Discussion
A common assumption in polyphenism is that partitioning and variability
of resource use will occur predominantly among ecotypes rather than
within ecotypes. In contrast, homogeneity of resource use is anticipated
to occur within ecotypes, be spatially and temporally stable, and
provide the selection opportunity for specialization (Amundsen et al.,
2008; Knudsen et al., 2010; Svanbäck et al., 2004). However, this study
provided evidence that variation occurred within an ecotype due to diet
specialization among individuals, possibly a precursor to further
population diversification via fine scale ecological selection
(Richardson et al., 2014; Vonlanthen et al., 2009).
Using fatty acids as dietary biomarkers, four distinct patterns of
resource use were identified within the piscivorous lake trout of Great
Bear Lake (Fig. 2). Groups 3 and 4 had the most overlap and these groups
were characterized by C20 and C22 monounsaturates, biomarkers of a food
web based on pelagic or deep-water copepods (Ahlgren et al., 2009;
Happel et al., 2017; Hoffmann, 2017; Loseto et al., 2009; Stowasser et
al., 2006). Specifically, 20:1n-9 is associated with calanoid copepods
known to be particularly important in northern pelagic food webs
(Ahlgren et al., 2009; Budge et al., 2006; Kattner et al., 1998; Loseto
et al., 2009). High levels of 14:0, 18:3n-3 and 18:4n-3 fatty acids
within groups 3 and 4 are also associated with pelagic environments
(Scharnweber et al., 2016; Tucker et al., 2008), although high levels of
18:2n-6 and 18:3n-3 have also been associated with terrestrial markers
(Budge et al., 2001; Budge et al., 1998; Hoffmann, 2017).
Groups 1 and 2 were characterized by high concentrations of 16:4n-3,
20:4n-6 and 22:6n-3 found in diatom and dinoflagellate-based food webs,
respectively. The fatty acid 20:4n-6 reflects a benthic feeding strategy
(from benthic invertebrates to fish) (Stowasser et al., 2006; Tucker et
al., 2008), whereas 22:6n-3 in pennate diatoms (Iverson, 2009) and
filter feeders links planktonic dinoflagellates to benthic
filter-feeding bivalves in a food web (Alfaro et al., 2006; Virtue et
al., 2000). Relatively high concentrations of 16:0, 18:0 and 22:6n-3 and
low concentrations of 16:1n-7 supported the interpretation of
carnivorous (or cannibalistic) dietary patterns (Dalsgaard et al., 2003;
Iverson, 2009; Iverson et al., 2004; Piché et al., 2010). Individuals
positioned between ends of principal components suggests a clinal
pattern of resource use or habitat coupling (Vonlanthen et al., 2009),
where borders among groups are neither abrupt nor obvious as they are
part of a continuum (Hendry et al., 2009b). Overall, observed trophic
patterns could reflect prey associated with different microhabitat
patches; however, the key assumption of disparity of prey associated
with habitat heterogeneity (Skulason et al., 1995; Svanbäck et al.,
2005) may not be applicable to Great Bear Lake (Chavarie et al., 2016a;
Chavarie et al., 2020).
Sympatric divergence, in which barriers to gene flow are driven by
selection between ecological niches, has been implicated in the
evolution of ecological and morphological variation in fishes (Chavarie
et al., 2016d; Hendry et al., 2007; Præbel et al., 2013). Despite the
limited ability of neutral microsatellite markers to detect patterns of
functional divergence (Berg et al., 2016; Lamichhaney et al., 2016;
Roesti et al., 2015), the significant genetic differentiation based on
comparisons with Giant sub-set suggests some deviation from panmixis
within the piscivorous ecotype. Such a genetic pattern displayed by the
Giant sub-set, despite a lack of ecological discreteness, perhaps
resulted from size-assortative mating and/or differences in timing and
location of spawning (Nagel et al., 1998; Rueger et al., 2016; Servedio
et al., 2011). Great Bear Lake is not the only lake in North America
with an apparent divergence in lake trout body size; in Lake Mistassini,
“Giant” individuals also differed genetically from other lake trout
groups (Marin et al., 2016). The similarity based on lake trout body
size between both lakes suggests analogous variables favoring partial
reproductive isolation. Although alternative causes of genetic
differentiation may be possible, due to the short time since the onset
of divergence, post-zygotic isolation seems unlikely in this system
(e.g., prezygotic isolation generally evolves more rapidly Coyne et al.,
2004) and we therefore favor assortative mating based on size and
location as an explanation for the low-level genetic divergence
observed. Nonetheless, putative partial reproductive isolation within an
ecotype adds to the complexity of diversification and speciation
processes potentially occurring within lake trout in Great Bear Lake
(Hendry, 2009; Nosil et al., 2009).
A central question arising from our analysis is what are the mechanisms
behind these patterns of variation? As individual specialization can
result in dietary sub-groups and perhaps differences in habitat use
among sections of a population, such inter-individual variation within
ecological sub-groups could substantially influence processes of
diversification (Araújo et al., 2008; Cloyed et al., 2016).
Among-individual resource specialization within an ecotype in a
species-poor ecosystem like Great Bear Lake could reflect the
diversifying force of intraspecific competition, lack of constraining
effects of interspecific competition, the abundance and distribution of
resources (e.g., temporal and spatial variation of resources), or some
combination of these variables (Bolnick et al., 2007; Cloyed et al.,
2016). Multiple patterns of resource specialization within a single
ecotype, as we see for lake trout in Great Bear Lake, contrasts with the
expected pattern of trophic divergence among ecotypes and homogenization
in habitat use or diet within an ecotype, a key assumption guiding the
development of functional ecological theory (Svanbäck et al., 2004;
Violle et al., 2012). Expression of intraspecific divergence through
habitat and foraging specialization is thought to drive selection on
traits that enable more efficient use of resources (Schluter, 2000;
Skulason et al., 1995; Snorrason et al., 2004).
In Great Bear Lake, multiple trophic generalists (which include the
piscivores studied herein) coexist with one specialist lake trout
ecotype. This contrasts with the more commonly reported observation of
multiple specialist ecotypes (Chavarie et al., 2016a; Elmer, 2016;
Kassen, 2002). A generalist population, however, can be composed of
several subsets of specialized individuals (Bolnick et al., 2009;
Bolnick et al., 2007; Bolnick et al., 2002). This broad distribution of
trophic variation within a population appears to be the case within the
Great Bear Lake piscivores. The among-individual specialization may
result, to some degree, from variable use of spatially separated
resources and possibly temporally variable resources, both of which
could be expected in a large northern lake (Fig. A4; Costa et al., 2008;
Cusa et al., 2019; Quevedo et al., 2009). Ecologically, among-individual
resource specialization within an ecotype is another form of diversity
(Araújo et al., 2008; Bolnick et al., 2003; Pires et al., 2011). Such
diversity may increase stability and persistence of an ecotype within a
system where energy resources are scarce and ephemeral (Cloyed et al.,
2016; Pfennig et al., 2012; Smith et al., 2011). Whether the level of
among-individual specialization within this ecotype is stable or not is
a question that cannot be answered with our data.
Realized niche expansions are often linked to individuals of different
morphologies and body sizes, with evidence of efficiency trade-offs
among different resources (Cloyed et al., 2016; Parent et al., 2014;
Roughgarden, 1972; Svanbäck et al., 2004). When a resource gradient
exists, niche expansion can be achieved via genetic differentiation,
phenotypic plasticity, or a combination of these processes (Bolnick et
al., 2020; Parent et al., 2014). The apparent segregation of resource
use, based on our fatty acid analyses, despite a lack of major
morphological, body size, and genetic differentiation among the four
dietary groups within the piscivorous ecotype, suggests that behavioral
plasticity is causing the observed patterns of dietary differentiation.
Plasticity may promote diversification by expanding the range of
phenotypes on which selection can act (Nonaka et al., 2015; Pfennig et
al., 2010; West-Eberhard, 2003). Theoretical models suggest that
exploiting a wide range of resources is either costly or limited by
constraints, but plasticity is favored when 1) spatial and temporal
variation of resources are important, 2) dispersal is high, 3)
environmental cues are reliable, 4) genetic variation for plasticity is
high and 5) cost/limits of plasticity are low (Ackermann et al., 2004;
Hendry, 2016).
The expression of plasticity in response to ecological conditions (e.g.,
habitat structure, prey diversity) can increase fitness. While most
studies of diet variation focus on morphological differences among
ecotypes in a population, diet variation can also arise from behavioral,
biochemical, cognitive, and social-rank differences that cause
functional ecology to be expressed at a finer scale than at the ecotype
level (McGill et al., 2006; Svanbäck et al., 2005; Violle et al., 2012;
Zhao et al., 2014). Indeed, behavioral plasticity likely has a temporal
evolutionary advantage due to relatively reduced reliance on
ecologically beneficial morphological adaptation (Smith et al., 2011;
Svanbäck et al., 2009). The only detectable morphological differences
among piscivorous groups we identified in Great Bear Lake were
associated with jaw lengths, snout-eye distance, and head length and
depth, which are strongly related to foraging opportunities (Adams et
al., 2002; Sušnik et al., 2006; Wainwright et al., 2016). Some
morphological characters likely express different degrees of plastic
responses (adaptive or not), and thus may be expressed differently
depending on the magnitude and time of exposure to heterogeneous
environments (Hendry, 2016; Sharpe et al., 2008). For example,
environmental components (e.g., habitat structure) appear to have
stronger and faster effects on linear characters (e.g., jaw length) than
on body shape (Chavarie et al., 2015; Sharpe et al., 2008). Trophic
level might also limit the scope for morphological variation in lake
trout because piscivory can limit diversification of feeding morphology
in fishes (Collar et al., 2009; Svanbäck et al., 2015).