eigenvectors derived from symmetric null spatial models. Main conclusions: Results are consistent with biophysical dispersal models yielding approximate summaries of past multigenerational gene flow conditioned upon directionality of connections. For A. tenuis and A. millepora, northern and central reefs have been important sources to downstream southern reefs over the recent evolutionary past and should continue to provide southward gene flow. Endemic genetic diversity of southern reefs suggests substantial local recruitment and lack of long-distance gene flow from south to north.

Signatures of selection in the coral holobiont reveal complex adaptations to inshore environments driven by Holocene climate change

“…Across all of these models migration coefficients were larger for the southward direction. Although this is against the predominant direction of inshore current flow 48 and predicted movement from biophysical models 49 it is in accordance with studies that have shown southward migration to be the predominant direction of gene flow for A. tenuis and A. millepora on the GBR 9,49 including for inshore populations. This disconnect between biophysical modelling (which reflects northward inshore currents) and southward gene flow could reflect differences in effective population size.…”

Genomic signatures in the coral holobiont reveal host adaptations driven by Holocene climate change and reef specific symbionts

“…Across all of these models, migration coefficients were larger for the southward direction. Although this is against the predominant direction of inshore current flow (35) and predictions based on biophysical modeling (36), it is consistent with studies that have shown southward migration to be the predominant direction of gene flow for A. tenuis and A. millepora on the GBR (6,36) including for inshore populations. This disconnect between biophysical modeling (which reflects prevailing, but variable, northward inshore currents) and apparent gene flow could reflect differences in effective population size.…”

Time matters: genetic composition and evaluation of effective population size in temperate coastal fish species

“…Over the last four decades, we have assembled extensive knowledge on the genetic characterization of marine organisms, mainly concerning the spatial distribution and structuring of populations (e.g., Knutsen et al, 2007;Plank et al, 2010;Robalo et al, 2013;Riginos et al, 2019;Verry et al, 2020). With this information, it is now possible and relevant to understand how these patterns behave through time.…”