Reconciling seascape genetics and fisheries science in three co-distributed flatfishes
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...
| Main Authors: | , , , , , , |
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| Format: | Dataset |
| Language: | unknown |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://zenodo.org/record/4009715 https://doi.org/10.5061/dryad.h70rxwdgq |
| Summary: | 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. 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 FIS (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 ... |
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