1 | INTRODUCTION
Understanding the resilience of biological marine resources to changing
oceanographic conditions is central to ecosystem-based fisheries
management and the implementation of adaptive sustainable harvest
strategies (Link 2002; Levin and Möllmann 2015). The ability of
populations to respond to disturbances in their habitat is in part
determined by the genetic diversity present in the population (Parker et
al. 2000; Hoffman and Sgro 2011). This genetic diversity, exhibited by a
portfolio of available gene variants, allows for a quick response if
selectively advantageous variants are already present in the population
(Sunday et al. 2011; Pacifici et al. 2017). Understanding the response
of marine populations to environmental perturbations will allow us to
readily assess the resilience or vulnerability of these populations and
species.
Measuring differential survival between subpopulations reveals how
environmental conditions can influence the overall productivity of
exploited populations. For example, Schindler et al. (2010) demonstrated
that environmental conditions favored the production of discrete salmon
populations residing in Bristol Bay, Alaska. Maintenance of a portfolio
of locally adapted genotypes ensured adult returns to the region, but
returns to different streams were maximized under different
environmental conditions. This maintenance of a portfolio of adaptive
alleles may be a key aspect of resilience of populations to natural
environmental variability.
The idea of a temporal portfolio effect, in which adaptive variation is
maintained by overlapping generations in a temporally variable
environment, has been studied in general (Ellner and Hairston 1994). For
example, many freshwater zooplankton taxa have relatively short-lived
adults that may be subject to strong selection, but eggs can remain
viable for decades in sediment, resulting in persistent egg banks that
are relatively buffered from environmental variation (Brendonck and
DeMeester 2003). We hypothesize that a similar effect may occur in
marine fish species with highly dispersive larvae and long-lived,
relatively sedentary adults. The genetic composition of each recruitment
cohort may reflect relatively strong selection during the larval stage,
while the adult population would maintain genetic variation reflecting
multiple cohorts. Here we test this hypothesis using genomic methods for
detecting population structure and adaptive loci.
Genomic data allow us to scan for individual and population-level
differences across the whole genome, and genomics is becoming integral
in answering a wide array of previously unresolved questions in
conservation biology with numerous applications in fisheries (Wenne et
al., 2007; Barrio et al. 2016; Jasonowicz et al., 2016;
Valenzuela-Quiñonez, 2016; Kumar and Kocour, 2017). It is now possible
to estimate, with a high level of precision and certainty, the
demographic structure of fish populations at small spatio-temporal
scales, and to identify local adaptation from genomic data (Wang and
Höök, 2009; Barrio et al., 2016; Catchen et al. 2017; McKinney et al.
2017). RADseq approaches have been extensively used to describe various
biological and ecological phenomena, such as phylogeography, population
differentiation and structure, population and individual admixture
(composition of lineages), genetic diversity, and outlier loci
detection, among others (Alexander et al. 2009; Andrews et al. 2016;
Narum et al. 2013).
Our model species, the Pacific ocean perch (POP; Sebastes
alutus ), is a long-lived species with its oldest individuals being over
100 years old (Conrath and Knoth 2013). POP are the most abundant and
economically important rockfish species in the Gulf of Alaska (Conrath
and Knoth 2013) with landings in excess of 55,000 tons in 2017 (NOAA
2019). The fishery is managed using an age-structured model where the
vital population rates are derived from the abundances of different ages
in the catch (Megrey 1988; Hulson et al. 2017), but the relationship
between the abundance of spawning fish and their offspring cohort is
highly variable and unpredictable. This extreme annual fluctuation in
success and failures of various year classes has been noted as a
characteristic of this and many other commercially exploited species
(Westrheim 1958; Carlson and Haight 1976).
POP in the Gulf of Alaska live on the upper slope of the continental
shelf. They spawn from September through November, with parturition
occurring in April through May the following year, when larvae rise from
demersal spawning habitats on the continental shelf break (150 – 400m
depth) to surface waters. They then become part of the ichthyoplankton
and within a few weeks metamorphose to a young-of-the-year form (YOY).
They are carried in the surface waters by currents and settle out of the
water column in nearshore rocky habitat by the end of their first year
(Carlson and Haight 1976; Major and Shippen 1970). During their
shoreward movement, larvae grow rapidly and allocate significant amounts
of energy to creating lipid tissue. This lipid tissue is apparently lost
during settlement (Moss et al. 2016), suggesting energy acquisition and
growth are important determinants of settlement success (Hoey and
McCormick 2004). They remain in the nearshore habitat for the next few
years until they join the discrete adult schools residing on the
continental shelf and slope (Love et al. 2002). They reach sexual
maturity at eight to ten years of age and repeatedly spawn until their
hundredth year or longer (Hulson et al. 2017). These adult schools are
genetically differentiated and the degree of their differentiation
(FST) is correlated to the geographic distance
between them (Palof et al. 2011).
Larval dispersal pathways in POP may be highly variable from
year-to-year since they mostly depend on ocean currents in a given year
(Mundy et al. 2010). An important prediction of ocean current and
dispersal models (Stockhausen 2009; Stockhausen and Hermann 2007) is
that in each year, the larvae at a given pelagic location are comprised
of mixtures of individuals from different spawning locations indicating
a high degree of mixing among them. However, population genetic studies
of young-of-the-year and adults indicate there is limited mixing among
subpopulations (Palof et al., 2011; Kamin et al., 2014). The Kamin et
al. (2014) follow up study examined the YOY POP catches corresponding to
locations near the adults caught by Palof et al. (2011). Their work
showed that the collections of YOY POP were most related to the linearly
closest adult populations. Either widely dispersed juveniles are able to
return to their natal areas, or survival is maximized among locally
retained larva, possibly due to local adaptation.
Here, we test whether POP larvae exhibit signatures of selection that
could allow for the maintenance of a portfolio of adaptive variation in
the multi-cohort adult population. We examined YOY POP collected from
the eastern Gulf of Alaska during two years (2014 and 2015), when the
oceanographic conditions were drastically different, with 2014 being an
average temperature year, and 2015 being anomalously warm which is
expected to have a negative impact on the fish (Cavole et al. 2016;
Gentemann et al. 2017; Jones et al. 2018). We evaluated the potential
for differences in selection strength for YOY POP across years by
testing whether the fish differed in physiological conditions in 2014
and 2015, measured as a body condition index based on weight-length
relationships, and total lipid content. We then used genotype
environment association (GEA) tests with RADseq genomic data to test for
differences in selection acting on the genome to favor different
phenotypes between the two years. Finally, we identified candidate
biological pathways on which selection was acting in the two different
YOY cohorts. We predicted that the strength of selection would be higher
in 2015 than 2014 due to the unusually high 2015 sea temperatures,
resulting in poor body condition and a greater number of SNPs associated
with environmental variables and physiological condition in the 2015
dataset. These findings may explain the difference in recruitment for
the 2014 and 2015 cohorts as estimated in the 2017 stock assessment
(Hulson et al., 2017).