4 Discussion
This study provides further insight to the spatial distribution of
genetic variation underlying migration timing in a broad range of
steelhead populations. Genetic relationships were characterized for
neutral and candidate markers for 113 populations, supporting previous
findings of population structure and demonstrated strong differences
between major lineages. We determined linkage blocks for 13 candidate
markers associated with migration timing and found different
heterozygote haplotypes were found to be predominant in coastal versus
inland lineages. Environmental drivers of candidate variation revealed
the importance of temperature and precipitation to selection on
variation for migration in this system. Overall, this study provides
extensive geographic variation for candidate markers associated with
migration timing that is expected to be important for conservation
applications in this species (Waples & Lindley, 2018).
Patterns of genetic variation among steelhead populations were highly
distinct between neutral and candidate markers. Neutral structure was
consistent with previous studies with various marker types that largely
correspond to geographic population structure and significant
heterogeneity in environmental conditions at collections sites
(Blankenship et al., 2011; Matala et al., 2014; Micheletti et al.,
2018b). For example, the Clearwater River steelhead have consistently
showed a distinct genetic signal from others in the Snake River basin
regardless of marker type (Narum et al., 2008; Campbell et al., 2012;
Matala et al., 2014; Micheletti et al., 2018b). Additionally, the
neutral markers provided further resolution than previous studies for
the inland lineage especially for populations in the Yakima River
drainage that were distinct from the rest of the populations in the
middle Columbia River. The distinct neutral patterns in the Clearwater
and Yakima River drainages were likely due to different levels of
genetic influence from hatchery programs (Blankenship et al., 2011).
Populations in the Yakima River have likely been minimally influenced by
hatchery programs as there has been no direct hatchery program for
steelhead in this subbasin. Similarly, large stretches of the Clearwater
River basin, including the Selway and Lochsa Rivers, are managed
exclusively for wild fish (Nielsen et al., 2009; Campbell et al., 2012).
Presence of dams and historical rapid population declines may also
differentiate sub-basins of inland steelhead from one another
(Blankenship et al., 2011; Matala et al., 2014). Finally, the Klickitat
River is positioned in a geographically intermediate location at the
eastern base of the Cascades and considered to be on the boundary of the
coastal and inland lineages. The intermediate status of the Klickitat
River collections was evident in the neutral PCA population structuring
which is consistent with previous studies (e.g., Micheletti et al.,
2018b). This intermediate signal was also observed in two other
populations, Fifteenmile Creek and Mill Creek, which may indicate gene
flow with steelhead in the Klickitat R. or admixture. In contrast to
geographical patterns observed at neutral loci, the candidate PCA
divided collections by their predominant adult migration timing. The
Skamania stock was a useful reference for the extreme extent of fixed
genetic variation for premature alleles due to artificial selection for
early migration timing over several decades in this hatchery program. At
the other end of the spectrum, the mature genotype was predominant in
most collections, while the heterozygote collections were dispersed
across the basin, but with divergent ratios of haplotypes between
coastal and inland lineages. The presence of genetic variation for
premature alleles in the inland lineage suggests that some populations
of steelhead (i.e., those in the Salmon R. drainage) may exhibit
phenotypic variation for early and late arrival timing to spawning
grounds as shown by Micheletti et al. (2018b).
Haplotype blocks of markers with the greatest association with one
another and with the migration timing phenotype improve ability to
evaluate genetic variation associated with migration timing across the
landscape. In addition to LD assessments, we evaluated differences
between average genotype frequencies with fewer candidate markers.
Marker 9 had the most similar average genotype frequencies to markers
8-12 for all genotypes and markers 8-12 had the greatest LD in all
collections. This finding suggests that marker 9 could be useful under
circumstances of limited genotyping abilities. This same marker was also
helpful at distinguishing patterns in steelhead arrival timing to
spawning grounds (Micheletti et al., 2018b). However, it is still
beneficial to assess collections with entire haplotype blocks when
possible, to generate numerous haplotype combinations instead of only
three genotypes gained from a single marker.
We observed significant association between multiple environmental
variables and candidate markers when examined across lineages, which was
expected given that environmental conditions vary significantly across
the Columbia River basin landscape. We found migration distances,
temperature variables, and precipitation variables had the strongest
association to adaptation for all collections which was consistent with
previous landscape genomics analyses (Micheletti et al., 2018b).
Migration distance traveled between the Pacific Ocean and spawning sites
ranged from 60 to 1,400 km, presenting a vast difference between coastal
and inland lineages in energetic allocation before spawning (Olsen et
al., 2011; Hecht et al., 2015). Migration distance was not significantly
associated with migration timing within each lineage suggesting that
variation at candidate markers is not highly distinct among populations
at small geographic scales. Significant association of temperature with
candidate markers was not surprising since fish rely on environmental
temperatures to regulate body temperatures and trigger migratory
behavior (Jonsson 1991; Sykes et al., 2009), and extreme temperatures
can inhibit cardiac and metabolic proficiencies (Chen et al., 2018).
Further, genetic disparities in thermal tolerance when encountering
temperature barriers has been found to contribute to local adaptation in
salmonids (Eliason et al., 2011; Narum et al., 2013; Muñoz et al.,
2015). Finally, the significance of precipitation with variation at
candidate markers is expected to be important since precipitation
conditions can impact survival and selection on genes associated with
thermal tolerance when flow is low (Heath et al., 2002) and water
temperatures are elevated (Narum et al., 2013). In contrast, when
precipitation is high and stream flow is powerful, conditions may become
energetically costly for migrating steelhead, but also provide cues for
migration to spawning grounds (Keefer et al. 2014; 2018). Significantly
associated environmental variables within each lineage were more limited
than across lineages of steelhead, and largely reflected regional
differences in precipitation within the coastal lineage and temperature
within the inland lineage.
In this study, we assessed the spatial distribution of candidate
haplotype frequencies because selective pressures on steelhead migration
are disparate across the heterogeneous landscape. The coastal lineage
contained steelhead maturing both in the ocean and streams, whereas
inland lineage steelhead only matured in streams. Initial studies (Hess
et al., 2016; Prince et al., 2017; Thompson et al., 2019) identified and
associated greb1L genotypes with freshwater entry, while
Micheletti et al. (2018a) revealed a greater greb1L association
with arrival timing to spawning grounds. We also detected more than one
genotype present in inland collections, further supporting an
association with arrival timing to spawning grounds introduced by
Micheletti et al. (2018a). Our study incorporated more collections and
more candidate markers associated with migration timing than previous
studies, which allowed us to determine haplotypes to describe the
spatial pattern of mature and premature genotypes across the Columbia
River basin in greater detail. Coastal collections exhibited greater
genetic diversity at candidate markers, but greater influence of
premature alleles from Skamania and other hatchery stocks. In the inland
lineage, the mature genotype was detected at high frequency despite all
inland steelhead maturing in freshwater, supporting findings by
Micheletti et al. (2018a). Variation in the second haplotype block,
which includes markers in the intergenic region, indicates that inland
populations retain genetic variation that may allow for variable timing
in arrival to spawning grounds. However, further studies are needed that
dissect arrival phenotypes and the association at candidate markers atgreb1L and rock1 .
From a management perspective, detailing the distribution of migration
run timing has direct conservation implications. Early migrating fish
spend less time feeding in the nutrient rich ocean, resulting in less
opportunities for growth and potential for decreased reproductive
success. Further, more time in freshwater systems exposes early
migrators to thermal stress, disease, and greater risk for impacts of
climate change and selective fisheries (Quinn et al., 2015). Thus,
steelhead with this early migration pattern have increased odds of
extirpation and may require greater conservation efforts (Prince et al.,
2017). Previous findings (Micheletti et al., 2018a) were bolstered by
this study that indicate greater genetic diversity at candidate genes
for inland collections than previously understood. Effective
conservation efforts to maintain or increase genetic variation
underlying migration timing is expected to provide broader life history
diversity for populations to endure stochastic environments. Thus,
maintenance of genetic diversity associated with migration timing across
the Columbia River basin may be a key to promote resilient steelhead
populations that are able to recover from anthropogenic impacts.
Data Accessibility:Genotype data are available in
Dryad at doi:10.5061/dryad.jh9w0vt80.
Competing Interests: None declared
Acknowledgements: Thanks to all tribes and agencies that
provided samples, laboratory staff involved in sample processing
(CRITFC, IDFG), Funding from Bonneville Power Administration grant
number 2008-907-00.
Author Contributions: SRN designed and directed the study. EEC
analyzed the data. All authors
interpreted the results and wrote
the manuscript.