Towards an energetic landscape: broad-scale accelerometry in woodland caribou

Towards an energetic landscape: broad-scale

accelerometry in woodland caribou

Anna A. Mosser1,2*, Tal Avgar1, Glen S. Brown3, C. Spencer Walker1 and John M. Fryxell1

1Department of Integrative Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada;2University of Minnesota, Biology Program, 123 Snyder Hall, 1475 Gortner Ave., St. Paul, MN 55108, USA; and3Ontario Terrestrial Assessment Program, Ontario Ministry of Natural Resources, 1235 Queen Street East, Sault Ste.

Marie, ON, P6A 2E5, Canada

Summary

1. Energetic balance is a central driver of individual survival and population change, yet esti-

mating energetic costs in free- and wide-ranging animals presents a significant challenge. Ani-

mal-borne activity monitors (using accelerometer technology) present a promising method of

meeting this challenge and open new avenues for exploring energetics in natural settings.

2. To determine the behaviours and estimated energetic costs associated with a given activity

level, three captive reindeer (Rangifer tarandus tarandus) at the Toronto Zoo were fitted with

collars and observed for 53 h. Activity patterns were then measured over 13 months for 131

free-ranging woodland caribou (R. t. caribou) spanning 450 000 km2 in northern Ontario.

The captive study revealed a positive but decelerating relationship between activity level and

energetic costs inferred from previous behavioural studies.

3. Field-based measures of activity were modelled against individual displacement, vegetation

abundance (Normalized Difference Vegetation Index), snow depth and temperature, and the

best fit model included all parameters and explained over half of the variation in the data.

Individual displacement was positively related to activity levels, suggesting that broad differ-

ences in energetic demands are influenced by variation in movement rates. After accounting

for displacement, activity was highest at intermediate levels of vegetation abundance, presum-

ably due to foraging behaviour. Snow depth, probably associated with digging for winter for-

age, moderately increased activity. Activity levels increased significantly at the coldest winter

temperatures, suggesting the use of behavioural thermoregulation by caribou. These interpre-

tations of proximate causal factors should be regarded as hypotheses subject to validation

under normal field conditions.

4. These results illustrate the landscape characteristics that increase energetic demands for cari-

bou and confirm the great potential for the use of accelerometry in studies of animal energetics.

Key-words: accelerometry, biotelemetry, energetics, landscape, Rangifer

Introduction

An organism’s balance between energy acquisition and

expenditure affects its survival and reproduction (Brown

et al. 2004); thus, ecologists are interested in quantifying

patterns of energetic gains and losses. Recent work sug-

gests animal movement trajectories can be strongly influ-

enced by energetic tradeoffs (Shepard et al. 2013).

Energetic constraints are expected to determine the extent

of a species’ range (Anderson & Jetz 2005; Buckley 2008),

and energy deficits are likely associated with population

decline. Therefore, examining variation in energy expendi-

ture across environmental gradients, evocatively termed

the energetic landscape (Wilson, Quintana & Hobson

2011; Shepard et al. 2013), is an important component of

understanding and predicting patterns of space use, range

shifts or retractions as well as population dynamics.

A relatively new, yet promising, method of estimating

energetic costs is body accelerometry (Wilson et al. 2006;

Halsey et al. 2009a; Gleiss, Wilson & Shepard 2011;

Shepard et al. 2013). Accelerometers, typically integrated

into tracking devices attached to an animal, measure

body movements along three axes and approximate the

animal’s locomotion, a primary source of energy expendi-

ture in animals (Alexander 2003). While laboratory*Correspondence author. E-mail: [email protected]

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society

Journal of Animal Ecology 2014, 83, 916–922 doi: 10.1111/1365-2656.12187

research confirms the utility of this method, practical use

in field studies remains in development (e.g. Wilson,

Quintana & Hobson 2011; Nathan et al. 2012). In labo-

ratory experiments for a range of species, these accelera-

tion patterns have been calibrated against the rate of

oxygen consumption, a commonly used measure of meta-

bolic rate and energy expenditure (Elliott & Davison

1975). These calibrations have reliably yielded positive

linear relationships between body acceleration and oxy-

gen consumption (Halsey et al. 2009b). Accelerometry

data, however, when measured at a subsecond frequency,

present complications for use in field studies due to limi-

tations of on-board data storage and the amount of

information that can be transmitted via satellite. Manu-

facturers of wildlife tracking collars have therefore imple-

mented the use of activity sensors derived from

integrated accelerometry information (Body, Weladji &

Holand 2012). Here, we evaluate the potential for the use

of such activity sensors based on accelerometer scores as

a proxy for metabolic costs applicable to free-ranging

animals in remote settings.

To explore the use of this type of accelerometer tech-

nology, we paired a field-based study of woodland cari-

bou (Rangifer tarandus caribou) with a short captive

study of European reindeer (Rangifer tarandus tarandus).

Prior to field deployment of accelerometer-equipped col-

lars, we conducted an observational study of three cap-

tive reindeer fitted with the same collars, to verify the

relationships among the simple accelerometer statistic,

behaviour and activity level, as well as estimated energy

expenditure. One hundred and sixty woodland caribou in

northern Ontario were then fitted with collars for a mul-

tiyear study of caribou ranging ecology. The collar accel-

erometer measurements are reported as total active

seconds within a 5- or 20-min time interval (see Materi-

als and methods), allowing for easy transmission via

satellite and deployment of collars over multiple years.

Here, we report on one full year of movement and ac-

celerometry data for 131 female caribou ranging across

450 000 km2, a large fraction of the province of Ontario,

Canada.

This study is among the first to report such data for a

large number of free-ranging individuals inhabiting a

large and varied area and to consider the environmental

drivers of variation in energetic costs at a landscape scale

(Wilson, Quintana & Hobson 2011). Our central aim,

after verifying the utility of a simple activity measurement

in a captive study, is to characterize the broad-scale pat-

terns of variation in individual activity levels and to iden-

tify the environmental parameters underlying these

patterns. Identifying the factors that increase activity, and

thus energetic costs, will be a key component in deter-

mining whether energetic constraints are an important

cause of decline in Ontario caribou populations (Schaefer

2003; Callaghan, Virc & Duffe 2011; Festa-Bianchet et al.

2011).

Materials and methods

captive study

Three individuals from a herd of seven female captive European

reindeer at Metropolitan Toronto Zoo (ON, Canada) were fitted

with collars and observed during a 12-day period from 15

December 2009 to 4 January 2010. Use of collars was preap-

proved by a zoo review committee, and animal handling was car-

ried out by zoo staff. All observations took place roughly

between 8:30 am and 4:30 pm. Although this subspecies is

slightly smaller than that subsequently studied in the wild,

behavioural patterns are otherwise known to be similar. The rein-

deer were 2, 13, 15 years of age; two of the three were substan-

tially older than typically found in the wild. The observed

reindeer had access to ground level grass and hay as well as pel-

lets provided in a trough. Cratering to acquire food was unim-

portant for the zoo animals, although animals did dig through

shallow snow occasionally to forage on grasses. As a result, for-

aging costs due to cratering are not included here.

The collars (Gen4 model number TGW-4680; Telonics Inc.,

Mesa, AZ, USA) use uGPSi-20 accelerometers to detect move-

ment. Accelerometer measurements were reported as ‘active sec-

onds’, where an active second was recorded if the change in

acceleration from 1 s to the next in any one of three orthogonal

dimensions exceeded a threshold as set by the manufacturer

(about 0�3 g, or 2�87 m s�2). The sensor is also sensitive to rota-

tion, as this movement also causes a change in the measured

acceleration, and rotation angles of 17–45° thereby trigger an

‘active second’. This activity sensor falls somewhere between full

dynamic acceleration and static acceleration. The virtue of such a

measure is that it allows data to be collected and stored over

appreciable periods, which could in principle enhance field

applications in remote settings. For this captive study of short

duration, a continuous stream of second-by-second data was

recorded and stored on the device and then later downloaded

to a computer.

Observations began in the morning before the reindeer were

fed and continued during and after feeding until either the rein-

deer was recumbent or until the zoo closed for the evening. The

following behavioural categories were recorded: lying down,

standing, walking, grazing, rubbing antlers, trotting, social inter-

action and trough feeding. Data were logged with each change in

behavioural category, and the total duration of each behavioural

bout was recorded (to the second) along with a time stamp.

Observations focussed on a single individual at a time and obser-

vation periods averaged approximately 20 min. We accumulated

21, 13 and 19 h of observation for the 15-, 13- and 2-year-old

reindeer, respectively.

Accelerometer data were matched, second-by-second, with the

behavioural observations. Energetic costs associated with each

behaviour category (Boertje 1985a; Fancy & White 1986) were

used to assign an energetic cost to each observed second. These

data were then summarized over 5- and 20-min intervals,

randomly selected with no overlap from the larger data set. This

allowed comparison with the 5- and 20-min recording periods of

the field-based study. Inverse negative exponential curves

were then fitted to the data to describe the relationship between

activity level and the estimated energetic expenditure during

that interval.

© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, Journal of Animal Ecology, 83, 916–922

Towards an energetic landscape 917

free-ranging study

During February to April of 2009 and 2010, 160 female wood-

land caribou across northern Ontario were net-gunned from a

helicopter and fitted with GPS telemetry collars of the type

described above. Capturing and handling were performed

according to Ontario Ministry of Natural Resources animal care

regulations. Telemetry collars were distributed in an effort to pro-

vide good geographic representation across the range of promi-

nent vegetation communities inhabited by caribou in Ontario,

including upland conifer forests in the west and lowland commu-

nities dominated by fens and bogs in the northern and eastern

portions of range adjacent to Hudson Bay and James Bay. The

study area, determined by the ranging patterns of all individuals,

is roughly 450 000 km2 (Fig. 2).

Each collar recorded GPS coordinates and activity counts

every 5 h for 13 months and transmitted the data via an Argos

uplink every 5 days. Four activity intervals were recorded with

each location fix, two before and two after the fix, and the total

number of active seconds was reported for each. Activity inter-

vals were 5 or 20 min, differing by collar, and thus, the maxi-

mum possible activity counts were 300 or 1200 active seconds.

We accordingly normalized the data by dividing by the maximum

possible activity level, allowing us to merge data from both collar

types. Activity counts differed depending on the interval length,

and this is treated appropriately in the statistical analyses (see

below). A subset of data including only consecutive 5-h steps was

used, and total displacement (in metres) was calculated for each

step. Data from the first 24 h following collaring and the last

24 h before any mortality were excluded from the data set, as

were any points that were likely due to device error (those indi-

cating an improbable velocity >200 km per 5 h and a mean activ-

ity count of 0) or fixes that were not associated with at least

three activity intervals. This resulted in a final data set based on

103 000 fixes of 155 000 possible.

Each GPS location was coupled with three environmental vari-

ables that are expected to be associated with foraging and ther-

moregulatory behaviour. We expected foraging behaviour to be

associated with vegetation abundance and snow depth. Caribou

often feed on lichen and ground level plants in the winter and

must dig through the snow to reach them, a behaviour known as

cratering. The amount of available forage was estimated using

maps of Normalized Difference Vegetation Index (NDVI) at a

250-m resolution and 16-day intervals (Land Processes Distrib-

uted Active Archive Center, U.S. Geological Survey – Earth

Resources Observation and Science Center, lpdaac.usgs.gov).

NDVI values range from �2000 to 10 000, with higher values

indicating more vegetation. A mean NDVI value was calculated

from the area within a 250-m radius of each point, half the med-

ian 5-h displacement. Thermoregulatory behaviour was expected

to be associated with temperature. Maps of snow depth (metres)

and temperature (°C), based on real-time weather data and mete-

orological modelling, were obtained at a 40-km resolution for

each 3-h period (North America Regional Reanalysis data set

DSI-6175, NOAA Operational Model Archive and Distribution

System, nomads.ncdc.noaa.gov). Each GPS point was associated

with the nearest location and time for these two meteorological

variables.

Location, activity count, displacement and environmental mea-

sures were averaged for each individual and month. Means were

only included if the individual had at least 10 days of activity data

within the month (leaving 131 individuals and 1231 individual

months, and between 1 and 13 months of data per individual).

Mean monthly activity counts (transformed to percentage of

possible maximum) were modelled against displacement, NDVI,

snow depth and temperature, using a generalized least squares

model (gls function, version 2.13.1, The R Foundation for Statis-

tical Computing, Vienna, Austria), which included individual and

x–y coordinates to account for nonindependence and spatial

autocorrelation structure in the residuals. All models also

included interval type (5 or 20 min) as a parameter, as the mean

activity levels differed significantly, with a slightly higher mean

for the 5-min activity intervals. Statistical models considered

included a null model (intercept and interval type only), all linear

and second-order combinations, and two-way interactions

between environmental variables. For simplicity and ease of inter-

pretation, second-order terms and two-way interactions were not

considered within the same model. Candidate models were com-

pared using Akaike’s Information Criteria (AIC) with the best

model having the highest model weight (ranging from 0 to 1)

(Burnham & Anderson 2002). True R2 values cannot be calcu-

lated for these models, yet an estimate for the variance explained

by the best model can be calculated by comparing the residual

variance of the null model to that of the best model (Zheng

2000). These values are reported where appropriate.

Results

captive study

Activity measures for the captive reindeer ranged from

0% to 46% of seconds active within an interval, with a

mean and standard deviation of 7�6 � 8�0%. Of all obser-

vations, 43% of time was spent lying down, 27% grazing,

15% standing, 8% feeding at trough, 5% walking, 0�4%rubbing antlers, 0�4% in social interactions and 0�1%trotting. Direct observations confirmed that intervals with

low activity scores were associated with lower cost behav-

iours such as lying down, while intervals with higher

activity scores were associated with higher cost behaviours

such as grazing and walking (Fig. 1a). Energy expendi-

ture, as estimated by the energetic costs of the behaviours

observed within each interval, was significantly correlated

with the activity measures (Fig. 1b). Nonlinear models fit

to the data explained 64% of the variance for 5-min inter-

vals and 67% for 20-min intervals, and all model coeffi-

cients were significant with P ≤ 0�0001.

free-ranging study

For the study of free-ranging woodland caribou, measures

of activity ranged from 2�9% to 94�8% of maximum pos-

sible activity level, with a mean and standard deviation of

17�4 � 14�9%. That is, on average, individuals were active

for 17�4% of the seconds within a 5- or 20-min activity

interval. The mean for 5-min intervals was 18�0 � 16�5%,

whereas the mean for 20-min intervals was 17�1�13�8%.

Mean annual activity levels ranged from 14% to 21%

and varied across the study range (Fig. 2), with a mean


918 A. A. Mosser et al.

across all individuals of 17�6 � 3�0%. Mean monthly

activity levels ranged from 15�4% to 22�0%, with activity

levels highest in December and January and variation

greatest in the summer months. The best model for

monthly mean activity (Table 1) included displacement,

NDVI, snow depth and temperature, with a model weight

of 0�49 and an estimated 51% of the variance explained

(Table 2). This model included positive linear relation-

ships with displacement and snow depth and second-order

terms for NDVI and temperature (see Fig. 3, for the

shapes of these curves). A model with x and y coordinates

did not provide a more plausible model, indicating that

displacement and local environmental variables captured

a large fraction of observed spatial variation.

All top-ranked models include displacement and a

displacement-only model explained 37% of the data

variance. A plot of mean monthly activity versus displace-

ment (Fig. 3a) illustrates the strong association between

these variables. The scatterplot of monthly activity versus

displacement (Fig. 3a) suggests there may be a lower

bound to the data distribution, characteristic of so-called

polygonal relationships (Scharf, Juanes & Sutherland

1998). Plotting the residuals after accounting for displace-

ment (Fig. 3b,c) illustrates the remaining variance in

activity patterns that are associated with the environmental

variables.

Discussion

The captive study suggested that activity scores derived

from accelerometry were associated with differences in

reindeer behaviour that have been linked in earlier labora-

tory studies with predictable variation in energy expendi-

ture. There was, however, appreciable variance left

0%

20%

40%

60%

80%

100%

0 0·1 0·2 0·3

Per

cent

of t

ime

in b

ehav

iour

8

9

10

11

12

13

0 0·1 0·2 0·3 0·4 0·5

Ene

rget

ic c

ost (

kJ k

g–1 h

–1)

Proportion of interval active

5 min intervals5 min fitted curve20 min intervals20 min fitted curve

Behaviour Energetic cost (kJ kg–1 h–1)

Trotting 30·00 1

Walking 13·42 2

Rubbing 10·76 3

Grazing 10·76 2

Social interaction 10·76 3

Trough feeding 10·30 2

Standing 9·19 2

Lying down 8·37 2

4

5

(a)

(b)

Fig. 1. Results of captive reindeer study. (a) Observed behav-

iours for different levels of activity as measured over 5-min inter-

vals. The final category includes activity levels of ≥0�34. The

energetic costs associated with each behaviour, from published

research, are listed on the right. (b) The aggregate estimated cost

of captive reindeer behaviours over 5- or 20-min intervals in rela-

tion to the activity score. 1Boertje (1985a). 2Fancy & White

(1986). 3Assumed to be similar to ‘grazing’. 4Energetic

cost = 8�41 + 2�54(1�e�14�79(activity)). 5Energetic cost = 8�18 + 3�01(1�e�11�74 (activity)).

Fig. 2. An illustration of broad spatial variation of activity levels

in wild caribou. Monthly mean activity values were averaged for

each year and individual, and those with fewer than 10 months

of data were excluded (leaving 81 individuals represented here).

Mean annual spatial locations are indicated by white dots. Values

are the percentage of the possible maximum activity level. Inset

indicates region of study area.

Table 1. Top ten models (ordered by strength of model fit) for

analysis of mean monthly activity, including degrees of freedom

(d.f.), DAIC relative to the best fit model and model weight (x).Explanatory variables are displacement (disp), vegetation abun-

dance (NDVI), snow depth (snow) and temperature (temp).

When second-order terms were included in the model, the univar-

iate terms were necessarily included as well. The best explanatory

model, with a model weight of 0�49, explained an estimated 51%

of the variance

Model d.f. DAIC x

disp NDVI2 snow temp2 10 0�00 0�49disp NDVI2 temp2 9 0�83 0�33disp NDVI2 snow2 temp2 11 1�99 0�18disp temp2 7 24�41 0�00disp snow temp2 8 25�86 0�00disp NDVI temp2 8 26�40 0�00disp snow temp snow:temp 8 26�87 0�00disp temp snow:temp 7 27�10 0�00disp snow2 temp2 9 27�14 0�00disp NDVI snow temp2 9 27�76 0�00

AIC, Akaike’s Information Criteria; NDVI, Normalized Differ-

ence Vegetation Index.



unexplained, larger than that reported for other published

correlates of metabolic rate, such as heart rate (Nilssen

et al. 1984; Fancy & White 1986) or O2 consumption

(Halsey et al. 2009b). Additional research on meaningful

summary statistics for accelerometer data would be of

great value. The use of ‘overall dynamic body accelera-

tion’ (ODBA), for example, has proven to be highly

accurate in laboratory studies (Halsey, Shepard & Wilson

2011). It would instructive in a future study to measure

both the derived activity metric used in the current study

and ODBA.

For the study of free-ranging caribou, displacement was

associated with activity level. This result is not unex-

pected. Locomotion is a primary component of activity in

a vagile animal (Boertje 1985a; Duquette & Klein 1987),

is associated with elevated heart rates and likely metabo-

lism in reindeer (Nilssen et al. 1984) and is a behaviour

that is accurately captured by accelerometry (Gleiss,

Wilson & Shepard 2011). After accounting for displace-

ment, our best explanatory model suggested a parabolic

relationship with vegetation abundance, where foraging

activity is highest at intermediate levels of NDVI.

Separate analyses for colder and warmer months also sug-

gested that this reflects a seasonal difference in foraging

patterns. At lower measures of vegetation abundance seen

during the winter, we found the expected positive correla-

tion and caribou are more active where there is more veg-

etation, presumably due to foraging behaviour. At higher

levels of vegetation abundance seen during the summer

months, we found the opposite pattern. One plausible

explanation is that caribou living in these areas reach

asymptotic levels of food intake when vegetation is plenti-

ful and therefore actually spend less time foraging

(Boertje 1985b). NDVI is significantly and positively cor-

related with percentage conifer tree cover, as estimated

from a LANDSAT satellite land cover classification

(Spectranalysis Inc. 2004), in both warm and cool months.

Conifer cover is a preferred habitat for caribou and

source of forage, particularly for lichens in winter months

(Brown, Rettie & Mallory 2006; Brown et al. 2007).

Our best model also included a positive linear relation-

ship between activity and snow depth, suggesting that

activity levels are higher in deeper snow, presumably due

to cratering behaviour. Note that higher snow depths

depress movement across the landscape (Avgar et al.

2013) and yet increase overall activity levels. As this

Table 2. Best model fit for mean monthly activity versus candi-

date model including second-order terms

Variable Value Std.error t-value P-value

Interval type

(20 vs. 5 min)

�0�5954 0�2065 �2�88 0�004

Displacement 0�0028 0�0001 23�80 <0�001Normalized Difference

Vegetation Index

(NDVI)

0�0010 0�0002 5�18 <0�001

NDVI2 �1�422�7 0�0000 �5�47 <0�001Snow depth 2�6989 1�6071 1�68 0�093Temperature �0�0930 0�0185 �5�02 <0�001Temperature2 0�0051 0�0009 5�52 <0�001

0%

5%

10%

15%

20%

25%

30%

35%

Mea

n ac

tivity

leve

l (%

of m

axim

um)

Mean 5-h displacement

–10%

–5%

0%

5%

10%

15%

Act

ivity

leve

l (re

sidu

al)

NDVI

–10%

–5%

0%

5%

10%

15%A

ctiv

ity le

vel (

resi

dual

)

Snowdepth (m)

–10%

–5%

0%

5%

10%

15%

0 1000 2000 3000 4000 5000

–1500 0 1500 3000 4500 6000 7500

0 0·2 0·4 0·6

–30 –15 0 15 30

Act

ivity

leve

l (re

sidu

al)

Temperature (°°C)

(a)

(b)

(c)

(d)

Fig. 3. (a) Mean monthly activity of wild caribou versus mean

monthly 5-h displacement in metres (n = 1231). Residual mean

monthly activity (after accounting for displacement and interval

type) versus mean monthly (b) Normalized Difference Vegetation

Index value, (c) snow depth and (d) temperature (n = 1231). Grey

lines (b and d) show the polynomial relationship as estimated by

the best fit model. Dark shading corresponds to summer months

(May–October), whereas light shading corresponds to winter

months (November–April).



behaviour is potentially energetically costly (Fancy &

White 1985), especially during the lean winter months,

this aspect warrants further analysis at a finer scale.

The best model for monthly mean activity levels sug-

gested an inverted polynomial relationship with tempera-

ture. Considerably higher activity levels were only found

at the lowest temperatures. The lower critical temperature

for reindeer is estimated to be about �30 °C (Tyler &

Blix 1990), yet we see rises in activity at mean monthly

temperatures much above this threshold. The tempera-

tures actually experienced by the caribou during the cold-

est months were obviously colder than the mean and

factors such as wind contribute considerably to heat loss

as well. Caribou are well adapted to cold temperatures

and are known to rely upon shivering and metabolic

(or nonshivering) thermogenesis (Soppela, Nieminen &

Timisj�arvi 1986), yet this pattern suggests that they may

also use behavioural thermal substitution. Little is known,

however, about activity as a form of thermoregulation in

large mammals (McNab 2002; Humphries & Careau

2011), and we cannot say what form of behaviour this

thermoregulatory activity may take. Thermoregulatory

constraints are an important determinant of activity pat-

terns (e.g. Armstrong & Robertson 2000) and range limits

(e.g. Porter et al. 2002) for many species.

The modelled relationships summarize the primary

components of energetic costs for woodland caribou.

Landscapes that increase movement rates or displacement

distances, of low to intermediate vegetation abundance,

with greater snow depth, and lower temperatures are

expected to increase energetic costs. These parameters

explain over half the variation in the monthly data. The

results of this study suggest that activity sensors based on

accelerometry may be useful for field estimation of activ-

ity patterns and energetic costs in free-ranging animals.

Our results also demonstrated that activity levels varied

with both biological and environmental parameters. In

sum, this work supports the growing recognition that ac-

celerometry offers a new and important addition in

remote biotelemetry (Cooke et al. 2004), allowing ecolo-

gists to probe otherwise unobtainable patterns.

A number of practical limitations would still limit the

utility of accelerometry-based activity scores for precise

estimation of absolute energetic costs in the field. The

range of behaviours in captive animals, especially those in

zoos or holding pens, no doubt differs from that seen in

free-ranging caribou (Boertje 1985b; Fancy & White

1986). Our zoo animals exhibited much lower frequencies

of walking trotting and (particularly) running than typi-

cally found in free-living ungulates, as well as foraging

infrequently from ground level lichen, grasses or forbs.

Moreover, the field estimates of energetic costs we have

derived from the literature are possibly clouded by com-

plexities associated with spatial variation in topographic

relief, day-to-day variation in snow hardness, depth and

surface ablation, as well as variation among individuals

in their internal condition, motivational state and

accumulated energy deficits. Although we feel confident in

concluding from our study that the relative magnitude of

energetic costs in free-living caribou varies considerably

with temporal and spatial variation in environmental con-

ditions as well as mobility levels, limited knowledge about

caribou behavioural budgets as well as lack of a direct

calibration of metabolic costs still hampers our ability to

precisely predict absolute costs across the caribou ener-

getic landscape (Shepard et al. 2013).

Acknowledgements

This work was funded by grants from the Forest Ecosystem Science Coop-

erative Inc, Ontario Ministry of Natural Resources (OMNR) and Natural

Sciences and Engineering Research Council (NSERC) of Canada as well

as a Vanier Fellowship to T.A. We thank the staff of the OMNR, notably

Lyle Walton, for support in the field operations, the Toronto Zoo and

staff, and Luba Broitman for software development. We also thank Doug-

las Morris, Daniel Fortin and two anonymous reviewers for comments on

the manuscript. None of the authors has a conflict of interest.

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Received 25 February 2013; accepted 26 November 2013

Handling Editor: Murray Humphries



Towards an energetic landscape: broad-scale accelerometry in woodland caribou

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