Genetic structure and life history are key factors in species distribution models of spiny lobsters

Abstract

Aim - We incorporated genetic structure and life history phase in species distributionmodels (SDMs) constructed for a widespread spiny lobster, to reveal local adapta-tions specific to individual subspecies and predict future range shifts under the RCP8.5 climate change scenario. Location - Indo-West Pacific. Methods - MaxEnt was used to construct present-day SDMs for the spiny lobster Panulirus homarus and individually for the three genetically distinct subspecies of which it comprises. SDMs incorporated both sea surface and benthic (seafloor) climate layers to recreate discrete influences of these habitats during the drifting lar-val and benthic juvenile and adult life history phases. Principle component analysis (PCA) was used to infer environmental variables to which individual subspecies wereadapted. SDM projections of present-day habitat suitability were compared with pre-dictions for the year 2,100, under the RCP 8.5 climate change scenario. Results - In the PCA, salinity best explained P. h. megasculptus habitat suitability, compared with current velocity in P. h. rubellus and sea surface temperature inP. h. homarus. Drifting and benthic life history phases were adapted to different com-binations of sea surface and benthic environmental variables considered. Highly suitable habitats for benthic phases were spatially enveloped within more extensive sea surface habitats suitable for drifting larvae. SDMs predicted that present-day highlysuitable habitats for P. homarus will decrease by the year 2,100. Main conclusions - Incorporating genetic structure in SDMs showed that individualspiny lobster subspecies had unique adaptations, which could not be resolved inspecies-level models. The use of sea surface and benthic climate layers revealed therelative importance of environmental variables during drifting and benthic life history phases. SDMs that included genetic structure and life history were more informative in predictive models of climate change effects.