Free access
Editor's Choice

Activity, heart rate, and energy expenditure of a cold-climate mesocarnivore, the Canada lynx (Lynx canadensis)

Publication: Canadian Journal of Zoology
20 January 2022

Abstract

The energetic consequences of body size, behaviour, and fine-scale environmental variation remain understudied, particularly among free-ranging carnivores, due to logistical and methodological challenges of studying them in the field. Here, we present novel activity, heart rate, and metabolic data on free-ranging Canada lynx (Lynx canadensis Kerr, 1792) to (i) investigate intraspecific patterns of energy expenditure, particularly how they relate to body size, environmental conditions, and activity variation, and (ii) position lynx — a cold-climate, mesocarnivore — within interspecific allometries of carnivore energetics. Lynx demonstrated limited behavioural and metabolic responses to environmental conditions, despite extreme cold and moderate snow depths during our study, but marked body size patterns with larger lynx having higher activity and lower resting heart rate than smaller lynx. Compared with similar-sized carnivores, lynx were less active and had lower heart rate, likely due to their ambush hunting style, but higher energy expenditure, likely due to their cold-climate existence and access to abundant prey. Overall, lynx were more similar to other ambush hunters than to sympatric cold-climate species and mesocarnivores. Our data provide insight into the relative importance of abiotic and biotic drivers of carnivore energetics and the ways in which predators maintain energy balance in variable environments.

Résumé

Les conséquences énergétiques de la taille du corps, du comportement et de variations environnementales à petite échelle demeurent sous-étudiées, particulièrement chez les carnivores en liberté en raison de défis logistiques et méthodologiques associés à leur étude sur le terrain. Nous présentons de nouvelles données sur l’activité, le rythme cardiaque et le métabolisme pour des lynx du Canada (Lynx canadensis Kerr, 1792) en liberté afin (i) d’examiner les motifs intraspécifiques de dépense d’énergie, plus particulièrement leurs relations à la taille du corps, aux conditions ambiantes et aux variations de l’activité, et (ii) de positionner le lynx, un mésocarnivore de climat froid, au sein d’allométries interspécifiques d’énergétique des carnivores. Les lynx présentent des réactions comportementales et métaboliques limitées aux conditions ambiantes, malgré des froids extrêmes et des épaisseurs de neige modérées durant l’étude, mais des motifs de taille du corps marqués, les plus grands lynx présentant une plus grande activité et un rythme cardiaque au repos plus faible que les lynx plus petits. Comparativement à d’autres carnivores de taille semblable, les lynx étaient moins actifs et leur rythme cardiaque était plus faible, probablement en raison de leur mode de chasse par embuscade, mais de plus grandes dépenses énergétiques, vraisemblablement en raison de leur vie en climat froid et leur accès à une abondance de proies. Globalement, les lynx sont plus semblables à d’autres chasseurs par embuscade qu’à des espèces sympatriques et des mésocarnivores de climat froid. Nos données permettent de mieux comprendre l’importance relative de facteurs abiotiques et biotiques qui influencent l’énergétique des carnivores et les moyens qu’emploient les prédateurs pour maintenir leur équilibre énergétique dans des milieux variables. [Traduit par la Rédaction]

Introduction

Predators occupying high trophic positions tend to have high energy requirements due to their large body size (Kleiber 1932; Schmidt-Nielsen 1984; Peters 1986; White et al. 2019); elevated resting metabolic rates due to high muscle mass and large organs (Muñoz-Garcia and Williams 2005; but see White and Kearney 2013); and high levels of activity required to locate, pursue, and capture mobile prey (Gittleman and Harvey 1982; Carbone et al. 2005). At the same time, their energetic returns can be highly variable due to their reliance on difficult-to-find, mobile prey, requiring energetic strategies that minimize costs of activity, maximize fasting endurance, and (or) maximize energetic returns.
Within the mammalian order Carnivora, Carbone et al. (1999, 2007) describe two broad categories of predators — small carnivores feeding on small-bodied prey and large carnivores feeding on large-bodied prey — separated at an energetically defined body size threshold of 14.5 kg (see Fig. 1). Below this body size threshold, a combination of the smaller body size of the predator — which reduces overall energy expenditure — and the small body size of the prey — which are abundant, require shorter search distances, and brief capture and killing phases (Griffiths 1980) — makes specialization on smaller bodied prey energetically advantageous and sufficient to meet energetic needs. Above this body size threshold, a high-cost lifestyle, brought on by larger body size, longer search times for prey, faster chases, and greater power requirements, is compensated by greater energetic returns from large-bodied prey, making specialization on large-bodied prey the most energetically feasible strategy. What is less clear, is what strategy makes the most sense for intermediate-sized carnivores (∼10–15 kg, also called mesocarnivores; Roemer et al. 2009; Ray 2000), and particularly, those approaching the 14.5 kg size threshold (Carbone et al. 2007), since they are likely too big to be an energetically efficient small carnivore but too small to be an effective large carnivore that hunts and overpowers large prey.
Fig. 1.
Fig. 1. The relationship between predator mass and mean prey mass (kg; note that the x axis is on a logarithmic scale). Prey mass represents a mean mass of all prey species that represent >25% of the predator’s diet (data from De Cuyper et al. 2019). Carnivore families are differentiated by colours (Felidae in red, Mustelidae in light grey, and Canidae in dark grey) and Canada lynx (Lynx canadensis) are highlighted in purple. The vertical broken line represents Carbone et al.’s (2007) energetic threshold differentiating carnivores that can sustain themselves on small prey vs. those that require large prey (14.5 kg). At the mesocarnivore size range (10–15 kg), the mean prey mass ranges from a lot smaller in size (<1 kg), to similar in size (∼10 kg), to a lot larger in size (>50 kg) than the predator. Colour version online.
Insight into how predators of all sizes balance energetic costs and gains across ecologically relevant time and spatial scales has been limited by the logistical challenge of obtaining fine-scale behavioural and metabolic data from free-ranging predators. Although energetic traits of free-ranging carnivores have been estimated previously, they are most often based on activity-specific metabolic rates measured in captivity or predicted allometrically (e.g., Aldama et al. 1991; Young et al. 2012; Newbury and Hodges 2019), or through doubly labeled water over short intervals, without accompanying activity measures (e.g., Winstanley et al. 2003; Dekar et al. 2010; Barbour et al. 2019). These methodological constraints inevitably restrict the types and amount of environmental variation animals are exposed to, the timeframes over which data are collected, and insight regarding multifactorial contributors to variation in energetic traits. Particularly, the inability to track metabolic and behavioural variation of individuals over time has limited in-depth analyses of intraspecific variation in energetic traits, which is not well predicted from interspecific patterns and, in most cases, remains poorly documented and largely unexplained.
However, recent technical, methodological, and conceptual advances are enabling new insight into energetics of free-ranging wildlife via biologged energetic proxies such as acceleration and heart rate. Accelerometers provide both fine-scale activity and behavioural data, providing an effective method to assess energy expenditure based on detailed activity time budgets (Wilson et al. 2006; Halsey et al. 2009; Gleiss et al. 2011; Wilmers et al. 2017). Biologgers recording heart rate have been used in conjunction with accelerometers to provide more complete estimates of energy expenditure across behavioural states, including when animals are at rest (Green et al. 2009; Green 2011). A handful of studies have used accelerometers and GPS collars alongside traditional measures of energy expenditure (i.e., respirometry or doubly labeled water (DLW)) to investigate behavioural variation and the energetic costs of hunting of completely free-ranging terrestrial carnivores. However, to date, most have focused on large carnivores specialized on large-bodied prey (e.g., cougars, Puma concolor (Linnaeus, 1771): Williams et al. 2014; leopards, Panthera pardus (Linnaeus, 1758): Wilmers et al. 2017; polar bears, Ursus maritimus Phipps, 1774: Pagano et al. 2018) and (or) species with particularly metabolically demanding hunting strategies (e.g., cheetahs, Acinonyx jubatus (Schreber, 1775), and African wild dogs, Lycaon pictus (Temminck, 1820): Gorman et al. 1998; Scantlebury et al. 2014; Hubel et al. 2016). Little is known about the interdependency of body size, activity, and metabolic requirements in free-ranging carnivores, particularly in the 10–15 kg size range.
Canada lynx (Lynx canadensis Kerr, 1792; hereinafter lynx) are mesocarnivores weighing ∼6 to 14 kg that prey on mammals and birds weighing <2 kg (i.e., much smaller than themselves; Fig. 1). Within the northern boreal forest, lynx specialize on snowshoe hares (Lepus americanus Erxleben, 1777), which are characterized by marked variation in abundance (i.e., 8- to 10-year population cycles; Krebs et al. 2018), but at peak densities are abundant on the landscape. Lynx populations vary according to the abundance of snowshoe hares, undergoing their own 8- to 10-year population cycle that lags 1 to 2 years behind snowshoe hare highs and lows (O’Donoghue et al. 1997, 1998a). Like most felids, lynx are generally solitary and considered ambush hunters spending much of their time at rest, ambushing prey from beds or stalking and chasing them for only short distances (Murray et al. 1995; O’Donoghue et al. 1998a). However, lynx are one of the few cold-climate members of the family Felidae, occupying the world’s coldest forested biome and one of the most seasonal environments. Although they are well adapted to their northern climate, including large, snowshoe-like feet providing low foot load for travelling in snow (Murray and Boutin 1991; Buskirk 2000) and thick, well-insulated winter pelts (Hammel 1955), their intermediate body size could impose cold-climate thermoregulatory demands, increasing the energetic cost of both resting and activity. Thus, lynx energetics are likely to be a compromise between a low-cost lifestyle enabled by ambush predation, a high-cost lifestyle imposed by a carnivorous diet and living in a cold, snowy climate, and a size-constrained lifestyle of being a large-bodied mesocarnivore reliant on small-bodied prey.
In this paper, we use animal-borne biologgers to document activity levels, movement, and heart rate alongside the first-ever estimates of daily energy expenditure (DEE) (using DLW: Speakman 1997) of free-ranging lynx in winter, during peak abundance of their primary prey (i.e., snowshoe hares). Our first objective was to assess the nature and degree of intraspecific and environmentally driven variation in energetics and behaviour of lynx. Based on interspecific allometries of physiological traits, we predicted that (i) larger bodied lynx would express higher activity levels and DEE (whole body expenditure), but lower heart rates (mass-specific expenditure) than smaller bodied lynx. Due to increasing thermoregulatory requirements in the cold, we predicted that (ii) resting heart rate would increase as air temperature (Ta) decreases and (iii) activity could either decrease to reduce movement costs in the cold or increase to generate heat and aid in thermoregulation. Finally, due to increasing costs of locomotion in the snow, we predicted that (iv) active heart rate and DEE would increase with increasing snow depth and (or) activity would decrease when snow was the deepest. Our second objective was to test the hypothesis of Carbone et al. (2007) that carnivores around the 14.5 kg threshold exhibit energy conservation strategies since they necessarily expend more energy than small carnivores, due to their body size, but are limited by the small size of their prey and (or) rates of processing or assimilation of food (i.e., intake is not unlimited). We test this hypothesis at (i) an interspecific level by comparing the energetic traits of lynx in relation to other carnivores above or below the 14.5 kg body mass threshold and (ii) at an intraspecific level by comparing how large- and small-bodied lynx in our study population situated around this threshold differ in energetic and behavioural traits.

Materials and methods

All research was conducted along 50 km of the Alaska Highway, between Kluane Lake and Haines Junction, Yukon, Canada (61°N, 138°W), in a study area of approximately 300 km2 (more details are available in section S.1.1 of the Supplementary material).1 During our study period (November 2015 to April 2018), while densities of both snowshoe hares and lynx changed slightly from year to year, both species were relatively abundant (i.e., increasing and (or) at peak densities) in comparison with the low phases of their cycles (Krebs et al. 2019). Lynx are non-reproductive throughout the winter, but may start to breed into late March or early April (i.e., towards the very end of our collaring period). Male lynx tended to be solitary, whereas several of the female lynx that we collared were observed with offspring from the previous year and (or) other adult lynx. Snow was present on the ground between November and April in all years (mean depth of 25 cm; maximum depth of 69 cm). Mean monthly Ta ranged from –16.3 °C in December to –9.3 °C in March, and mean daily Ta ranged between –36.4 °C and 6.5 °C across all three study seasons.

Animal capture and handling

Approval for this study was granted by animal care committees from McGill University (Animal Use Protocol #4728), Trent University (AUP #24103), and the Yukon Territorial Government (Wildlife Research Permits WRP #0141 and WRP #0174; Scientist and Explorers Permits 15-01S&E, 16-02S&E, and 17-03S&E). Lynx were trapped using a combination of home-made wire mesh box traps (dimensions: 0.9 m wide by 1.2 m long by 1.0 m tall; adapted from Kolbe et al. 2003) and commercially available metal Tomahawk traps (dimensions: 0.5 m wide by 1.2 m long by 0.7 m tall; Tomahawk Live Trap, Hazelhurst, Wisconsin, USA) that were baited with commercially available scent lures, animal carcasses, and (or) visual attractants. Traps were checked every 12 to 24 h and were closed if nighttime Ta fell below –30 °C or if significant precipitation was expected. When a lynx was found in a trap, it was transported to a field station (in 2016; ∼15 km) or a nearby veterinary clinic (in 2017 and 2018; ∼40 km) for immobilization and handling. Upon arrival, lynx were chemically immobilized using a combination of ketamine hydrochloride (Ketalar), medetomidine (Domitor) or dexmedetomidine (Dexdomitor), and midazolam (Versed). They were then weighed and given a unique combination of coloured ear tags (standard nylon rototags; Nasco Education, Newmarket, Ontario, Canada) for individual identification. Every lynx was outfitted with a GPS collar (model Iridium GPS, 400 g (Followit, Sweden), or model Remote Download GPS, 350 g (Telemetry Solutions, USA)) that had a tri-axial accelerometer (model Axy3 or Axy4, 4 g; Technosmart, Italy) attached on the dorsal side of the collar. GPS collars recorded lynx locations every 15 or 30 min, and accelerometers were set to record at 1 Hz (in 2015–2016 and 2016–2017) or 10 Hz (in 2017–2018) with a resolution of ±8gforce. In 2018, heart rate loggers (model DST milli-HRT V17, 11.8 g; Star-Oddi, Iceland) were implanted subcutaneously in four lynx (2 males and 2 females) by a trained veterinarian (M. Oakley, DVM, Haines Junction, Yukon, Canada). These loggers were set to record heart rate once every 2 min using 200 Hz ECG sampling frequency mode, starting at least 24 h post surgery. After handling was complete, lynx were transported back to the location of their capture where they were released. To retrieve implantable dataloggers, lynx were recaptured up to 50 days after their initial surgery (33 ± 14 days, mean ± SD) and the same surgical procedures were used to retrieve dataloggers. Accelerometers were retrieved either through scheduled drop-offs of the GPS collars or when or if lynx were recaptured. More information on immobilization and surgery methods can be found in section S.1.2 of Supplementary material.1

Daily energy expenditure (DEE) measurements

While anaesthetized, a subset of lynx were injected with 4.0–4.5 mL of DLW (61% 18O (oxygen) and 33% 2H (deuterium)) intraperitoneally. Due to the low likelihood of recapturing and obtaining repeated blood or urine samples from individual lynx within a short time frame, which is most often what the DLW method relies on, we used fecal sampling to determine isotope elimination rates in free-ranging lynx. Several studies have tested and applied a fecal sampling approach for DLW estimates of DEE and have determined that there is no significant difference in isotope elimination rates or estimates of total energy expenditure when using feces compared with urine and (or) blood across taxa (e.g., on cheetahs: Scantlebury et al. 2014; on reindeer, Rangifer tarandus (Linnaeus, 1758): Gotaas et al. 1997). We collected repeated fecal samples from DLW-injected lynx by following GPS tracks of a given individual on foot (i.e., snowshoes) from 48 h post injection to 2 weeks post injection (for more information see section S.1.3 of the Supplementary material).1 We collected all fecal samples observed along the track of a focal lynx and, for every sample, recorded date and time of collection, GPS location, and nearest GPS waypoint from the focal lynx’s collar to identify the likely date and time of feces production (to within 15–30 min, depending on GPS fix rate). The prevalence of snow cover and freezing Ta during our study period ensured feces were frozen until samples were recovered, after which they were stored in 50 mL metal-topped glass containers and maintained frozen at –20 °C until analysis. We successfully collected at least two fecal samples (i.e., an “initial” sample within 72 h of injection, and a “final” sample within 12 days post injection) for 13 of the 16 lynx that we injected with DLW. In addition to these samples, we collected fecal samples from one male and one female per sampling year (2015–2016 and 2016–2017) from lynx traps (i.e., before injecting DLW) to measure background isotope enrichments.
All fecal samples were homogenized and a subsample was vacuum distilled for water collection. Isotopic enrichments (18O and 2H) of the resulting distillates were measured using a liquid water isotope analyzer (Los Gatos Research (LGR), San Jose, California, USA). Isotope enrichments of fecal samples collected more than 8 days post injection were already at background isotope levels (i.e., all DLW had been eliminated), meaning we could not use most of the “final” samples that we had collected to measure isotope elimination rates. Therefore, we used two different methods to estimate DEE, depending on the number of “viable” samples per individual. We either (1) used a single-sample approach that involved measuring isotope enrichments in a single fecal sample taken 3.8 ± 1.9 days post injection and estimating the initial enrichments of 18O and 2H, or (2) for the lynx for which we had multiple fecal samples with enrichments above background levels (n = 3), we used a two-sample approach, where isotopic enrichments were measured for both an “initial” and “final” fecal sample (i.e., no interpolation of initial enrichments required). We used estimates obtained from the two-sample approach for these three individuals and used the single-sample estimates for the rest (all DEE estimates are provided in Supplementary Table S1).1 We calculated CO2 production using a two-pool equation that incorporates mean dilution space ratio of both isotopes and was used previously for DLW estimates in large animals (eq. 17.15 in Speakman 1997; as in Scantlebury et al. 2014). Since we were unable to recapture and weigh lynx during the sampling window, we assumed body mass and total body water remained constant over the 3.7 ± 1.9 days. The CO2 estimates were converted to kilojoules of energy metabolized assuming a respiratory quotient of 0.8 (i.e., a mixed diet of carbohydrates, fat, and protein) and an associated energy equivalent of 25.1 kJ/L CO2 produced (Gessaman and Nagy 1988).

Snow and temperature measurements

From 2015 to 2017, Ta readings were taken every 30 min from four iButtons (DS1922L, Maxim Integrated) placed across an ∼ 8 km stretch of the study area. In 2018, Ta readings were taken every 4 h from two of the four locations. Ta recordings were averaged across these locations for analyses. We also measured snow depth and daily snow accumulation at three different locations, but within the same 8 km stretch of the study area. We measured snow depth to the nearest 0.5 cm at three open locations and three covered locations at each sampling site. Due to discrete snowfall and melt events, snow accumulation over the course of the study seasons tended towards depth transitions rather than continuous incremental accumulation; as a result, we binned snow depth into four categories (<20, 20–40, 40–60, >60 cm). Shallow snow tended to be associated with early winter and deepest snow with mid- to late winter, but variation across our three study years in the timing of major snow events and total winter snowfall led to mid- and late winter data points in all four categories. In addition, daily snowfall (to the nearest 0.1 cm) was measured using snow boards (i.e., a square piece of wood) that were checked and cleaned daily. Snow measurements were averaged across habitat types and locations. Locations where environmental data were recorded were fixed during the study.

Activity parameters

From GPS locations, we calculated step length (i.e., straight-line distance between two consecutive locations), movement rate (i.e., step length divided by the time period between two points), and daily distance travelled (i.e., sum of all step lengths within each 24 h period). We used a machine learning algorithm to classify accelerometer data into four known behavioural states (not moving, grooming, feeding, and walking) and an “unknown behaviour” category (for more information on behavioural calibration of accelerometers see Studd et al. 2021). From this, we calculated the proportion of each 24 h period that lynx spent active; we used the time that each lynx spent “walking” as the active state, excluding stationary behaviours like grooming and feeding. We analyzed GPS and accelerometer data between 15 November and 31 March of each year to focus on winter (i.e., period with consistent below-freezing Ta and snow cover) and to avoid the breeding season, especially pregnancy and lactation for females. We excluded the first 24 h of data after immobilization and any days when an individual was found in a trap, except for calculations of activity levels and movement during the DLW sampling period for which we included all GPS and accelerometer data between time of release and time of fecal sample production.

Heart rate data

We evaluated the accuracy and quality of the heart rate data in two ways: (1) using the real-time ECG signal processing algorithm built into the Star-Oddi dataloggers (i.e., the quality index) and (2) manually investigating raw ECG signals and comparing them to heart rate values provided by the loggers. Our data were relatively high quality, with 90% of the readings having quality index values of 0 or 1 (the two highest quality categories; see Supplementary Table S21). and were also relatively accurate, with 96% of the manually validated heart rate readings falling within 10% of the ECG-calculated values (metric used in Fuchs et al. 2019; more information is available in section S.1.4 of the Supplementary material and Supplementary Fig. S11). Although we were confident in the quality of our data, there were still readings that ranged from 0 to 1005 beats/min. So, to ensure that only plausible heart rate values were included in our analyses, we created upper and lower thresholds for filtering data informed by both ECG data collected by loggers and data available in the literature. After filtering our data, all heart rate readings fell between 24 and 294 beats/min, representing 95% of the raw heart rate readings initially obtained from the loggers (more information is available in section S.1.4 of the Supplementary material and Supplementary Fig. S2).1 We categorized all heart rate readings based on the accelerometer-classified behaviours at the exact time of the heart rate reading (i.e., every 2 min). We considered “active heart rate” as periods of time when lynx were walking, “resting heart rate” as periods of time when lynx were not moving, and in subsequent analyses, excluded periods when lynx were stationary but grooming or feeding (intermediate heart rate values during these periods are shown in Supplementary Fig. S4).1
Since we only had heart rate data for four individuals, we wanted to assess the generality of relationships between resting heart rate and body size with a larger sample size of lynx. We had recorded heart rate once every 5 min throughout the immobilization and handling period of every individual that we collared. Using this data, we assessed whether body size patterns were similar to those that we observed among the four lynx with heart rate loggers. We also assessed whether heart rate while anesthetized was correlated with resting heart rate of free-ranging animals, following recovery and release (n = 26; for more details see section S.2.4 of the Supplementary material1).

Interspecific data

To compare lynx to species that fall above and below the 14.5 kg energetic threshold, we collected data from the literature on mean prey size, activity levels, heart rate, and DEE from as many carnivore species as possible, excluding marine (e.g., pinnipeds) and omnivore (e.g., ursids) species (for all sources see Supplementary Table S3)1. To compare lynx to small carnivores specializing on small prey and large carnivores specializing on large prey (Fig. 1), we calculated a mean prey mass for every carnivore species provided in De Cuyper et al. (2019). We only included prey species that represented >25% of the predator diet and excluded any duplicates of a prey species (i.e., from different studies). We obtained the majority of data on the proportion of time spent active by digitizing data points from fig. S5 in Rizzuto et al. (2018) using WebPlotDigitizer version 4.2 (Rohatgi 2019) and added a few more from the literature (for all sources see Supplementary Table S3).1 All values included were from studies using VHF collar, GPS collars, or accelerometers to measure activity, as well as studies reporting full 24 h activity cycles. Due to the scarcity of heart rate data on free-ranging carnivores, especially medium- and large-bodied carnivores, we included any data that we could find, which included free-ranging, semi-captive, and immobilized animals. We calculated mean heart rate values, combining both active and resting, when available. For DEE data, we used Carbone et al. (2007) as a starting point and added data for additional carnivore species from the literature. In all cases, we averaged across males and females when necessary, and selected only winter data, when possible, to be most comparable with lynx data from our study. We averaged body mass values across all of the studies from which we obtained behavioural and (or) energetic data for each species since body mass variation within a species (across studies) was small compared with the level of variation across species (i.e., a 175 kg range). If a study did not report mass for a species, then we used the mean body mass from the other sources.

Statistical analyses

To determine the drivers of activity (proportion of day active) and movement (daily distance travelled), we used linear mixed-effect models (using the lme4 package in R version 3.5.2; Bates et al. 2015) with body mass, mean daily Ta, and snow depth as fixed effects, and individual ID and sampling year as random effects to account for repeated measures on individual animals within and across sampling years. Due to sexual dimorphism in lynx, sex and body mass are confounded (i.e., females tend to be smaller and males tend to be bigger). To avoid collinearity among these two variables, we included body mass in our models as opposed to sex to focus on a continuous variable (as opposed to a categorical variable with two levels), to be inclusive of within-sex size variation (i.e., body size varies within males and females), and because body mass is a well-known driver of energetic traits at an interspecific level. However, we report descriptive statistics for males and females separately in Table 1 and Fig. 1 (top panel) to highlight dimorphism in body size and similarities in other traits of interest. We used the proportion of each 24 h period spent active as the response variable, as opposed to splitting it up by phase of the day, because we did not detect obvious circadian patterns in activity, movement, or heart rate of lynx (see section S.2.1 of the Supplementary material and Supplementary Fig. S3).1
Table 1.
Table 1. The mean and variation (range and (or) 5th and 95th quantiles) of body mass, activity, movement, heart rate (HR), and daily energy expenditure (DEE) of Canada lynx (Lynx canadensis) in winter.
To statistically test effects of body mass, Ta, and snow accumulation on resting and active heart rate, we used linear mixed-effect models with individual ID as a random effect to account for repeated measures on individual lynx. We only sampled heart rate in 2018, so we did not include year in these models. We included daily snow accumulation (>0 cm) as opposed to snow depth in this analysis since variation in snow depth was minimal across the 2-month period for which we had heart rate data (90% of data points occurring when snow depth was 38–46 cm). So, instead, we looked at the effect of fresh snowfall on heart rate while active and resting. In all cases, we determined p values using lmeTest package in R (Kuznetsova et al. 2015). We also tested the effects of body mass on the mean heart rate when lynx were immobilized using a linear regression (see section S.2.4 in the Supplementary material).1 To estimate the lower critical temperature of lynx (i.e., the Ta at which heart rate begins to increase with decreasing Ta), we ran a segmented regression (using “segmented” package in R version 1.1.0; Muggeo 2008) between Ta and both resting and active heart rates. Finally, to test the effects of body mass, Ta, and snow depth on DEE, we ran a series of univariate ordinary least-squares regressions to correspond with our limited sample size.

Results

Activity and movement

We recorded activity of 9 adult female (9.0 ± 0.6 kg, mean ± SD) and 17 adult male (10.8 ± 1.2 kg) lynx across 51 ± 27 days between December 2015 and March 2018 (samples sizes broken down by year in Supplementary Table S4).1 On average, lynx were active for 21% of the day, with 90% of observations falling between 10% and 40% active (Table 1). They traveled 6.7 km daily, with 90% of observations falling between 2.3 and 13.3 km/day (Table 1). Intraspecific variation in lynx activity was related to body size and snow depth, but not Ta (for a summary of the statistical results see Supplementary Table S5).1 Overall, smaller bodied lynx tended to express slightly lower activity levels than larger bodied lynx (∼2% per kg; t[156] = 4.6, p < 0.0001), a pattern driven primarily by large-bodied males being more active than smaller bodied males and females (Fig. 2). Lynx spent 8% less time active on days when snow depth was >60 cm compared with days when snow depth was <20 cm (t[1420] = –3.5, p < 0.01; Fig. 2). Finally, activity remained relatively constant (<1% change) as Ta increased from –40 to 0 °C (t[1390] = 0.8, p = 0.5; Fig. 2). Similar patterns emerged for daily movement; distance travelled (km) by lynx was highest for the largest male lynx, slightly lower in deep snow, and remained constant across 40 °C of Ta variation (see Supplementary Fig. S5).1
Fig. 2.
Fig. 2. The relationship between proportion of day spent active and body mass (top), air temperature (middle), and snow depth (bottom) of 26 Canada lynx (Lynx canadensis; 9 females and 17 males) in winter (between 15 November and 31 March). We did not analyze data for males and females separately, but we plotted them in separate panels (top) to demonstrate the distribution and to highlight dimorphism in body size, but similarities in mean activity levels. The coral (orange–red) broken lines are trend lines. Colour version online.

Heart rate

We collected heart rate data every 2 min from two adult female lynx (mean mass = 9.2 kg) and two adult male lynx (mean mass = 10.1 kg) across 20 ± 17 days between January and March 2018. The mean heart rate of free-ranging lynx across all behavioural states was 117 beats/min. The mean active heart rate was 144 beats/min (90% of data points falling between 80 and 204 beats/min), which was 55% higher than the mean resting heart rate of 96 beats/min (90% of data points falling between 60 and 140 beats/min; Table 1 and Supplementary Fig. S41). As body mass increased, resting heart rate decreased by ∼17 beats/min per kg (t[2] = –3.6, p = 0.05; Fig. 3), while active heart did not decrease significantly (only 5 beats/min per kg; t[2] = –0.9, p = 0.5). The heart rate of anaesthetized lynx (mean = 85 beats/min) was correlated with resting heart rate of free-ranging lynx (F[1,2] = 50.0, p = 0.02, R2 = 0.96), and also decreased as body mass increased but by a smaller amount (∼2 beats/min per kg; t[30] = –2.0, p = 0.05; Supplementary Fig. S61). Ta had a small effect on resting heart rate (t[4100] = –5.6, p < 0.001), but a greater effect on active heart rate (t[1308] = –20.1, p < 0.0001); on average, as Ta decreased from 0 to –40 °C, resting heart rate increased by ∼3 beats/min, but active heart rate increased by 28 beats/min (Fig. 3). The estimated breakpoint of the segmented regression between resting heart rate and Ta across all four individuals was –15.7 °C (t[4098] = –1.2, p < 0.001; vertical broken line in Fig. 3) and for active heart rate Ta was –13.0 °C (t[1308] = –1.4, p < 0.001; vertical broken line in Fig. 3). Daily snow accumulation had no effect on resting heart rate (t[4100] = –0.7, p = 0.5), but active heart rate increased by ∼3 beats/min for every centimetre of fresh snowfall (t[4100] = 13.3, p < 0.0001; Fig. 3).
Fig. 3.
Fig. 3. Variation in resting (left) and active (right) heart rates of Canada lynx (Lynx canadensis; 2 males and 2 females) in winter. (Top) The relationship between body mass and heart rate. Declines in resting heart rate with body mass are significant, whereas declines in active heart rate are not. (Middle) The relationship between air temperature and heart rate. The breakpoints for the overall trend were determined by a segmented regression (coral (orange–red) broken lines; resting heart rate at –15.7 °C and active heart rate at –13.0 °C). (Bottom) The relationship between heart rate and fresh snow accumulation in the previous 24 h. In all cases, grey lines represent separate individuals, with larger individuals in lighter grey and smaller individuals in darker grey, and coral (orange–red) broken lines represent the overall trend (i.e., across all individuals) (see legend). Colour version online.

DLW

We located fecal samples, calculated isotope enrichments, and estimated DEE for three female (mean mass = 9.1 kg) and seven male (mean mass = 11.3 kg) lynx (samples sizes for each year available in Supplementary Table S4).1 Three individuals had more than one estimate in a single year; variation among these estimates were relatively low for two individuals (5% and 13% variation), but one erroneous measure caused high variation among measurements for the third individual (55%; see Supplementary Table S11). The mean DEE of lynx was 75.9 W (range 27.2–154.5 W). Assuming lynx consume only snowshoe hares at peak densities, the average lynx would require 1.3 snowshoe hares per day to meet these energetic demands (range 0.5–2.8 hares; for calculations see section S.2.5 of the Supplementary material1). Lynx DEE was negatively related to the proportion of time spent active (F[1,7] = 5.4, p = 0.05, R2 = 0.4), decreasing by ∼50 W for every 10% increase in activity (Fig. 4). DEE also increased with increasing snow depth by ∼25 W per 10 cm of snow (F[1,8] = 2.5, p = 0.04, R2 = 0.4; Fig. 4). Although not statistically significant, lynx DEE tended to decline with increasing body mass (∼12 W/kg; F[1,8] = –1.5, p = 0.2; Fig. 4) and tended to increase slightly with decreasing Ta (∼30 W per 10 °C; F[1,8] = 1.4, p = 0.2; Fig. 4).
Fig. 4.
Fig. 4. Daily energy expenditure (DEE) estimates of Canada lynx (Lynx canadensis; 7 males (circles) and 3 females (triangles)) in relation to body mass (top left), proportion of time spent active (top right), mean air temperature (bottom left), and mean snow depth (bottom right). Solid circles and solid triangles represent DEE estimates obtained from the two-sample method; open circles and open triangles represent DEE estimates obtained with a single sample and extrapolated initial enrichment levels (for more information see section S.1.3 of the Supplementary material).1 Only the relationships between DEE and proportion of time spent active and mean snow depth are significant, but linear regression lines (coral (orange–red) broken lines) were plotted on all figures to visualize trends. Horizontal broken lines represent the energetic returns from consuming 1.2 snowshoe hares (Lepus americanus) per day, which is the estimated daily kill or feeding rate at peak hare densities in O’Donoghue et al. (1998b) and Studd et al. (2021; for calculations see section S.2.5 of the Supplementary material1). Colour version online.

Interspecific patterns

Compared with the activity levels of 35 other carnivore species, the winter activity levels of lynx (i.e., being active for 21% of the day) fall in the bottom 10% of activity levels for all species (Fig. 5). Based on their body mass, relative to other felid species, lynx would be predicted to spend 44% of the day active (mass–activity regression for felids: F[1,14] = 6.2, p = 0.03, R2 = 0.31), which is 109% higher than the mean value from our data and within the 98th percentile of activity values expressed by any lynx on any given day. In general, carnivore heart rate decreases with body size (F[1,18] = 24.3, p < 0.001, R2 = 0.56), and heart rate of felids tended to be lower than the heart rate of canids and mustelids (Fig. 5). The mean heart rate of free-ranging lynx (117 beats/min) is only 10% lower than the 132 beats/min value predicted for their body size based on a mass – heart rate regression across 19 carnivore species (Fig. 5). Finally, at the interspecific level, carnivore DEE increased with body size (F[1,27] = 484.2, p < 0.001, R2 = 0.95) and lynx DEE was 12% higher than what would be predicted from the mean body mass of lynx in this study.
Fig. 5.
Fig. 5. The proportion of day spent active (top), heart rate (beats/min; middle), and daily energy expenditure (DEE (W); bottom) of Canada lynx (Lynx canadensis; solid purple circles) compared with other carnivore species (Felidae in red, Mustelidae in grey, and Canidae in dark grey). We included heart rate from free-ranging, semi-captive, and anaesthetized animals (indicated by an asterisk) and did the same for lynx from this study (i.e., both free-ranging and anaesthetized heart rate). Methods used for obtaining DEE estimates are indicated by symbols: the doubly labeled water method has no symbol; +, respirometry measurements; *, modeled based on activity time budgets and (or) allometric equations. Note the logarithmic axes on all three plots. Sources of carnivore activity, heart rate, and DEE data are in Supplementary Table S3.1 Colour version online.

Discussion

By outfitting lynx with a combination of accelerometers, GPS collars, implantable heart rate loggers, and simultaneously obtaining DEE estimates via DLW, we reveal novel mass- and behaviour-related variation in lynx energetics and smaller-than-predicted influences of environmental variation. Lynx activity and movement were variable, but did not vary predictably with Ta, which varied by almost 40 °C, and was only slightly lower as snow got deeper. Heart rate increased slightly in the cold and with fresh snow when travelling, but again, with a lot of variation among individuals and across conditions. Consistent with interspecific expectations, the largest lynx were slightly more active and had lower heart rate (i.e., lower mass-specific metabolic rate). However, this was not reflected in DEE results, where, if anything, DEE (i.e., whole animal metabolic requirements) was lower for larger lynx. Collectively, these results indicate lynx, in general, and the largest individuals that we sampled (∼14 kg), in particular, minimize energy expenditure by maintaining very low activity levels, and while at rest, keeping costs low, which may be essential for this cold-climate, small-prey specialist that is too big to be an energetically efficient small carnivore.
Generally, lynx lived up to their reputation as an inactive, perhaps even “lazy” cat (Thompson 1977) spending, on average, only 21% of their day active. Lynx exhibited much lower rates of activity than similar-sized mesocarnivores (e.g., ocelot, Leopardus pardalis (Linnaeus, 1758); bobcat, Lynx rufus (Schreber, 1777); Iberian lynx, Lynx pardinus (Temminck, 1827); European badger, Meles meles (Linnaeus, 1758)) and sympatric boreal canids (e.g., red fox, Vulpes vulpes (Linnaeus, 1758); coyote, Canis latrans Say, 1823; wolf, Canis lupus Linnaeus, 1758). Their activity rates were more similar to the smallest bodied mustelid species, which escape to underground and subnivean spaces when inactive (Larroque et al. 2015), and the largest felid species, all of which are also ambush hunters, but specializing on large-bodied prey, and occupying warmer, highly productive environments. If lynx activity reflects the minimum amount of activity necessary to locate and capture enough prey to satisfy energy requirements (Curio 2012), then the low activity that we observed could have been accentuated by the high abundance of snowshoe hares, which were at peak densities in their population cycle during our study period (Krebs et al. 2019). Even so, based on interspecific comparisons, it seems that activity levels are dictated more strongly by hunting style than climate, making lynx more similar to other ambush predators than to sympatric, cold-climate cursorial predators.
There was substantial among-individual variation in lynx activity that was only weakly predicted by body size and not obviously responsive to environmental variation. In our study population, the largest bodied lynx had slightly higher activity levels and travelled greater distances in a day than smaller bodied lynx. However, plotting female and male lynx separately showed that this relationship is most evident in male lynx — larger males (∼13 kg) had higher activity than smaller males (∼9 kg) — but not in female lynx, which are smaller and show less size variation. Lynx were generally less active when snow was deepest, but did not vary activity according to Ta, despite a 40 °C range of Ta variation. These patterns mirror winter activity of snowshoe hares in the same location, which is constant across the same range of Ta (Menzies et al. 2020) and decreases when snow is deep (Peers et al. 2020). Surprisingly, while lynx and snowshoe hares are both assumed to be crepuscular and (or) nocturnal, we did not find a clear circadian pattern in the lynx activity or movement data (see section S.2.1 of the Supplementary material).1 Previous studies have reported slightly higher activity in the late afternoons and early evenings, but not a nocturnal activity pattern (Kolbe and Squires 2007; Crowley et al. 2013). Overall, it appears that the daily routine of lynx activity (i.e., sleep–hunt–kill–eat–sleep, repeat) may not align with photoperiod or other environmental cues, but instead, correlate more strongly with spatial and temporal variation in prey behaviour and (or) be dictated by the energetic status of a given individual (e.g., hunting success, fasting tolerance, time since last meal; Podolski et al. 2013).
The mean heart rate of free-ranging lynx, across all individuals, days, and activity states, was 117 beats/min. Resting heart rate of free-ranging lynx averaged 94 beats/min and active heart rate averaged 144 beats/min (55% higher than resting levels). A major source of heart rate variation was interindividual variation in body mass, with smaller lynx characterized by faster heart rates than larger lynx. Across mammal species, from shrew to whale body sizes, large animals have larger hearts and slower heart rates than small animals (Williams et al. 2015). We show here that the same interspecific pattern in heart rate is present within the mammalian order Carnivora, declining from >200 beats/min in small mustelids to <100 beats/min in large felids, and within a single species. It is often the case that relationships become weaker or lower in magnitude as the taxonomic level considered is lower (i.e., moving from inter- to intra-order or from inter- to intra-species), but the observed negative relationship between body size and resting heart rate in lynx (decrease of ∼60 beats/min across 4 kg of size variation) was unexpectedly strong and steep compared with weak or non-existent intraspecific patterns found in the literature (Clark and Farrell 2011; Hezzell et al. 2013; Häggström et al. 2016). For example, Hezzell et al. (2013) document a difference of only 0.21 beats/min per kg of size variation, resulting in a 10.5 beats/min difference in heart rate for dog breeds ranging in size from 5 to 55 kg. So, while a negative relationship between heart rate and body size was expected, this relationship could be something more than a simple intraspecific manifestation of an interspecific pattern and, instead, an energy saving mechanism for larger, more active lynx. As well, felids tend to exhibit consistently lower heart rate than what is expected based on body size (i.e., to be “lion-hearted”), due to their low activity, low cost, low intensity ambush hunting styles (Williams et al. 2015), which our analysis shows to be true for lynx.
Beyond this size-related variation, there was evidence that lynx heart rate increased as Ta declined from cold to very cold, particularly when active, consistent with a thermoregulatory response to cold Ta. Results are consistent with a lynx lower critical temperature (TLC) between –10 and –20 °C, most likely around –16 °C for resting lynx and –13 °C for active lynx. The only published estimate of lynx TLC that we are aware of indicates that, for a single lynx weighing 12.6 kg, the thermoneutral extends to at least –10 °C, which was the lowest Ta that was measured (Casey et al. 1979). A previous study on bobcats, which are smaller bodied and not as cold adapted as lynx, estimated their TLC to be –2.0 °C in winter (Mautz and Pekins 1989) and a study on red foxes in Alaska (USA) estimates their TLC to be –13 °C (Irving et al. 1955). Finally, although lynx are efficient at moving through snow, we did observe a slight increase in active heart rate in response to greater accumulation of fresh snow, but more data, during periods with deeper snow and (or) greater variation in snow depth, could help investigate this further. Our biologging data shows stronger physiological responses than behavioural responses to Ta, a bit of both to increasing snow depth, neither in response to photoperiod, and both responding to variation in body size at the intra- and inter-specific levels.
Despite their low rates of activity and slow heart rates, lynx demonstrated slightly higher DEE than predicted for a carnivore of their body mass, with a DEE more comparable with similar-sized canids, which are typically highly active, cursorial species. The mean DEE of these 10 lynx, in winter, during peak snowshoe hare abundance, was ∼75 W; this rate of expenditure is equivalent to the energetic returns of killing 1.3 snowshoe hares per day (for calculations see section S.2.5 of the Supplementary material),1 which is slightly higher but comparable with previously estimated daily kill or feeding rates of lynx at peak hare densities (1.2 snowshoe hares per day; O’Donoghue et al. 1998b; Studd et al. 2021). In general, cold-climate species tend to have higher upper boundaries of DEE than warm-climate species due to increased thermoregulatory requirements (Anderson and Jetz 2005). Thus, higher than expected lynx DEE may reflect its status as a cold-climate endotherm (White et al. 2007; White and Kearney 2013) and a vertebrate-specialist carnivore (Muñoz-Garcia and Williams 2005) studied during a period of abundant resources (Fletcher et al. 2012; Auer et al. 2016). Among-individual variation in DEE was most related to activity, but surprisingly, lynx that were less active tended to expend more energy. Although activity is more costly than resting (reflected in increased heart rate while active), the predominance of inactivity in lynx time budgets (∼80%) means that the cost of resting could be the primary determinant of DEE for lynx. And, as demonstrated by our biologging data, resting heart rate was higher for smaller lynx, which tended to be less active, and lower for the largest lynx, which tended to be more active. We predicted that larger lynx would have higher energy requirements, based on intraspecific (Konarzewski and Książek 2013) and interspecific (White and Kearney 2013) patterns in the literature, but if anything, our data suggests an opposite pattern (i.e., larger lynx having similar or lower energy requirements than smaller lynx). Although the negative relationship between heart rate and body size could reflect lower mass-specific energy requirements of larger animals, the magnitude of this pattern suggests that it could be more than that and could also reflect an energy conservation mechanism consistent with patterns seen in DEE data. There is evidence from other study systems that individuals with higher activity levels can compensate for greater activity by reducing expenditure during periods of rest, leading to reduced overall energy expenditure (evidence in birds; Welcker et al. 2015), which could also explain the negative relationship between DEE and activity (i.e., lower resting costs leads to lower expenditure despite greater activity). DEE also tended to increase with increasing snow depth, which is expected due to increased locomotory costs in deeper snow (Parker et al. 1984; Crête and Larivière 2003). We also observed in heart rate data (i.e., increased active heart rate with greater daily snow accumulation). We did not find significant effects of Ta on DEE in winter, but trends were consistent with the thermoregulatory costs suggested by heart rate responses (i.e., increased expenditure in the cold). We acknowledge that these DEE results must be interpreted with caution because they are based on limited sample size, only slightly significant or non-significant trends, and a novel fecal recovery method for DLW without an initial post-injection dilution estimate. Nevertheless, the results presented here represent the only estimates available for DEE of free-ranging lynx and do generally align with biologging data collected on more individuals and continuously over time.
Through a combination of emerging biologging technology and traditional measures of energy expenditure, we show the energy requirements of lynx appear to be driven by a complex interaction between the inactive, low-cost lifestyle of ambush hunters; the constraints of relying on small-bodied, mobile prey; and less prominently, the costs of living in an environment characterized by cold Ta and deep snow. The largest lynx that we studied (∼12–14 kg) are reaching the body size threshold determined by Carbone et al. (2007) at which feeding on smaller bodied prey becomes energetically infeasible. However, this infeasibility is defined primarily by the increased costs of activity experienced by larger bodied predators, without consideration of potential compensations while at rest. The predominance of inactivity in lynx time budgets (enabled by high resource abundance and short, highly successful prey pursuits), in addition to low costs while at rest (due to good insulation and low heart rates), might allow lynx to circumvent many of the costs that their large bodies and cold-climate existence otherwise impose on them. The question remains, whether the patterns that we demonstrate here — when hares are abundant and Ta is low — change after hares crash and lynx have to travel greater distances to find fewer hares, or when Ta becomes hot and large lynx have trouble dissipating heat. Further investigations into the seasonality and cyclicity, especially as it relates to the relative importance of intrinsic and extrinsic drivers of behaviour and energy expenditure, have potential to reveal greater insight into how predators in seasonal environments achieve positive energy balance at annual or multiannual scales, and whether size-driven, behaviourally driven, and environmentally driven energy economies remain the same over time.

Contributors’ statement

A.K.M. and M.M.H. conceived the ideas and designed the study. S.B., D.L.M., and M.M.H. contributed to funding and logistics of field research. A.K.M., with the help of E.K.S., J.L.S., and RED livetrapped animals and collected the data. E.K.S. conducted behavioural calibrations of accelerometer data. A.K.M. conducted all laboratory work and other analyses. A.K.M. and M.M.H. led the writing of the manuscript. All authors contributed to interpretation of results, improving written drafts, and gave approval for submission.

Data availability

Data will be made publicly available at the time of publication.

Acknowledgements

We are grateful to Agnes MacDonald and her family for long-term access to her trapline, as well as members of the Champagne and Aishihik First Nation for allowing us to conduct fieldwork as visitors within their homelands. We are thankful for the many lynx who carried our loggers and taught us so much. We thank numerous technicians and graduate students for help in the field, as well as Michelle Oakley, DVM, for her veterinary assistance and support on this project. We acknowledge local trappers (D. Drummond, L. Goodwin, L. Graham, S. Oakley, A. Preto) who helped inexperienced graduate students successfully livetrap lynx. Thank you go to Alina Evans and Asgeir Bjarnason for their help with implantable loggers, and to D. Powers, E. Ste Marie, F. Van Oordt, and K. Elliott for help with laboratory analyses. We thank M. Peers and Y. Majchrzak for the snow data and immense support in the field. Lodging and logistical support was provided by the Kluane Red Squirrel Project (KRSP). We also thank the Assiniboine Park Zoo for their support and help testing dataloggers in the initial phases of the project.

Footnote

1
Supplementary material, tables, and figures are available with the article at https://doi.org/10.1139/cjz-2021-0142.

References

Aldama J.J., Beltran J.F., and Delibes M. 1991. Energy expenditure and prey requirements of free-ranging Iberian lynx in southwestern Spain. J. Wildl. Manage. 55(4): 635–641.
Anderson K.J. and Jetz W. 2005. The broad‐scale ecology of energy expenditure of endotherms. Ecol. Lett. 8(3): 310–318.
Auer S.K., Salin K., Rudolf A.M., Anderson G.J., and Metcalfe N.B. 2016. Differential effects of food availability on minimum and maximum rates of metabolism. Biol. Lett. 12(10): 20160586.
Barbour K., McClune D.W., Delahay R.J., Speakman J.R., McGowan N.E., Kostka B., et al. 2019. No energetic cost of tuberculosis infection in European badgers (Meles meles). J. Anim. Ecol. 88(12): 1973–1985.
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R.H.B., Singmann, H., et al. 2015. lme4: Linear mixed-effects models using ‘Eigen’ and S4. Available from https://CRAN.R-project.org/package=lme4.
Buskirk, S.W. 2000. Habitat fragmentation and interspecific competition: Implications for lynx conservation [Chapter 4]. In Ecology and conservation of lynx in the United States. Gen. Tech. Rep. RMRS-GTR-30WWW. Edited by L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, G.M. Koehler, C.J. Krebs, K.S. McKelvey, and J.R. Squires. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, Colo. pp. 83–100.
Carbone C., Mace G.M., Roberts S.C., and Macdonald D.W. 1999. Energetic constraints on the diet of terrestrial carnivores. Nature, 402(6759): 286–288.
Carbone C., Cowlishaw G., Isaac N.J.B., and Rowcliffe J.M. 2005. How far do animals go? Determinants of day range in mammals. Am. Nat. 165: 290–297.
Carbone C., Teacher A., and Rowcliffe J.M. 2007. The costs of carnivory. PLoS Biol. 5(2): e22.
Casey T.M., Withers P.C., and Casey K.K. 1979. Metabolic and respiratory responses of arctic mammals to ambient temperature during the summer. Comp. Biochem. Physiol. A Physiol. 64(3): 331–341.
Clark T.D. and Farrell A.P. 2011. Effects of body mass on physiological and anatomical parameters of mature salmon: evidence against a universal heart rate scaling exponent. J. Exp. Biol. 214(6): 887–893.
Crête M. and Larivière S. 2003. Estimating the costs of locomotion in snow for coyotes. Can. J. Zool. 81(11): 1808–1814.
Crowley S.M., Hodder D.P., and Larsen K.W. 2013. Canada Lynx (Lynx canadensis) detection and behaviour using remote cameras during the breeding season. Can. Field.-Nat. 127(4): 310–318.
Curio, E. 2012. The ethology of predation. Vol. 7. Springer Science & Business Media.
De Cuyper A., Clauss M., Carbone C., Codron D., Cools A., Hesta M., and Janssens G.P. 2019. Predator size and prey size–gut capacity ratios determine kill frequency and carcass production in terrestrial carnivorous mammals. Oikos, 128(1): 13–22.
Dekar M.P., Magoulick D.D., and Beringer J. 2010. Bioenergetics assessment of fish and crayfish consumption by river otter (Lontra canadensis): integrating prey availability, diet, and field metabolic rate. Can. J. Fish. Aquat. Sci. 67(9): 1439–1448.
Fletcher Q.E., Speakman J.R., Boutin S., McAdam A.G., Woods S.B., and Humphries M.M. 2012. Seasonal stage differences overwhelm environmental and individual factors as determinants of energy expenditure in free‐ranging red squirrels. Funct. Ecol. 26(3): 677–687.
Fuchs B., Sørheim K.M., Chincarini M., Brunberg E., Stubsjøen S.M., Bratbergsengen K., et al. 2019. Heart rate sensor validation and seasonal and diurnal variation of body temperature and heart rate in domestic sheep. Vet. Anim. Sci. 8: 100075.
Gessaman J.A. and Nagy K.A. 1988. Energy metabolism: errors in gas exchange conversion factors. Physiol Zool. 61: 507–513.
Gittleman J.L. and Harvey P.H. 1982. Carnivore home-range size, metabolic needs and ecology. Behav. Ecol. Sociobiol. 10(1): 57–63.
Gleiss A.C., Wilson R.P., and Shepard E.L. 2011. Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure. Methods Ecol. Evol. 2(1): 23–33.
Gorman M.L., Mills M.G., Raath J.P., and Speakman J.R. 1998. High hunting costs make African wild dogs vulnerable to kleptoparasitism by hyaenas. Nature, 391(6666): 479–481.
Gotaas G., Milne E., Haggarty P., and Tyler N.J. 1997. Use of feces to estimate isotopic abundance in doubly labeled water studies in reindeer in summer and winter. Am. J. Physiol. 273(4): R1451–R1456.
Green J.A. 2011. The heart rate method for estimating metabolic rate: review and recommendations. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 158(3): 287–304.
Green J.A., Halsey L.G., Wilson R.P., and Frappell P.B. 2009. Estimating energy expenditure of animals using the accelerometry technique: activity, inactivity and comparison with the heart-rate technique. J. Exp. Biol. 212(4): 471–482.
Griffiths D. 1980. Foraging costs and relative prey size. Am. Nat. 116(5): 743–752.
Häggström J., Andersson Å.O., Falk T., Nilsfors L., OIsson U., Kresken J.G., et al. 2016. Effect of body weight on echocardiographic measurements in 19,866 pure‐bred cats with or without heart disease. J. Vet. Intern. Med. 30(5): 1601–1611.
Halsey L.G., Shepard E.L.C., Quintana F., Laich A.G., Green J.A., and Wilson R.P. 2009. The relationship between oxygen consumption and body acceleration in a range of species. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 152(2): 197–202.
Hammel H.T. 1955. Thermal properties of fur. Am. J. Physiol. Legacy Content, 182(2): 369–376.
Hezzell M.J., Humm K., Dennis S.G., Agee L., and Boswood A. 2013. Relationships between heart rate and age, bodyweight and breed in 10,849 dogs. J. Small Anim. Pract. 54(6): 318–324.
Hubel T.Y., Myatt J.P., Jordan N.R., Dewhirst O.P., McNutt J.W., and Wilson A.M. 2016. Energy cost and return for hunting in African wild dogs and cheetahs. Nat. Commun. 7: 11034.
Irving L., Krog H., and Monson M. 1955. The metabolism of some Alaskan animals in winter and summer. Physiol. Zool. 28(3): 173–185.
Kleiber M. 1932. Body size and metabolism. Hilgardia, 6(11): 315–353.
Kolbe J.A. and Squires J.R. 2007. Circadian activity patterns of Canada lynx in western Montana. J. Wildl. Manage. 71: 1607–1611.
Kolbe J.A., Squires J.R., and Parker T.W. 2003. An effective box trap for capturing lynx. Wildl. Soc. Bull. 31(4): 980–985.
Konarzewski M. and Książek A. 2013. Determinants of intra-specific variation in basal metabolic rate. J. Comp. Physiol. B, 183(1): 27–41.
Krebs C.J., Boonstra R., and Boutin S. 2018. Using experimentation to understand the 10‐year snowshoe hare cycle in the boreal forest of North America. J. Anim. Ecol. 87(1): 87–100.
Krebs, C.J., Jung, T., O’Donoghue, M., Kukka, P., Gilbert, S., Taylor, S., et al. 2019. The community ecological monitoring program annual data report. Available from https://www.zoology.ubc.ca/∼krebs/kluane.html.
Kuznetsova, A., Brockhoff, P.B., and Christensen, R.H.B. 2015. lmerTest: tests in linear mixed effects models. R package version 2.0. Available from https://CRAN.R-project.org/package=lmerTest.
Larroque J., Ruette S., Vandel J.M., and Devillard S. 2015. Where to sleep in a rural landscape? A comparative study of resting sites pattern in two syntopic Martes species. Ecography, 38(11): 1129–1140.
Mautz W.W. and Pekins P.J. 1989. Metabolic rate of bobcats as influenced by seasonal temperatures. J. Wildl. Manage. 53(1): 202–205.
Menzies A.K., Studd E.K., Majchrzak Y.N., Peers M.J., Boutin S., Dantzer B., et al. 2020. Body temperature, heart rate, and activity patterns of two boreal homeotherms in winter: homeostasis, allostasis, and ecological coexistence. Funct. Ecol. 34(11): 2292–2301.
Muggeo V.M. 2008. Segmented: an R package to fit regression models with broken-line relationships. R News, 8(1): 20–25.
Muñoz-Garcia A. and Williams J.B. 2005. Basal metabolic rate in carnivores is associated with diet after controlling for phylogeny. Physiol. Biochem. Zool. 78(6): 1039–1056.
Murray D.L. and Boutin S. 1991. The influence of snow on lynx and coyote movements: does morphology affect behavior? Oecologia, 88(4): 463–469.
Murray D.L., Boutin S., O’Donoghue M., and Nams V.O. 1995. Hunting behaviour of a sympatric felid and canid in relation to vegetative cover. Anim. Behav. 50(5): 1203–1210.
Newbury R.K. and Hodges K.E. 2019. A winter energetics model for bobcats in a deep snow environment. J. Ther. Biol. 80: 56–63.
O’Donoghue M., Boutin S., Krebs C.J., and Hofer E.J. 1997. Numerical responses of coyotes and lynx to the snowshoe hare cycle. Oikos, 80(1): 150–162.
O’Donoghue M., Boutin S., Krebs C.J., Murray D.L., and Hofer E.J. 1998a. Behavioural responses of coyotes and lynx to the snowshoe hare cycle. Oikos, 82(1): 169–183.
O’Donoghue M., Boutin S., Krebs C.J., Zuleta G., Murray D.L., and Hofer E.J. 1998b. Functional responses of coyotes and lynx to the snowshoe hare cycle. Ecology, 79(4): 1193–1208.
Pagano A.M., Durner G.M., Rode K.D., Atwood T.C., Atkinson S.N., Peacock E., et al. 2018. High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear. Science, 359(6375): 568–572.
Parker K.L., Robbins C.T., and Hanley T.A. 1984. Energy expenditures for locomotion by mule deer and elk. J. Wildl. Manage. 48: 474–488.
Peers M.J., Majchrzak Y.N., Menzies A.K., Studd E.K., Bastille-Rousseau G., Boonstra R., et al. 2020. Climate change increases predation risk for a keystone species of the boreal forest. Nat. Clim. Change, 10(12): 1149–1153.
Peters, R.H. 1986. The ecological implications of body size. Vol. 2. Cambridge University Press, Cambridge, U.K.
Podolski I., Belotti E., Bufka L., Reulen H., and Heurich M. 2013. Seasonal and daily activity patterns of free-living Eurasian lynx Lynx lynx in relation to availability of kills. Wildl. Biol. 19(1): 69–77.
Ray, J.C. 2000. Mesocarnivores of northeastern North America: status and conservation issues. Wildlife Conservation Society, Bronx, N.Y.
Rizzuto M., Carbone C., and Pawar S. 2018. Foraging constraints reverse the scaling of activity time in carnivores. Nat. Ecol. Evol. 2(2): 247–253.
Roemer G.W., Gompper M.E., and Van Valkenburgh B. 2009. The ecological role of the mammalian mesocarnivore. BioScience, 59(2): 165–173.
Rohatgi, A. 2019. WebPlotDigitizer. Version 4.2. Available from https://automeris.io/WebPlotDigitizer [accessed May 2020].
Scantlebury D.M., Mills M.G., Wilson R.P., Wilson J.W., Mills M.E., Durant S.M., et al. 2014. Flexible energetics of cheetah hunting strategies provide resistance against kleptoparasitism. Science, 346(6205): 79–81.
Schmidt-Nielsen, K. 1984. Scaling: Why is animal size so important? Cambridge University Press, Cambridge, U.K.
Speakman, J. 1997. Doubly labelled water: theory and practice. Springer Science & Business Media.
Studd E.K., Derbyshire R.E., Menzies A.K., Simms J.F., Humphries M.M., Murray D.L., and Boutin S. 2021. The Purr‐fect Catch: Using accelerometers and audio recorders to document kill rates and hunting behaviour of a small prey specialist. Methods Ecol. Evol. 12(7): 1277–1287.
Thompson, R. 1977. Snares and snaring. R. Thompson Co., Lynwood, Wash. 59 pp.
Welcker J., Speakman J.R., Elliott K.H., Hatch S.A., and Kitaysky A.S. 2015. Resting and daily energy expenditures during reproduction are adjusted in opposite directions in free‐living birds. Funct. Ecol. 29(2): 250–258.
White C.R. and Kearney M.R. 2013. Determinants of inter-specific variation in basal metabolic rate. J. Comp. Physiol. B, 183(1): 1–26.
White C.R., Blackburn T.M., Martin G.R., and Butler P.J. 2007. Basal metabolic rate of birds is associated with habitat temperature and precipitation, not primary productivity. Proc. R Soc. B Biol. Sci. 274(1607): 287–293.
White C.R., Marshall D.J., Alton L.A., Arnold P.A., Beaman J.E., Bywater C.L., et al. 2019. The origin and maintenance of metabolic allometry in animals. Nat. Ecol. Evol. 3(4): 598–603.
Williams T.M., Wolfe L., Davis T., Kendall T., Richter B., Wang Y., et al. 2014. Instantaneous energetics of puma kills reveal advantage of felid sneak attacks. Science, 346(6205): 81–85.
Williams T.M., Bengtson P., Steller D.L., Croll D.A., and Davis R.W. 2015. The healthy heart: lessons from nature's elite athletes. Physiology, 30(5): 349–357.
Wilmers C.C., Isbell L.A., Suraci J.P., and Williams T.M. 2017. Energetics‐informed behavioral states reveal the drive to kill in African leopards. Ecosphere, 8(6): e01850.
Wilson R.P., White C.R., Quintana F., Halsey L.G., Liebsch N., Martin G.R., and Butler P.J. 2006. Moving towards acceleration for estimates of activity‐specific metabolic rate in free‐living animals: the case of the cormorant. J. Anim. Ecol. 75(5): 1081–1090.
Winstanley R.K., Buttemer W.A., and Saunders G. 2003. Field metabolic rate and body water turnover of the red fox Vulpes vulpes in Australia. Mammal. Rev. 33(3–4): 295–301.
Young J.K., Hudgens B., and Garcelon D.K. 2012. Estimates of energy and prey requirements of wolverines. Northwest Sci. 86(3): 221–229.

Supplementary Material

Supplementary data (cjz-2021-0142suppla.docx)

Information & Authors

Information

Published In

cover image Canadian Journal of Zoology
Canadian Journal of Zoology
Volume 100Number 4April 2022
Pages: 261 - 272

History

Received: 26 July 2021
Accepted: 12 December 2021
Accepted manuscript online: 20 January 2022
Version of record online: 20 January 2022

Permissions

Request permissions for this article.

Key Words

  1. activity
  2. allometry
  3. biologger
  4. boreal ecology
  5. energy expenditure
  6. heart rate
  7. Canada lynx
  8. Lynx canadensis
  9. mesocarnivore
  10. winter

Mots-clés

  1. activité
  2. allométrie
  3. capteur de paramètres biologiques
  4. écologie boréale
  5. dépense énergétique
  6. rythme cardiaque
  7. lynx du Canada
  8. Lynx canadensis
  9. mésocarnivore
  10. hiver

Authors

Affiliations

Allyson K. Menzies [email protected]
Department of Natural Resource Sciences, Macdonald Campus, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada.
Emily K. Studd
Department of Natural Resource Sciences, Macdonald Campus, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada.
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Jacob L. Seguin
Environmental and Life Sciences Graduate Program, Trent University, Peterborough, ON K9J 7B8, Canada.
Wildlife Conservation Society Canada, Ontario Northern Boreal Program, Thunder Bay, ON P7A 4K9, Canada.
Rachael E. Derbyshire
Environmental and Life Sciences Graduate Program, Trent University, Peterborough, ON K9J 7B8, Canada.
Dennis L. Murray
Department of Biology, Trent University, Peterborough, ON K9J 7B8, Canada.
Stan Boutin
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Murray M. Humphries
Department of Natural Resource Sciences, Macdonald Campus, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada.

Funding Information

:
Graduate student scholarships and research funding were provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Weston Foundation, Wildlife Conservation Society Canada (WCS Canada), and Polar Knowledge Canada Northern Scientific Training Program (NSTP).

Metrics & Citations

Metrics

Other Metrics

Citations

Cite As

Export Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

1. Behavioural adjustments of predators and prey to wind speed in the boreal forest

View Options

View options

PDF

View PDF

Get Access

Login options

Check if you access through your login credentials or your institution to get full access on this article.

Subscribe

Click on the button below to subscribe to Canadian Journal of Zoology

Purchase options

Purchase this article to get full access to it.

Restore your content access

Enter your email address to restore your content access:

Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

Media

Media

Other

Tables

Share Options

Share

Share the article link

Share on social media

Cookies Notification

We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy.
×