4.1 Estimated level of intrapopulation feeding diversity
We used cross-sectional sampling to calculate \(\text{PS}_{i}\), a
specialization metric, as an estimate of intrapopulation feeding
diversity in Salish Sea harbor seals. Our data confirmed intrapopulation
feeding diversity across the spatial (hundreds of km) and temporal
(years) scales that the scat samples represented (average\(\text{PS}_{i}\) = 0.399, 95% CI = 0.026, R = 100,000). These data
demonstrate intrapopulation feed diversity but leave room for two
alternative hypotheses (which cannot be separated in this case),
regarding the absolute level of individual specialization: the
occurrence of congruent long-term generalists and short-term
specialists, or the occurrence of long-term specialists.
4.2 Importance of Time of year, Sex, Location, and Year on
relative specialization ( \(\text{PS}_{i})\)
Month was an important predictor of relative specialization because
removing it from the model caused a large drop in goodness-of-fit
measurements (Table 2). This pattern makes intuitive sense as the type
of prey eaten by harbor seals (Lance et al., 2012; Olesiuk et al., 1990)
as well as their dive foraging behavior (Wilson et al., 2014) vary
throughout the year. Therefore, changes in foraging behavior (both prey
choice and dive type) were likely mechanisms behind the observed change
in relative specialization throughout the year. However, there were
likely other factors influencing relative specialization in addition to
month.
Sex also had an impact on relative specialization, yet smaller than that
of Month (Table 1). Differences in the level of relative specialization
between female and male harbor seals were likely due to females and
males in the region eating different prey items and having different
foraging strategies (Bjorland et al., 2015; Schwarz et al., 2018; Wilson
et al., 2014). For instance, females more often perform deeper foraging
dives, eat benthic prey more commonly, and have smaller home ranges than
males ((Peterson, Lance, Jeffries, & Acevedo-Gutiérrez, 2012; Schwarz
et al., 2018; Wilson et al., 2014). Therefore, we propose the following
theoretical resource distribution: males have more overlap between
individuals while the females have less overlap between individuals;
variations in this pattern appear to be associated with prey type (which
will be addressed in the following section) (Figure 6).
Including an interaction term between Month and Sex increased the
goodness-of-fit of the model (Table 2). This result indicates that
differences between male and female seals likely varied throughout the
year. Specifically, there were clear decreases in relative
specialization in male harbor seals during the summer and fall months
that were not reflected in females (Figure 2), indicating that the
behavior of both sexes was similar in the spring but diverged in the
summer and fall. This behavior was likely due to changes in feeding
patterns of females and males throughout the year (Lance et al., 2012;
Wilson et al., 2014). A possible reason for the different feeding
patterns in the summer months is female change in behavior due to
pupping (Ternte, Bigg, & Wigg, 1991). While nursing, females spend most
of their time on the haul-out and make short foraging trips (Boness,
Bowen, & Oftedal, 1994; D’Agnese, 2015). A similar difference was seen
between sexes during the fall; however, both sexes were relatively least
specialized during the fall. During the fall, there is a large influx of
returning adult Salmoniformes (Quinn, 2005) that are preyed upon by both
female and male harbor seals (Schwarz et al., 2018). In the Salish Sea,
Salmoniformes can compose >50% of the population diet in
the summer and fall (Lance et al., 2012). This resource could be rich
enough that it is beneficial for a majority of seals, both males and
females, resulting in less need for specialization. This explanation is
further supported by the correlation between feeding on adult
Salmonifomes and a relatively less specialized diet (Table 3),
indicating it was a widely used resource in the region.
Our data also suggest that location explained a large amount of variance
in relative specialization. The random factors of Year and Location
increased the \(r^{2}\) by more than four times, indicating that both
had a large influence on relative specialization. However, because
Sample Size, Location, and Year explained 0.39, 0.36, and 0.002 of the
variance (SD = 0.62, 0.597, 0.05), respectively, one can assume that
Sample Size and Location were the random factors responsible for the
increase in goodness of fit of the model, not Year. This result
indicates that where the seals were foraging impacted the level of
relative specialization in the population, without noticeable changes
from year to year. Our results also indicate that there was likely some
bias introduced by the number of samples in a group. For instance, there
was a correlation between average \(\text{PS}_{i}\) and theoretical
minimum \(\text{PS}_{i}\) (rho = -0.231, p = 0.03). However, this
potential bias is unlikely to have had a substantial effect on the
outcome of our study because we included sample size as a random
variable in the model and variation in sample size does not appear to
explain the seasonal changes in \(\text{PS}_{i}\) (Figures 2, 3).