DOI: 10.5061/dryad.h70rxwdgq
title: Reconciling seascape genetics and fisheries science in three co-distributed flatfishes

description: Uncertainty hampers innovative mixed-fisheries management by the scales at
 which connectivity dynamics are relevant to management objectives. The
 spatial scale of sustainable stock management is species-specific and
 depends on ecology, life history and population connectivity. One valuable
 approach to understand these spatial scales is to determine to what extent
 population genetic structure correlates with the oceanographic
 environment. Here we compare the level of genetic connectivity in three
 co-distributed and commercially exploited demersal flatfish species living
 in the North East Atlantic Ocean. Population genetic structure was
 analysed based on 14, 14 and 10 neutral DNA microsatellite markers for
 turbot, brill and sole respectively. We then used redundancy analysis
 (RDA) to attribute the genetic variation to spatial (geographic location),
 temporal (sampling year) and oceanographic (water column characteristics)
 components.
description: Genotypes were analysed in order to compare levels of genetic
 variation and genetic differentiation between the three species.
 Multi-locus genotypes were tested for deviations from Hardy-Weinberg
 equilibrium using the pegas package
 in the R software (Paradis, 2010; R Core Team, 2020). Linkage
 disequilibrium was evaluated using Fisher’s exact test implemented in the
 genepop package in R (Rousset, 2008). R package hierfstat was
 used to test for significance of
 <i>F</i><sub>IS</sub> (reflecting heterozygote
 deficiency/excess) using a randomization test (Goudet & Jombart,
 2015). Subsequently the level of genetic variation for each sample was
 estimated as number of alleles (allelic richness), observed (HO) and
 expected (HE) heterozygosity. We evaluated the
 proportional importance of geographical location (SPACE), sampling year
 (TIME) and water-column dynamics (ENV) in explaining genetic connectivity
 patterns. To do so, the genotype matrix of each species was first
 converted into allele counts, where each row is an individual and each
 column indicates the count (0, 1 or 2) per allele. Redundancy analysis
 (RDA) identified the spatio-temporal and environmental features explaining
 genetic (i.e. allelic) variation among the individuals of each
 species. The significance of RDA models was determined
 using 1000 random permutations with the vegan package v2.5.6 in R (Oksanen
 et al., 2019). The RDA Significant models were then subjected to forward
 selection using the ordiR2step function implemented in the vegan package,
 including a threshold of α = 0.05 and given the adjusted R² parameter of
 the RDA with all variables included to obtain an unbiased selection
 (Blanchet, Legendre, & Borcard, 2008). Forward selection corrects
 for highly inflated type I errors and overestimated amounts of explained
 variation. The reduced set of explanatory variables based on forward
 selection,  was then used to recalculate the explained proportion of
 genetic variation.  Geographic variables (SPACE) were
 represented by Moran’s Eigenvector Maps (MEMs), along with longitude and
 latitude. The MEMs were calculated for each individual species, based on a
 distance based matrix of the shortest geographic waterway distance between
 sampling locations (Borcard & Legendre, 2002). Temporal variables
 (TIME) were represented by dummy variables from sampling year indicators.
 The year a sample was obtained was scored with the value 1; other years
 were marked with the value 0. Lastly, water-column variables (ENV) for the
 greater North Sea area (including English Channel and Skagerrak) were
 downloaded from the ICES WGOOFE website
 (groupsites.ices.dk/sites/wgoofe). For each of the nine
 water-column parameters, the monthly and yearly average were available for
 the time period 1980-2004. The yearly standard deviation of each variable
 across the 12 months was calculated to capture the intra-annual variation.
 Seascape genetic analyses were conducted with two sets of environmental
 variables (ENV1 and ENV2). The first set (ENV1) consisted of 18 variables
 including the yearly average and standard deviation of each of the nine
 parameters. This set covers the broad environmental variation and captures
 the relevant biological information for a comprehensive analysis across
 the three species in an identical dynamic system. For the second set
 (ENV2), we selected specific month averages for each species.
 Specifically, we selected April, May, June, and September for turbot (4
 months; 36 variables), March, May, June, and September for brill (4
 months; 36 variables), and February, April, and September for sole (3
 months; 27 variables). This set allowed us to test whether specific
 seasonal variation in reproduction time (start and peak spawning time)
 affects the genetic variation among individuals.