Data from: Genetic analyses reveal complex dynamics within a marine fish management area

Genetic data have great potential for improving fisheries management by
identifying the fundamental management units – i.e. the biological
populations - and their mixing. However, so far the number of practical
cases of marine fisheries management using genetics has been limited.
Here, we used Atlantic cod in the Baltic Sea to demonstrate the
applicability of genetics to a complex management scenario involving
mixing of two genetically divergent populations. Specifically, we
addressed several assumptions used in the current assessment of the two
populations. Through analysis of 483 single nucleotide polymorphisms
(SNPs) distributed across the Atlantic cod genome we confirmed that a
model of mechanical mixing, rather than hybridization and introgression,
best explained the pattern of genetic differentiation. Thus, the fishery
is best monitored as a mixed-stock fishery. Next, we developed a targeted
panel of 39 SNPs with high statistical power for identifying population of
origin and analysed more than 2000 tissue samples collected between 2011
and 2015 as well as 260 otoliths collected in 2003/2004. These data
provided high spatial resolution and allowed us to investigate
geographical trends in mixing, to compare patterns for different life
stages and to investigate temporal trends in mixing. We found similar
geographical trends for the two time points represented by tissue and
otolith samples and that a recently implemented geographical management
separation of the two populations provided a relatively close match to
their distributions. In contrast to the current assumption, we found that
patterns of mixing differed between juveniles and adults, a signal likely
linked to the different reproductive dynamics of the two populations.
Collectively, our data confirm that genetics is an operational tool for
complex fisheries management applications. We recommend focussing on
developing population assessment models and fisheries management
frameworks to capitalize fully on the additional information offered by
genetically assisted fisheries monitoring.