The evolution of marine larval dispersal kernels in spatially structured habitats: Analytical models, individual-based simulations, and comparisons with empirical estimates.

dc.contributor.author Shaw, Allison K.
dc.contributor.author D'Aloia, Cassidy C.
dc.contributor.author Buston, Peter M.
dc.date.accessioned 2019-03-27T20:16:07Z
dc.date.available 2020-01-17T09:41:09Z
dc.date.issued 2019-01-17
dc.description Author Posting. © University of Chicago Press, 2019. This article is posted here by permission of University of Chicago Press for personal use, not for redistribution. The definitive version was published in Shaw, A. K., D'Aloia, C. C., & Buston, P. M. The evolution of marine larval dispersal kernels in spatially structured habitats: Analytical models, individual-based simulations, and comparisons with empirical estimates. American Naturalist, 193(3), (2019):424-435, doi:10.1086/701667. en_US
dc.description.abstract Understanding the causes of larval dispersal is a major goal of marine ecology, yet most research focuses on proximate causes. Here we ask how ultimate, evolutionary causes affect dispersal. Building on Hamilton and May’s classic 1977 article “Dispersal in Stable Habitats,” we develop analytic and simulation models for the evolution of dispersal kernels in spatially structured habitats. First, we investigate dispersal in a world without edges and find that most offspring disperse as far as possible, opposite the pattern of empirical data. Adding edges to our model world leads to nearly all offspring dispersing short distances, again a mismatch with empirical data. Adding resource heterogeneity improves our results: most offspring disperse short distances with some dispersing longer distances. Finally, we simulate dispersal evolution in a real seascape in Belize and find that the simulated dispersal kernel and an empirical dispersal kernel from that seascape both have the same shape, with a high level of short-distance dispersal and a low level of long-distance dispersal. The novel contributions of this work are to provide a spatially explicit analytic extension of Hamilton and May’s 1977 work, to demonstrate that our spatially explicit simulations and analytic models provide equivalent results, and to use simulation approaches to investigate the evolution of dispersal kernel shape in spatially complex habitats. Our model could be modified in various ways to investigate dispersal evolution in other species and seascapes, providing new insights into patterns of marine larval dispersal. en_US
dc.description.embargo 2020-01-17 en_US
dc.description.sponsorship We thank S. Levin, M. Neubert, S. Proulx, L. Sullivan, R. Warner, and several anonymous reviewers for helpful comments. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute. The project was supported by a start-up award from the University of Minnesota to A.K.S. and a National Science Foundation award (OCE-1260424) to P.M.B. and colleagues; C.C.D. was supported by the Weston Howland Junior Postdoctoral Scholarship from the Woods Hole Oceanographic Institution. en_US
dc.identifier.citation Shaw, A. K., D'Aloia, C. C., & Buston, P. M. (2019). The evolution of marine larval dispersal kernels in spatially structured habitats: Analytical models, individual-based simulations, and comparisons with empirical estimates. American Naturalist, 193(3), 424-435. en_US
dc.identifier.doi 10.1086/701667
dc.identifier.uri https://hdl.handle.net/1912/23925
dc.publisher University of Chicago Press en_US
dc.relation.uri https://doi.org/10.1086/701667
dc.subject biological oceanography en_US
dc.subject dispersal kernel en_US
dc.subject evolutionarily stable strategy en_US
dc.subject larval dispersal en_US
dc.subject marine ecology en_US
dc.subject population connectivity en_US
dc.title The evolution of marine larval dispersal kernels in spatially structured habitats: Analytical models, individual-based simulations, and comparisons with empirical estimates. en_US
dc.type Article en_US
dspace.entity.type Publication
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