Larval Transport Modeling of Deep-Sea Invertebrates Can Aid the Search for Undiscovered Populations

Type Article
Date 2011-08
Language English
Author(s) Yearsley Jon M.1, Sigwart Julia D.2
Affiliation(s) 1 : Univ Coll Dublin, Sch Biol & Environm Sci, Dublin 2, Ireland.
2 : Queens Univ Belfast, Sch Biol Sci, Marine Lab, Portaferry, North Ireland.
Source Plos One (1932-6203) (Public Library Science), 2011-08 , Vol. 6 , N. 8 , P. e23063 (10p.)
DOI 10.1371/journal.pone.0023063
WOS© Times Cited 24

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. Principal Findings: 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/Significance: 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.

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Publisher's official version 10 1 MB Open access
Supporting information S1. Interrogation of the ARGO probe data [PDF]. Results of analyses to demonstrate limited ... 18 704 KB Open access
Supporting information S2. Dispersal pathways [animated GIF]. Animation demonstrating particle (predicted larval) pathways originating at the source populations of the model organism, chitons. 266 KB Open access
Supporting information S3. Deep-sea chiton dataset [XLS]. An Excel file that contains the abundance (number of specimens) and distribution of deep sea chiton species. These data encompass several ... 29 KB Open access
Supporting information S4. simulateParticles.m [Matlab script]. This Matlab script performs particle tracking using the vector fields generated by ... 8 KB Open access
Supporting information S5. dlongdlat.m [Matlab script]. This Matlab script calculates a particles rate of change of position (longitude and latitude in degrees per day) from the ocean current vector 626 bytes Open access
Supporting information S6. generateVectorFields.m [Matlab script]. This Matlab script uses the Argo probe data in probeData.mat (Supporting Information S9) to estimate deep sea ocean currents. It wil 14 KB Open access
Supporting information S7. readArgoData.m [Matlab script]. This Matlab Script reads the NETCDF files that contain Argo probe data from the Coriolis web sever 6 KB Open access
Supporting information S8. coastline.mat [Matlab binary file]. This Matlab binary file (.mat) contains the coastline data for the islands in our study region. 1 MB Open access
Supporting information S9. probeData.mat [Matlab binary file]. This Matlab binary file (.mat) contains the Argo probe data used in the paper. This file is used by... 440 KB Open access
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