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

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 1977 classic paper (``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 contribution of this work is to provide a spatially explicit analytic extension of Hamilton and May 1977, 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.READMEContains readme and notes for all files.README.txtfiles_runsimsContains Matlab code (.m files) for running model simulation data used for Figures 2-4 in the paper.output_environmentsContains Matlab data files (.mat files) for each type of simulation environment.output_simulationsContains Matlab data files (.mat files) for each individual-based simulation.files_plotsimsContains Matlab code (.m files) for plotting Figures 2-4 in the paper, using the above .mat files.files_plotcomparisonsContains Matlab code (.m files), R code (.R files) and data files (.csv) to compare simulated and empirical data and to plot Figure 5 in the paper.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: OCE-126042