1. Introduction
Population and ecosystem dynamics are key ecological processes to
monitor as ecosystems undergo anthropogenic alterations due to habitat
fragmentation and loss (Fahrig 2003; Mantyka-Pringle et al. 2012) and
climate warming (Parmesan & Yohe 2003; Scheffers et al. 2016). Species
have responded through changes in ecological processes including shifts
in phenology (Parmesan & Yohe 2003; Visser & Both 2005), changes to
foraging behaviour (Mahan & Yahner 1999), altered habitat
use/distribution (Mantyka-Pringle et al. 2012; Kortsch et al. 2015), and
reduced reproductive/survival rates with resulting declines in
population abundance (Fahrig 2003; Scheffers et al. 2016). These changes
in species abundances and distributions can lead to altered community
structure and trophic interactions (Rall et al. 2010; Molinos et al.
2015; Scheffers et al. 2016) as well as regime shifts (Petchey et al.
1999; Kortsch et al. 2014), with implications for ecosystem function and
stability (de Ruiter et al. 1995; Neutel et al. 2002; Rall et al. 2010).
Changes in community structure are especially critical to ecosystems
where higher trophic levels are vulnerable to anthropogenic change
because altered top predator population dynamics can cause cascading
effects (Shackell et al. 2010).
Examining energy dynamics over time can provide insights into ecological
responses to both natural and anthropogenic change. Bioenergetics has
been studied at individual/species levels using ingestion and
assimilation rates (Bailey & Mukerji 1977; Cressa & Lewis 1986), prey
consumption estimates (Lantry & Stewart 1993), and metabolism (Lam et
al. 1991). Furthermore, broader-scale energetics studies have documented
patterns in population energetic requirements (Markussen & Øritsland
1991; Ryg & Øritsland 1991; Ernest et al. 2003) and ecosystem energetic
dynamics across trophic levels (Sakshaug et al. 1994). Bioenergetics
research at various scales is useful for monitoring ecological patterns
given that alterations in individual energetic balances may lead to
changes in population dynamics (Yodzis & Innes 1992; Humphries et al.
2004). Thus, understanding temporal dynamics in energetics and
relationships to environmental conditions may provide insights into the
mechanisms influencing population dynamics and improve our ability to
predict how populations may respond to future stressors.
The Arctic marine ecosystem has experienced rapid and extensive changes
in sea ice in response to climate warming (Comiso 2002; Stirling &
Parkinson 2006; Stroeve & Notz 2018; IPCC 2019). In particular, reduced
sea ice extent and earlier sea ice breakup are major factors that
influence the life history of many Arctic marine species (Comiso 2002;
Stirling & Parkinson 2006; Meier et al. 2014), especially sea
ice-dependant marine mammals (Laidre et al. 2008, 2015; Post et al.
2009; Wassman et al. 2011). For example, polar bears (Ursus
maritimus ) are particularly vulnerable to sea ice decline (Stirling et
al. 1999; Stirling & Derocher 2012) because they rely on sea ice for
movement, reproduction, and as a platform from which to hunt their main
prey, ice-associated seals (Stirling & Archibald 1977; Smith 1980). As
both a top predator and a species sensitive to sea ice conditions, polar
bears are particularly useful for monitoring changing Arctic marine
ecosystem dynamics. The Western Hudson Bay (WH) polar bear population is
an example of a long-term monitoring program where individuals have been
captured and measured over three decades, which provides a unique
opportunity to examine energetic dynamics relative to sea ice habitat.
Declines in WH polar bear body condition (Sciullo et al. 2016),
reproductive rates (Stirling et al. 1999), survival (Regehr et al.
2007), and abundance (Lunn et al. 2016) have all been associated with
climate warming. Such changes to population dynamics are influenced by
individual condition and energy balances (Yodzis & Innes 1992;
Humphries et al. 2004), which in turn are driven by alterations in
energy intake and expenditure (Pagano et al. 2018). The open water
period Hudson Bay, during which polar bears fast on land, has lengthened
(Stern & Laidre 2016) and an increase to a 180 day fasting period is
predicted to result in increased starvation and mortality rates (Molnár
et al. 2010, 2014; Pilfold et al. 2016). It is therefore important to
examine energetic dynamics at various levels and long-term monitoring
can provide important insights into top predator bioenergetic responses
to climate warming and implications for ecosystem dynamics.
Energetics has been examined in polar bear populations using a fat
condition index (Stirling et al. 2008), metabolic rates (Pagano et al.
2018), body condition metrics and fasting (Atkinson & Ramsay 1995;
Robbins et al. 2012; Rode et al. 2018), and lipid content (Sciullo et
al. 2016). Additionally, the use of body measurements to estimate
individual energetic stores can provide insights into energetic
dynamics. For example, storage energy and energy density have been used
to quantify energy budgets for individual polar bears (Molnár et al.
2009, 2010; Sciullo et al. 2016). Storage energy represents the energy
that is available for maintenance, reproduction, and growth, and is
influenced by energy intake and expenditure (Molnár et al. 2009, 2010;
Sciullo et al. 2016). However, because not all energy is available for
use when individuals are fasting, energy density is another useful
metric as it accounts for the energy content per unit mass (Molnár et
al. 2009, 2010; Sciullo et al. 2016). These measures are both
informative for understanding changes in individual energy balances, as
well as predicting changes in population dynamics in response to future
conditions.
We used data on population abundance, age/sex structure, and
morphometrics collected from WH polar bears to estimate the population
energy density and storage energy from 1985 to 2018. Our objectives were
to: 1) examine temporal dynamics of energy in the WH population, 2)
assess the influence of environmental conditions on population energy,
and 3) explore lagged effects of environmental variables. In addition,
we analyzed energy dynamics within the population to provide insights
into intra-population variation and examine the vulnerability of
different age/sex classes based on energy balances. This research
increases our understanding of the temporal and intra-population
energetic patterns of a top predator experiencing habitat loss due to
climate warming, as well as potential implications for Arctic marine
ecosystem dynamics.