Phenotypic variation and selective mortality as major drivers of recruitment variability in fishes

Authors

  • Darren W. Johnson,

    Corresponding author
    1. Marine Biology Research Division, Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, USA
    • Correspondence and present address: Department of Biological Sciences, California State University, Long Beach. 1250 Bellflower Blvd., Long Beach, CA, 90840, USA. E-mail: darren.johnson@csulb.edu

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  • Kirsten Grorud-Colvert,

    1. Department of Integrative Biology, Oregon State University, Corvallis, Oregon, 97330, USA
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  • Su Sponaugle,

    1. Department of Integrative Biology, Oregon State University, Corvallis, Oregon, 97330, USA
    2. Marine Biology and Fisheries, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA
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  • Brice X. Semmens

    1. Marine Biology Research Division, Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, USA
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Abstract

An individual's phenotype will usually influence its probability of survival. However, when evaluating the dynamics of populations, the role of selective mortality is not always clear. Not all mortality is selective, patterns of selective mortality may vary, and it is often unknown how selective mortality compares or interacts with other sources of mortality. As a result, there is seldom a clear expectation for how changes in the phenotypic composition of populations will translate into differences in average survival. We address these issues by evaluating how selective mortality affects recruitment of fish populations. First, we provide a quantitative review of selective mortality. Our results show that most of the mortality during early life is selective, and that variation in phenotypes can have large effects on survival. Next, we describe an analytical framework that accounts for variation in selection, while also describing the amount of selective mortality experienced by different cohorts recruiting to a single population. This framework is based on reconstructing fitness surfaces from phenotypic selection measurements, and can be employed for either single or multiple traits. Finally, we show how this framework can be integrated with models of density-dependent survival to improve our understanding of recruitment variability and population dynamics.

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