title: Larval transport modeling of deep-sea invertebrates can aid the search for undiscovered populations.
abstract: BACKGROUND
  Many deep-sea benthic animals occur in patchy distributions separated by thousands of kilometres, yet because deep-sea habitats are remote, little is known about their larval dispersal. Our novel method simulates dispersal by combining data from the Argo array of autonomous oceanographic probes, deep-sea ecological surveys, and comparative invertebrate physiology. The predicted particle tracks allow quantitative, testable predictions about the dispersal of benthic invertebrate larvae in the south-west Pacific.
  
  
  RESULTS
  In a test case presented here, using non-feeding, non-swimming (lecithotrophic trochophore) larvae of polyplacophoran molluscs (chitons), we show that the likely dispersal pathways in a single generation are significantly shorter than the distances between the three known population centres in our study region. The large-scale density of chiton populations throughout our study region is potentially much greater than present survey data suggest, with intermediate 'stepping stone' populations yet to be discovered.
  
  
  CONCLUSIONS
  We present a new method that is broadly applicable to studies of the dispersal of deep-sea organisms. This test case demonstrates the power and potential applications of our new method, in generating quantitative, testable hypotheses at multiple levels to solve the mismatch between observed and expected distributions: probabilistic predictions of locations of intermediate populations, potential alternative dispersal mechanisms, and expected population genetic structure. The global Argo data have never previously been used to address benthic biology, and our method can be applied to any non-swimming larvae of the deep-sea, giving information upon dispersal corridors and population densities in habitats that remain intrinsically difficult to assess.
authors.affiliation: School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland, United Kingdom. jon.yearsley@ucd.ie
authors.forename: Jon M
authors.initials: JM
authors.lastname: Yearsley
authors.forename: Julia D
authors.initials: JD
authors.lastname: Sigwart
mesh_terms: D000465:Algorithms; D000818:Animals; D044822:Biodiversity; D005843:Geography; D007814:Larva / Q000254:growth & development; D008386:Marine Biology / Q000379:methods*; D008542:Melanesia; D008954:Models, Biological; D009510:New Caledonia; D009792:Oceans and Seas; D049916:Polyplacophora / Q000145:classification / Q000254:growth & development*; D011156:Population Density; D011157:Population Dynamics; D013997:Time Factors; D009514:Vanuatu