Larger body size leads to greater female beluga whale ovarian
reproductive activity at the southern periphery of their range
Steven H. Ferguson*1,2, David J.
Yurkowski1,2, Justine M. Hudson1,
Tera Edkins1, Cornelia Willing2, and
Cortney A. Watt1,2
1 Fisheries and Oceans Canada, Freshwater Institute,
Winnipeg, MB, Canada
2 University of Manitoba, Biological Sciences,
Winnipeg, MB, Canada
* corresponding author steve.ferguson@dfo-mpo.gc.ca
Abstract: Identification of phenotypic characteristics in reproductively
successful individuals provides important insights into the evolutionary
processes that cause range shifts due to environmental change. Female
beluga whales (Delphinapterus leucas ) from the Baffin Bay region
(BB) of the Canadian Arctic in the core area of the species’ geographic
range have larger body size than their conspecifics at the southern
range periphery in Hudson Bay (HB). We investigated the mechanism for
this north and south divergence as it relates to ovarian reproductive
activity (ORA = total corpora) that combines morphometric data with
ovarian corpora counted from female reproductive tracts. Based on the
previous finding of reproductive senescence in older HB females, but not
for BB whales, we compared ORA patterns of the two populations with age
and body length. Female beluga whale ORA increased more quickly with age
(63% partial variation explained) in BB than in HB (41%). In contrast,
body length in HB female beluga whales accounted for considerably more
of the total variation (12 vs 1%) in ORA compared to BB whales. We
speculate that female HB beluga whale ORA was more strongly linked with
body length due to higher population density resulting in food
competition that favors the energetic advantages of larger body size
during seasonal food limitations. Understanding the evolutionary
mechanism of how ORA varies across a species’ range will assist
conservation efforts in anticipating and mitigating future challenges
associated with a warming planet.
Key words: age, body length, Delphinapterus leucas , fitness,
geographic range, ovarian corpora
Introduction
Evolution occurs through natural selection whereby individuals with
greater fitness contribute disproportionately more genetic information
to future generations. In addition to this individual variation,
populations will vary due to adaptations to different environmental
selection pressures (Orsini et al. 2008; Coulon et al. 2008; Pauls et
al. 2013). Population-level differences in fitness could then vary
geographically along an environmental gradient, such as altitude or
latitude, based on location within the species’ range (Kirkpatrick and
Barton 1997; Peterson et al. 2011). For example, sink populations at the
periphery of a species’ range are constantly in phenotypic flux due to
the demographic challenges of living in an environment where
species-specific traits are less well adapted compared to populations
near the core of the species’ range (Gaston 2009; Sheth and Angert
2016). It is critical to understand the extent of species-level
plasticity that allows individuals to track extreme environmental
selection pressures at the edge of their geographic range in our rapidly
changing world to inform conservation (Hardie and Hutchings 2010;
Valladeres et al. 2014).
Populations at the core of the species’ range, where individuals are
presumably most suitably adapted to their environment, likely differ
from populations at the range periphery where greater phenotypic
variation occurs. Reproductive activity is costly and offers a
potentially relevant metric to assess the suitability of females to
their environment. The ovaries of many mammals provide an index of
ovarian reproductive activity (ORA; Marsh et al. 1984; Ellis et al.
2018) and therefore researchers have used lab examinations of female
reproductive tracts from sustainably hunted individuals to identify the
number of ovarian corpora (Lehmann 1993; Nazarova and Evsikoy 2012;
Ringsby et al., 2009). Whales are distinct in that their corpora
albicantias (CA) physically remain for the duration of the whale’s life,
providing a possible way to track an individual’s historical record of
reproductive events and number of lifetime ovulations (Perrin et al.
1984; Ellis et al., 2018). As a result, we can examine the ovarian
reproductive history of individual whales since each CA represents one
ovulation, although not necessarily a pregnancy (Berta et al. 2015).
During ovulation, an oocyte is released from the Graafian follicle with
the rupture site forming the corpus luteum (CL), a temporal bright
yellow, hormonal gland helping to promote and to maintain implantation
of the embryo. Subsequently, this body regresses to a CA which is
generally considered to persist within the ovarian tissue throughout the
life of a female whale even after diminishing in size and color
(Mackintosh, 1942; Laws, 1961; Fujino 1963).
Relating phenotypic characteristics to lifetime reproductive activity
can provide important insight into evolutionary processes and allow
comparisons between populations that may indicate adaptation (Peterson
et al. 2019). We thus need to assess the contribution of variation in
phenotypic traits, such as body size, to reproductive variation
(Gaillard et al. 2000), in order to understand key variables for
survival and reproductive success.
Large mammalian females are generally considered to be capital breeders
(Stearns 1992) and, therefore, should illustrate a strong relationship
between individual body size and reproductive activity. Despite
relationships between reproductive metrics and body size being
investigated across several mammalian species (pinnipeds (Le Boeuf &
Reiter 1988), ungulates (Clutton-Brock et al. 1988), and rodents (Ribble
1992), this relationship has not been demonstrated in whales, likely due
to the logistical difficulties of measuring adult body size and
reproduction over an individual’s lifetime. Odontocete (toothed) whales
generally live in cooperative societies where selection on female
dominance likely operates through variation in body size (Ward et al.
2009; Croft et al. 2017). Despite this, no studies have investigated the
factors that influence female ORA, age, and body size in odontocete
whales.
There are 21 beluga whale (Delphinapterus leucas ) populations
across the Arctic providing a latitudinal continuum of populations
within their range (Hobbs et al. 2020). A collection of tissue samples
provided by Inuit hunters during subsistence hunts from across the
eastern Canadian Arctic have been archived by Fisheries and Oceans
Canada and include female beluga whale reproductive tracts with ovaries.
To date, this collection has revealed spatial differences in morphology,
phylogenetic history, demography, and reproduction between individuals
wintering in the Hudson Bay (HB) region, compared to those wintering in
Baffin Bay (BB) (Postma 2017; Ferguson et al. 2020) (Fig. 1). For this
study, we chose to compare the HB whales, representing adaptations to
life at the southern periphery of the beluga whale geographic range
(59o latitude), to BB whales (73olatitude) representing adaptations to life within the core of the
species’ range. Knowing that HB whales are smaller than BB whales
(Stewart 1994), our objective was to determine whether female body size
differences relating to ORA occurred between peripheral HB and core BB
regions while controlling for age. Specifically, we determined how
variation in ORA, measured as total ovarian corpora counts, relates to
body size of female beluga whales from both populations.
Methods
The dataset included 172 female reproductive tracts with at least one
corpus: 41 from BB and 131 from HB. To create a complete dataset
required for robust statistical testing (Moritz & Bartz-Beielstein
2017), missing length and age data were replaced with the median value
of all whales in each population. The five BB whales with missing age
were assigned 20 years-of-age and the 6 HB whales, 26 years-of-age.
Similarly, the 6 BB whales with missing length were assigned 354 cm and
the 17 HB whales with missing length, 327 cm. We conducted post-mortem
gross examinations of female reproductive tracts, collected from 17
northern communities within the Eastern Canadian Arctic from 1989 to
2014 (Fig. 1). Ageing was based on examination of dentine and cementum
growth layer groups in teeth (Waugh et al. 2018). Whale standard length
was measured in the field according to a standard protocol, measured
from the middle of the fluke to the tip of the rostrum (American Society
of Mammalogists, 1961). We combined reproductive morphology data for two
northern populations (Cumberland Sound and high Arctic) into a BB region
based on a similar growth-age-reproduction relationship (Ferguson et al.
2020). For consistency in terminology, we refer to BB and HB as
populations while recognizing that each region likely comprises a number
of sub-populations (Skovrind et al. 2021).
Sample processing is described in more detail in Ferguson et al. (2020);
briefly, ovaries were excised, weighed, measured, and preserved in 10%
neutral-buffered formalin. For each ovary, we recorded the number of CLs
and CAs (Best, 1968). In cetaceans CLs and CAs form distinct and
persistent features, accumulating within the ovary (Perrin et al. 1976)
as a record of a female’s potential reproductive history (Slijper 1962;
Collet and Harrison et al., 1972; but see Dabin et al. 2008). Corpora
assessments were performed by one reader to minimize bias in the
subjective determination of accessory corpora (Harrison, 1977). As a
measure of ORA, we counted all existing CLs and CAs within the beluga
whales’ ovaries. For whales with only one ovary sampled (23 of 97 whales
from BB and 113 of 210 whales from HB), we doubled the corpora count
since beluga whales do not appear to exhibit a side-dominance in ovarian
function (Robeck et al. 2010; Sheldon et al. 2019).
Statistical analysis
A Generalized Linear Mixed Model fit by maximum likelihood (Laplace
approximation) with a Poisson distribution (Coxe et al. 2009) was used
to assess differences in ORA between the two regions. Poisson regression
models are best used for modeling events where the outcomes are counts
or, more specifically, discrete data with non-negative integer values.
Generalized Linear Models are models in which response variables follow
a distribution other than the normal distribution. Knowing that the
relationships between ORA and age or body length are non-linear
(Lamaître et al. 2015), we transformed the non-linear relationship to
linear form using a link function creating a log-linear model, whereby
the coefficients are calculated using maximum quasi-likelihood. Region
(categorical), age (continuous), and length (continuous) were included
as fixed effects and year as a random effect. Model selection was guided
by Variance Inflation Factors (VIF) and Akaike’s information criterion
for small sample size (AICc) using information theory (Burnham and
Anderson 2002). We calculated log-likelihood (LL), AICc values, ∆AICc,
and AICc weights (wi – relative likelihood of the model) using MuMIn
(version 1.43.17; Zuur et al., 2009; Mazerolle, 2019). First (Step 1),
we tested the full model to determine whether the effect of length and
age on ORA differed by region. Then (Step 2), we addressed
region-specific relationships by removing region as a fixed effect and
running separate models for each region. Our study employed a limited
set of a priori models (i.e., n = 6), and therefore we report all top
models (Delta (∆AICc) < 3.0) while accepting that models with
AIC scores near the top-ranked model might not be as competitive based
on consideration of model deviance (Burnham and Anderson 2002; Arnold
2010). All statistical analyses and graphics were performed using R
statistical software (v. 3.6.3).
The effect of body size on ORA was assessed for each region separately
while controlling for whale age. We used partial correlations which
measured the “unique” contribution of an independent variable (age and
body length) to r2 of the model. Here, we followed the
“hierarchical analysis procedure” where the order of variable entry
affected analysis and interpretation of partials (Cohen and Cohen 1975).
As a result, length was the first predictor variable entered into the
model, due to length being the primary variable of interest to answer
our hypotheses, followed by age. The partial correlation analysis
assumes linearity in the relationships among ORA, age, and length, which
we tested with residual plots (Zuur et al. 2010). To display possible
nonlinearities, we used LOESS (locally estimated scatterplot smoothing)
in the figures as a non-parametric regression method that combines
multiple regression models in a k-nearest-neighbor-based meta-model
(Owolabi et al. 2016).
Results
Whales from HB displayed greater ORA (range 1-35, median 8, mean 10.3)
than BB (range 1-23, median 6, mean 7.8) although the difference was
marginal (F1,170=3.78, p=0.05). For age distribution, BB
whales (range 8-46, median 21, mean 23.5) were younger than HB whales
(range 10-68, median 27, mean 29.5; F1,170=7.77,
p<0.01). For length, BB whales (range 204-447, median 362,
mean 359.6) were larger than HB (range 189-455, median 333, mean 333.5;
F1,170=12.2, p<0.001) (Fig. 2).
The effect of region (BB and HB) (Step 1) on ORA was assessed in a
complete model (ORA ~ length + age + region+ (1
| year)). Model selection supported two different models (ΔAIC
< 1) (Table 1). One of these models found a difference in ORA
between regions (Delta = 0.160) and in conjunction with prior knowledge
of regional differences, we contrasted BB and HB using separate GLM
models to discover any region-specific age and length relationships
(Step 2). For BB beluga whales, length was not a predictor of ORA
whereas both age and length were predictors for HB beluga whales (Table
1).
Finally, we used partial correlations to account for explained variation
only attributable to length. For BB beluga whales, length explained
0.4% of variation in ORA while controlling for age, whereas age
explained 63.6% of variation in ORA. For HB beluga whales, length
explained 5.7% of the variation in ORA while controlling for age,
whereas age explained 41.4% of variation in ORA. For BB beluga whales,
the rate of increase in ORA with age was 1.5 times greater than HB (0.50
versus 0.33 ORA per year, t = -2.17, p = 0.031), while the rate of
increase in ORA with length did not differ between populations (t =
0.53, p = 0.96; Fig. 3). However, HB whales had higher ORA for similar
body lengths (t = 2.95, p = 0.0037). Length explained 1% of the total
variation in ORA for BB beluga whales (0.4% / (0.4% + 63.3%) * 100%)
compared to 12% of ORA explained by length for HB (5.7% / 5.7% +
41.4%) * 100%).
Discussion
Population-level differences in ovarian reproductive activity (ORA)
could be an adaptation to environmental selection pressures that vary
along latitudinal gradients (Orsini et al. 2008; Coulon et al. 2008;
Pauls et al. 2013). Although ORA did not differ among beluga whales
along a latitudinal continuum, for the southern population of female
beluga whales at the periphery of the species’ geographic range, ORA was
more strongly influenced by body size than ORA of populations within the
core northern range. Additionaly, body size was a greater predictor of
ORA for female HB beluga whales living at the southern edge of their
distribution compared to BB whales living in core northern range. If
this finding holds for other species facing similar selective pressures
from climate warming, then our results provide critical information on a
mechanism of redistribution and underscores limits to opportunities for
adaptation in changing environments.
In females, fecundity selection, which selects for traits that increase
the number of offspring successfully raised, is a major driver of body
size, whereas in males, sexual selection is a major evolutionary force
selecting for larger body size (Ralls 1977). Fecundity selection in
females is an adaptation that needs to be balanced with survival
(Pincheira‐Donoso and Hunt, 2017). For example, selection for large
female body size is counterbalanced by opposing selective forces that
may include (1) increased risk from predation, parasitism, or starvation
because of their large size (e.g., reduced agility, increased
detectability, higher energy requirements, heat stress) and (2) a longer
developmental time to attain larger size which may result in a later age
of sexual maturity and decreased lifetime reproductive success
(Blanckenhorn 2000).
Linear increases in age with reproductive success are expected as the
number of offspring born to a female accumulates over time; however,
non-linear effects such as a decline in reproduction with advancing age
are more challenging to explain or confirm. For a limited number of wild
cetaceans, lifetime reproductive success reaches a plateau at oldest
ages when they stop reproducing (Perrin et al., 1976; Mizroch 1981;
March and Kasuya 1984). The number of beluga whale CAs has been found to
increase up to approximately 40 years of age (Brodie 1972,
Heide-Jørgensen and Teilmann 1994, Suydam 2009, Ferguson et al. 2020).
After 40 years of age, a decline in ORA was found in the HB population
at the southern limit of the species’ distribution (Ferguson et al.
2020), but there was no decline in ORA with age for the core BB
population (although there were fewer older females in the BB
hunter-collected samples compared to HB (see Ferguson et al. 2020)).
This lack of a decline in reproduction with advancing age in BB beluga
whales may characterize a growing population of younger whales
recovering from past overexploitation (Wade et al., 2012) or an evolved
life-history adaptation of a population selected for life in core range
(i.e., source vs sink; Kozłowski 1993).
It is unclear why larger body size among female beluga whales is more
strongly correlated with ORA in a population of smaller-bodied whales
living near the southern periphery of their geographic range. It is
possible that although larger body size is favored by females in the
southern population, due to the high population density relative to food
availability, they may struggle to grow to a size similar to that found
in northern areas (Luque and Ferguson 2010). For the smaller-bodied
whales of southern populations, individual selection may be strong for
large females because of the advantages accrued with greater fat storage
capability and the associated survival advantages during seasonal food
limitation (Lindsteadt and Boyce 1985). Similarly, we would predict that
southern populations would select for longer nursing duration due to the
advantages provided by greater offspring growth and survival (Beauplet
and Guinet 2007; Matthews and Ferguson 2015). In contrast, the northern
population lives at lower population density and likely without food
limitation and thus can grow to a larger body size. Food limitation in
southern areas, would contrast with density-independent limitation
through ice entrapments in northern areas, where differences in body
size may not provide survival advantages (Heide-Jørgensen et al. 2002,
Luque and Ferguson 2010).
Another consideration is the contrasting demographic history between the
two regions and how long-term changes in population dynamics can drive
differences in ORA. The pristine, pre-commercial whaling abundance of
the BB population was previously estimated to be double that of the most
recent population abundance estimate from 1996 of 21,213 beluga whales
(Innes et al. 2002; Innes and Stewart 2002). Although, the population
growth trend has been interpreted as increasing, the Baffin Bay
population as a whole is still considered depleted due to past
commercial whaling (Hobbs et al., 2020). Similarly, the Cumberland Sound
population, also located in the BB region, is considered depleted due to
past overharvesting from commercial whaling practices (Sergeant and
Brodie 1975) with a current abundance estimated at 1,381 or 15% of the
original estimated population size (Watt et al. 2020). In contrast, the
HB population is considered to be possibly the largest in the world, at
a minimum size of 54,473 beluga whales (Matthews et al. 2017). Although
considerable commercial harvesting of HB beluga whales occurred over the
past century (Mitchell and Reeves 1981), the population is likely at or
near carrying capacity (Hammill et al., 2017; Hobbs et al., 2020).
Demographic rates differed between the beluga whale populations studied
here and research has shown that long-term population dynamics can not
only fluctuate over time, but drive large differences in reproduction
(Ozgul et al. 2006; Boyce et al., 2006; this study).
Despite the large number of samples provided by Inuit hunters from
across Nunavut, the number of intact and complete female reproductive
tracts with ovaries and associated morphometric information was
moderate. As a result, we were unable to consider other covariates that
may explain ORA variation, such as temporal trends that could be
associated with environmental shifts. In addition, since hunters are
somewhat selective in the size of harvested whales, there is the
possibility of bias in the whales hunted (e.g., health), although we
would expect this possible bias to be similar between our two study
regions. Another data uncertainty is whether CAs in older females become
progressively smaller and more difficult to detect (Suydam 2009).
Interpreting ORA of beluga whales is made difficult because of the
occurrence of accessory corpora (Burns and Seaman 1986) and younger
females may produce more accessory corpora than older ones (Brodie 1971;
Harrison et al. 1972; Perrin et al. 1984). Greater ORA may also indicate
more successful reproduction, resulting in the birth of calves that may
or may not survive to reproduce themselves. An unsuccessful pregnancy or
calf mortality could result in a shorter reproductive interval and
earlier ovulation resulting in a possible bias; similarly, successful
pregnancy can result in fewer CAs since ovulation does not occur during
the gestation period (Cha et al., 2012). The persistence of CAs provides
a measure of the number of successful ovulation events, but it does not
provide additional information on reproductive success following birth.
We expect these possible biases to be consistent across both regions and
are unlikely to affect the comparison of patterns between populations
located along a latitudinal continuum; however, it is a limitation of
the study. Our statistical assessment of partial correlation assumed
linear relationships among ORA, age, and length in order to partition
the variance to understand the relationship between ORA and length while
controlling for age. However, nonlinearity was evident, particularly for
age and ORA, indicating that nonparametric approaches may also be
applicable to understand questions unrelated to variance, and should be
explored with a larger sample size.
Understanding the evolutionary mechanisms for animal adaptations to
shifting environments via changes in life-history parameters will assist
conservation efforts in anticipating and possibly ameliorating future
demographic challenges associated with a warming world (Sæther et al.
1996; Stockwell et al. 2003; Hazen et al., 2013). For example,
increasing anthropogenic stress from contaminants, noise, and conflicts
with fisheries may exacerbate reproductive costs to beluga whales
(Mosnier et al., 2015; Norman et al. 2015). Furthermore, contemporary
evolution might reduce reproductive success through interactions between
population size and strength of selection making most conservation
efforts risky unless they can measure and account for changes in fitness
(Fernández and Caballero 2001). More insight is required to understand
the complex relationships between changing evolutionary pressures and
population dynamics, such as fecundity, individual body growth patterns,
sociality, and genetic traits to strengthen conservation efforts, thus
ensuring long‐term species persistence.
Acknowledgements: We thank the Inuit hunters and the Hunters and
Trappers Associations of Nunavut, Canada, for collecting beluga whale
reproductive tracts through community-based monitoring. Funding was
provided by Nunavut Wildlife Management Board, Fisheries and Oceans
Canada, and Natural Sciences and Engineering Research Council of Canada.
The Associate Editor, an anonymous reviewer and J.M. Gaillard provided
generous comments that helped improve the manuscript.
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Table 1: Modeled relationships explaining variation in female beluga
whale ovarian reproductive activity (ORA) measured as total corpora
counts relative to region (Baffin Bay: BB (n = 20), Hudson Bay: HB (n =
80)), body length (cm), and age (y). Step 1 summarizes model selection
and complete model information. Step 2 describes model information for
the lowest AIC model from each region (BB and HB) separately. Model
selection criteria includes degrees of freedom (df), log-likelihood
(logLik), AICc values, Delta (∆AICc), and AICc weights (weight –
relative likelihood of the model).