Inferring genetic connectivity in real populations, exemplified by coastal and oceanic Atlantic cod

Ingrid Spies; Lorenz Hauser; Per Erik Jorde; Halvor Knutsen; André E Punt; Lauren A Rogers; Nils Chr Stenseth

doi:10.1073/pnas.1800096115

Inferring genetic connectivity in real populations, exemplified by coastal and oceanic Atlantic cod

Proc Natl Acad Sci U S A. 2018 May 8;115(19):4945-4950. doi: 10.1073/pnas.1800096115. Epub 2018 Apr 19.

Authors

Ingrid Spies^{1

2

3}, Lorenz Hauser⁴, Per Erik Jorde^{3

5}, Halvor Knutsen^{3

5

6}, André E Punt⁴, Lauren A Rogers^{7

3}, Nils Chr Stenseth^{8

6}

Affiliations

¹ National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, Seattle, WA 98115; ingrid.spies@noaa.gov n.c.stenseth@ibv.uio.no.
² Quantitative Ecology and Resource Management, University of Washington, Seattle, WA 98195.
³ Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway.
⁴ School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195-5020.
⁵ Institute of Marine Research, N-4817 His, Norway.
⁶ Centre for Coastal Research, University of Agder, 4604 Kristiansand, Norway.
⁷ National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, Seattle, WA 98115.
⁸ Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway; ingrid.spies@noaa.gov n.c.stenseth@ibv.uio.no.

Abstract

Genetic data are commonly used to estimate connectivity between putative populations, but translating them to demographic dispersal rates is complicated. Theoretical equations that infer a migration rate based on the genetic estimator F_ST , such as Wright's equation, F_ST ≈ 1/(4N_em + 1), make assumptions that do not apply to most real populations. How complexities inherent to real populations affect migration was exemplified by Atlantic cod in the North Sea and Skagerrak and was examined within an age-structured model that incorporated genetic markers. Migration was determined under various scenarios by varying the number of simulated migrants until the mean simulated level of genetic differentiation matched a fixed level of genetic differentiation equal to empirical estimates. Parameters that decreased the N_e /N_t ratio (where N_e is the effective and N_t is the total population size), such as high fishing mortality and high fishing gear selectivity, increased the number of migrants required to achieve empirical levels of genetic differentiation. Higher maturity-at-age and lower selectivity increased N_e /N_t and decreased migration when genetic differentiation was fixed. Changes in natural mortality, fishing gear selectivity, and maturity-at-age within expected limits had a moderate effect on migration when genetic differentiation was held constant. Changes in population size had the greatest effect on the number of migrants to achieve fixed levels of F_ST , particularly when genetic differentiation was low, F_ST ≈ 10^-3 Highly variable migration patterns, compared with constant migration, resulted in higher variance in genetic differentiation and higher extreme values. Results are compared with and provide insight into the use of theoretical equations to estimate migration among real populations.

Keywords: Wright’s equation; fisheries management; genetic connectivity; population dynamics; population genetic theory.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animal Migration*
Animals
Female
Gadus morhua / genetics*
Genetic Variation*
Male
Models, Genetic*
Selection, Genetic*