Content uploaded by Gabriel E Machovsky-Capuska
All content in this area was uploaded by Gabriel E Machovsky-Capuska on Apr 09, 2018
Content may be subject to copyright.
INVITED PERSPECTIVES AND REVIEWS
Motive for Killing: What Drives Prey Choice in Wild Predators?
Gabriel E. Machovsky-Capuska*†‡, Sean C. P. Coogan*‡, Stephen J. Simpson*‡& David Raubenheimer*†‡
* The Charles Perkins Centre, The University of Sydney, Sydney, Australia
†Faculty of Veterinary Science, The University of Sydney, Sydney, Australia
‡School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia
Gabriel E. Machovsky-Capuska, The Charles
Perkins Centre, Faculty of Veterinary Science
and School of Life and Environmental
Sciences, The University of Sydney, Sydney,
NSW 2006, Australia.
Received: February 18, 2016
Initial acceptance: April 24, 2016
Final acceptance: June 15, 2016
Keywords: carnivores, nutrition, energy,
nutritional geometry, foraging, cameras
Carnivorous animals are assumed to consume prey to optimise energy
intake. Recently, however, studies using Nutritional Geometry (NG) have
demonstrated that speciﬁc blends of macronutrients (e.g. protein, fat and
in some cases carbohydrates), rather than energy per se, drive the food
selection and intake of some vertebrate and invertebrate predators in the
laboratory. A vital next step is to examine the role of nutrients in the for-
aging decisions of predators in the wild, but extending NG studies of carni-
vores from the laboratory to the ﬁeld presents several challenges.
Biologging technology offers a solution for collecting relevant data which
when combined with NG will yield new insights into wild predator nutri-
A critical point to resolve among ecologists is whether
animals that derive the preponderance of their energy
and nutrients from capturing and eating other ani-
mals or the tissues thereof (hereafter referred to as
‘carnivores’ or ‘predators’) are limited by the amount
of prey they can capture (prey quantity) or the nutri-
tional composition of prey (prey quality; Simpson &
Raubenheimer 2012). Herbivores and omnivores,
which feed on foods that are highly variable in com-
position, have long been predicted to select food com-
binations that provide a nutritionally balanced diet
(Westoby 1978). The foods of carnivores, by contrast,
have been considered relatively similar in nutrient
content and nutritionally balanced relative to preda-
tor requirements but difﬁcult to acquire, from which
it would follow that predators are limited by the
quantity rather than quality of food (Stephens &
Krebs 1986; Koojiman et al. 2004). This has led to the
assumption based on optimal foraging theory that
predators forage to maximise their intake of energy
(Charnov 1976; Stephens & Krebs 1986; Whelan &
Schmidt 2007), rather than feed selectively to
optimise the dietary balance of nutrients.
A growing body of work, however, suggests that the
goals of animal foraging are more complex than
energy maximisation. The development of a multidi-
mensional geometric framework for quantifying the
nutritional priorities of animals, Nutritional Geometry
(NG), has allowed researchers to demonstrate nutri-
ent-speciﬁc foraging in both laboratory and ﬁeld stud-
ies of herbivores and omnivores. These studies have
related the amount and balance of dietary macronu-
trients self-selected by omnivores and herbivores to
several facets of ﬁtness, including mass gain (Simpson
et al. 2004), longevity and healthspan (Solon-Biet
et al. 2014), sexual display, reproduction and fecun-
dity (Lee et al. 2008; Maklakov et al. 2008; Solon-
Biet et al. 2015), immunity (Le Couteur et al. 2014),
and risk of predation (Hawlena & Schmitz 2010),
among others (Simpson & Raubenheimer 2012).
Recent applications of NG have shown that in the
laboratory, both invertebrate and vertebrate preda-
tors, too, forage to optimise macronutrient balance
rather than maximise energy per se, and one study has
demonstrated strong links between ﬁtness (estimated
as egg production) and nutrient-speciﬁc foraging in
female predatory beetles (Anchomenus dorsalis; Jensen
et al. 2012). Vertebrate carnivores, including the
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH 1
domestic cat (Felis catus; Hewson-Hughes et al. 2011,
2013; Plantinga et al. 2011), domestic dog (Canis lupus
familiaris; Hewson-Hughes et al. 2012) and mink
(Neovison vison; Mayntz et al. 2009; Jensen et al.
2014) have all demonstrated the ability to self-select
non-random proportions of macronutrients from
complementary foods (sensu Simpson & Rauben-
Macronutrient Balancing in the Wild
An important question is whether nutrient balance
inﬂuences prey choice by predators beyond the labo-
ratory, in the wild (Kohl et al. 2015). Although this
has yet to be tested, indirect evidence suggests that it
is likely to be the case. Recent analyses show that, far
from being invariant, the nutrient content of prey
species can vary substantially (e.g. insects, Rauben-
heimer & Rothman 2013; small mammals, Eisert
2011; ungulates, Coogan et al. 2014; and ﬁsh and
squid, Lenky et al. 2012; Tait et al. 2014; Machovsky-
Capuska et al. 2016a; Fig. 1). Additionally, prey
quantity is often not limiting relative to carnivore
energy requirements (Jeschke 2007), suggesting that
wild predators may be able to selectively consume
animals or choose among body parts to optimise diet
Fig. 1: Example to illustrate the potential of combining biologging and
nutritional geometry to study nutrient selection in a predatory central-
place forager. (a) miniaturized video camera deployed on the top of the
four central feathers of the tail of a chick-rearing adult masked booby
(Sula dactylatra tasmani, reproduced with permission from Machovsky-
Capuska et al. 2016b); (b) aerial prey detection of ﬂyingﬁsh (Exocoetidae
spp., reproduced with permission from Machovsky-Capuska et al.
2016b); (c and d) Undigested individual prey samples collected from
regurgitations undergo chemical composition analyses in the labora-
tory; (e) laboratory measures of prey nutrient content are plotted using
amounts-based nutritional models (see Raubenheimer and Simpson
1993; Simpson and Raubenheimer 1993). The protein:lipid ratios of the
three prey species were extracted from the literature; from left to right
(yellowtail kingﬁsh (Seriola lalandi, Machovsky-Capuska et al. 2016b);
arrow squid (Nototodarus spp., Machovsky-Capuska et al. 2016a) and
ﬂyingﬁsh (Cheilopogon sp., Machovsky-Capuska et al. 2016b). If the
food composition corresponded with the composition of the red target
("intake target", e.g. the squid) then it would be macronutrient-
balanced. The predator could, however, also obtain a balanced diet by
mixing its intake from the two ﬁsh species (black arrows), even though
neither is on its own nutritionally balanced. (f) Monitoring carnivores in
the wild over multiple days will allow researchers to establish their regu-
latory responses to constrained variation in the compositions of avail-
able foods. Daily macronutrient intakes (obtained as the sum of the
macronutrient compositions of prey consumed per day) would align
along the diagonal, vertical or horizontal arrays if energy, protein or
lipid intake, respectively, were prioritised in the face of dietary con-
straint that prevented them from achieving their intake target.
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH2
Motive for Killing G. E. Machovsky-Capuska et al.
quality (Kohl et al. 2015). Furthermore, reproductive
performance has been linked to the shortage of speci-
ﬁc nutrients in wild vertebrate (Kitaysky et al. 2006)
and invertebrate (Salomon et al. 2008) predators,
suggesting a ﬁtness incentive for nutrient-speciﬁc for-
aging. Population declines in carnivorous marine ver-
tebrates have been linked to the reduction in the
concentration of lipids in available prey (
et al. 2008). Evidence for sex-speciﬁc macronutrient
foraging strategies in a wild avian carnivore has
recently been provided for Australasian gannets
(Morus serrator), in which males consistently captured
prey with higher protein-to-lipid ratios and lower
lipid-to-water ratios than females (Machovsky-
Capuska et al. 2016a). Finally, a macronutrient bal-
ance perspective to carnivore foraging can help to
explain observations of wild vertebrate predators, for
example, the tendency of grizzly bears (Ursus arctos),
an omnivorous member of Order Carnivora, to mix a
diet composed of both meat and fruit (Robbins et al.
2007; Coogan et al. 2014), and of some predators to
target organs of prey, such as liver and brain, which
are high in non-protein energy (Stahler et al. 2006;
Kohl et al. 2015).
Combined with the evidence from laboratory stud-
ies, there is thus a strong prima facie case to suspect
that prey selection by predators in the wild is guided
by speciﬁc nutrient content and not energy per se
(Machovsky-Capuska et al. 2016a). To date, however,
the study of macronutrient balancing by vertebrates
in the wild has largely been limited to primates,
including predominately herbivorous Peruvian spider
monkeys (Ateles chamek, Felton et al. 2009), mountain
gorillas (Gorilla beringei beringei, Rothman et al. 2011)
and sifakas (Propithecus diadema, Irwin et al. 2015),
and the more omnivorous chacma baboon (Papio
hamadryas ursinus, Johnson et al. 2013). These studies
showed that, as in laboratory studies of other taxa,
foraging was strongly associated with the ratio of
macronutrients. Field studies have focused on pri-
mates largely because these mammals can be habitu-
ated to human presence, allowing continuous
observations over prolonged periods to be made of
focal individuals from close range. The use of direct
observations to study the nutritional ecology of wild
carnivores is, by contrast, considerably more challeng-
ing. In some circumstances, predators may be habitu-
ated to human presence thereby allowing direct
collection of data on feeding behaviour, for example,
in observations of large African carnivores from vehi-
cles (Mills 1992). However, due to the opportunistic
nature of predation, such observations provide an
incomplete and relatively brief account of feeding,
which is useful for reconstructing group-level diets
but pose considerable challenges for determining the
diets of individuals and quantifying intake rates
(Rapson & Bernard 2007).
Consequently, several indirect techniques are com-
monly used to assess prey consumption and diet in
predators. Faecal (scat) analyses have been used to
identify the species and size of prey consumed in a
diversity of predators (Bigg & Fawcett 1985; Wachter
et al. 2012). This technique can be valuable for recon-
structing animal diets at the population level, and can
even provide evidence for detecting nutrient balanc-
ing from population studies (e.g. Remonti et al.
2015). In most cases, however, scat analysis provides
only a snapshot of the diets of individuals and is sus-
ceptible to bias due to differential digestibility of foods
(Marker et al. 2003; Jethva & Jhala 2004; Heaslip
et al. 2012).
Stable isotope anlayses (Boecklen et al. 2011) and
quantitative fatty acid signature analyses (QFASA)
(Iverson et al. 2004) are both techniques that are used
for reconstructing diets of animals indirectly through
measuring their impacts on the chemical composition
of the predators. Neither technique provides the reso-
lution offered by direct observation for identifying
either the food items consumed or the nutrient con-
tent of the diet, but both offer advantages, for exam-
ple, in integrating diets over longer periods than is
usually feasible using direct observation (Layman
et al. 2012; Bromaghin et al. 2016a, b).
Detailed discussion of the relative advantages and
disadvantages of these indirect techniques vs. direct
observation is beyond the scope of this paper. We do,
however, wish to emphasise two points: ﬁrstly, these
are not necessarily competing approaches but rather
complementary approaches each of which is suited to
speciﬁc questions and contexts; secondly, of the tech-
niques direct observation provides the highest resolu-
tion for testing hypotheses about the nutritional
drivers of prey selection by predators in the wild, and
yet is the least developed for use on predators.
An important priority is to develop the use of direct
observation thus enabling the collection of data
needed to apply geometric analysis to understand the
foraging priorities of predators in the wild. For this,
three primary challenges need to be overcome: (1)
the difﬁculties of recording predator foraging beha-
viour in the wild; (2) obtaining accurate estimates of
prey selection and consumption, including the selec-
tion of speciﬁc prey species, the proportion of carcass
consumed and the consumption of selected body
parts; and (3) collection of prey consumed (e.g. either
the entire body or selected body parts) for chemical
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH 3
G. E. Machovsky-Capuska et al. Motive for Killing
composition analysis (Machovsky-Capuska et al.
2016b). Recent technological advances offer consider-
able potential to help meet these challenges.
Biologging, Nutritional Geometry and Central-place
A promising approach for collecting data on the forag-
ing behaviour of wild predators [challenge (1) and (2)
above] is the application of ‘biologging science’ (Naito
2004; Ropert-Coudert & Wilson 2005). In this ﬁeld,
technological advances over the past four decades
have been harnessed to enable the remote measure-
ment of data for free-ranging animals using animal-
borne electronic devices, thereby expanding the
ability of ecologists to study inaccessible and/or
dangerous wildlife. Miniaturised data loggers can
collect and store a diverse range of information from
multiple sensors, such as global positioning systems,
altimeter recorders, accelerometers and temperature
thermistors. The deployment of such devices on
unhabituated free-ranging species has allowed for
high sampling frequencies that have greatly increased
our understanding of animal movements, foraging
patterns, physiology and behavioural ecology (re-
viewed in Ropert-Coudert et al. 2009 and also in Dell
et al. 2014). Furthermore, the deployment of multiple
loggers has enabled researchers to construct 3-D pro-
ﬁles of the environments in which animals interact,
thereby simulating direct observations without visual
Recent technological developments in animal-
borne sensors, including Animal-borne Video and
Environmental Data collection systems (AVEDs),
have provided the opportunity to gather visual infor-
mation from the animals’ perspective expanding the
ability to answer behavioural, physiological and eco-
logical questions by collecting data on multiple vari-
ables, as well as multiple animals, simultaneously
(Marshall 1998; Davis et al. 1999; Moll et al. 2007).
These deployments have enabled researchers to
obtain diverse and detailed information of wild preda-
tors, including social behaviour (Sakamoto et al.
2009), prey capture success (Davis et al. 1999; Taka-
hashi et al. 2008; Heaslip et al. 2012; Kane & Zamani
2014; Kane et al. 2015) and environmental character-
istics of predator habitats (Gr
emillet et al. 2010; Votier
et al. 2013). AVEDs have been successfully deployed
on a variety of large predators from terrestrial and
aquatic habitats (Table 1). These studies have yielded
tantalising glimpses of what is possible using AVEDs,
providing a foundation for more detailed studies on
predator nutritional ecology.
Biologging sensors and AVEDs have the capacity to
provide ﬁne-scale detailed information of predator
foraging movements [challenge (1)], and prey choice
and intake [challenge (2)] from the animals’ perspec-
tive when they cannot be directly observed, and
hence some of the data required to elucidate nutri-
tional drivers of foraging. In addition, biologging tech-
nology can also gather continuous foraging data over
a relatively long period (from days to weeks), which
will aid in understanding temporal trends in diet.
The challenge remains, however, of how to accu-
rately estimate nutrient intake from prey consump-
tion [challenge (3)], as the individual prey or selected
body parts collected for chemical composition analysis
should be undigested and preferably obtained from
the relevant geographic and temporal scale (Tait et al.
2014; Machovsky-Capuska et al. 2016a). Terrestrial
and aquatic avian central-place predators provide
exemplar systems in which prey can be collected post-
capture when the predator returns to a home base
after their foraging trips (Orians & Pearson 1979). A
Table 1: Summary of Animal-borne Video and Environmental Data
collection systems (AVEDs) that have successfully been deployed in a
variety of large terrestrial and aquatic predators. This summary is pro-
vided as an illustration and is not exhaustive.
Common name Scientiﬁc name References
Gyrfalcons Falco rusticolus Kane & Zamani (2014)
Peregrine falcons F. peregrinus Kane & Zamani (2014)
Goshawks Accipiter gentilis Kane et al. (2015)
African lions Panthera leo UP from G. Marshall in
Moll et al. (2007)
Weddell seals Leptonychotes
Davis et al. (1999)
Harbour seals Phoca vitulina Bowen et al. (2002)
Nifong et al. (2014)
Blue whales Balaenoptera musculus Calambokidis et al. (2007)
Tiger sharks Galeocerdo cuvier Heithaus et al. (2001)
Emperor penguins Aptenodytes forsteri Ponganis et al. (2000)
Gentoo penguins Pygoscelis papua Takahashi et al. (2008)
Sakamoto et al. (2009)
Northern gannets Morus bassanus Votier et al. (2013)
Cape gannets Morus capensis Gr
emillet et al. (2010),
Thiebault et al. (2014)
Green turtles Chelonia mydas Heithaus et al. (2002),
Arthur et al. (2007)
Caretta caretta Heithaus et al. (2002)
Heaslip et al. (2012)
Masked boobies Sula dactylatra
et al. (2016b)
UP, unpublished data.
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH4
Motive for Killing G. E. Machovsky-Capuska et al.
successful central-place forager has the complex chal-
lenge of balancing its own nutritional needs with the
needs of their offspring by provisioning them with
food obtained while foraging often through regurgita-
tions (Machovsky-Capuska et al. 2014). These preda-
tors therefore provide the rare opportunity to
estimate the amounts and proportional nutritional
composition of consumed prey and overall diets by
collecting undigested regurgitations that can then be
analysed for chemical composition (Tait et al. 2014;
Machovsky-Capuska et al. 2016a, b).
Recently, a study of a wild avian carnivorous cen-
tral-place forager (masked booby, Sula dactylatra tas-
mani) took advantage of their foraging behaviour
becoming the ﬁrst to overcome challenges (1)–(3) and
combine biologging data with NG (Fig. 1a–f;
Machovsky-Capuska et al. 2016b). Firstly, biologging
technology was attached to birds to collect data
required to elucidate foraging behaviour and prey
selection (Fig. 1a, b). Upon returning to home base,
regurgitations were obtained from masked boobies
and subsequently taken to the laboratory for chemical
composition analysis (Fig. 1c, d). Finally, nutritional
data from consumed prey were modelled using NG to
determine whether the predators were selecting a
consistent ratio of macronutrients or maintaining
some other speciﬁc nutritional parameter constant,
such as protein or lipid intake (Fig. 1e, f; Machovsky-
Capuska et al. 2016a). The results revealed the
amounts of macronutrients consumed in each dive
and the overall nutrient intake per foraging trip. As
well as furthering the understanding of nutritional
priorities of predators, such data enable the estimation
of important nutritional performance parameters
related to the foraging effort, for example the relation-
ships between the gain of speciﬁc nutrients and forag-
ing effort (e.g. time spent foraging, distance travelled
or predation effort).
Studies integrating biologging technology with NG
are not restricted to avian central-place predators,
however, and in fact could be applied quite broadly
to investigate the nutritional ecology of a variety of
carnivores from numerous systems. For example,
studies using miniaturised cameras on predators
where it is not possible to collect undigested prey
samples can beneﬁt from the use of software that
enables the size of prey captured to be estimated
using morphological features of the predator for ref-
erence (e.g. beak length, maximum width of turtles’
head; Heaslip et al. 2012). Although this can be chal-
lenging, recent technological advances offer the
opportunity to automatically extract these measure-
ments from images obtained from video footage in a
process called ‘automated image-based tracking’ (Dell
et al. 2014). Once the size of the prey animal or
speciﬁc body parts consumed has been extracted
from the video footage, a sample could be collected
for chemical composition analysis or representative
data can be extracted from the literature (Tait et al.
2014) and then modelled using NG. While use of lit-
erature data to estimate the chemical compositions of
prey can present obvious limitations, its use can also
provide valuable information for understanding the
nutritional ecology of wild predators when prey sam-
pling is impractical or unattainable (Remonti et al.
Several categories of questions can be addressed
by combining biologging data with NG. For exam-
ple, quantifying the patterns of macronutrient gains
using NG (Fig. 1e) can be used to test for active
regulation of macronutrient intake in non-invasive
ﬁeld studies. Active regulation is suggested if two or
more populations use different combinations of
foods in their diets in unique proportions to gain
similar nutrient intakes, as has been measured for
wild mountain gorillas (Rothman et al. 2007;
Raubenheimer et al. 2015). If desirable, the ratios
of nutrients in the predator’s diet (Fig. 1e) can be
compared to nutritional estimates of animals that are
available but not selected as prey species. For this,
samples of non-selected animals can be identiﬁed
from AVED data and collected from the ﬁeld for nutri-
tional analysis, or if not feasible nutritional estimates
could be obtained from the literature (e.g. Coogan
et al. 2014; Tait et al. 2014; Remonti et al. 2015). In
addition to nutrients, NG allows broader factors such
as ‘animal ﬁbre’ (e.g. chitin, bones and hair; Depauw
et al. 2013) or toxins (Lei et al. 2015) to be included
within models as dimensions in their own right and
their corresponding interactive effects (including neg-
ative effects) to be determined. Additionally, non-
nutritional variables (e.g. ﬁtness proxies such as
reproduction and growth) can be added to the model
as a response surfaces (e.g. Jensen et al. 2012).
Although different predators will present speciﬁc
challenges, it is clear that this approach holds consid-
erable potential for answering the question of
whether wild predators forage for energy per se or to
optimise their nutrient intake (Fig. 1f).
Conclusions and Future Directions
If results from laboratory studies on predator foraging
are borne out in the wild, this will suggest that nutri-
ent balancing is general across trophic levels with
important implications for understanding how
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH 5
G. E. Machovsky-Capuska et al. Motive for Killing
ecosystems work (Raubenheimer et al. 2009; Simp-
son et al. 2010; Wilder et al. 2013). It will also pro-
vide fresh theoretical insight into functional
characteristics of predators, including such fundamen-
tal concepts as the ecological niche and the general-
ist–specialist distinction (Machovsky-Capuska et al.
2016c), with direct practical signiﬁcance. For exam-
ple, it could contribute to predicting the efﬁcacy of
generalist predators as pest control agents (Symond-
son et al. 2002), predicting the invasive potential of
predators (Machovsky-Capuska et al. 2016c), and
identifying the dietary needs of endangered species,
helping to manage the appropriate foods and the
diversity of habitats in which they live (Rauben-
heimer et al. 2012; Nie et al. 2014). Where habitat is
lost, such information can be critical for identifying
suitable new habitats that could be used for species
translocations (Raubenheimer & Simpson 2006).
Understanding the foraging priorities of predators
could also enhance monitoring programmes of wild-
life health, for example to follow the fate of rehabili-
tated animals post-release into the wild (Machovsky-
Capuska et al. 2016b) or gain a better understanding
of the inﬂuence of weather-related ﬂuctuations on
food availability and how this impacts on carnivore
foraging and performance. It could also aid in predict-
ing and managing the consequences of anthropogenic
inﬂuences on animal food resources and foraging
behaviour, including ﬁsheries–wildlife interactions
Osterblom et al. 2008) and human–wildlife conﬂict,
as demonstrated by Coogan & Raubenheimer (2016).
The integration of the multidimensional nutri-
tional framework NG and data collecting abilities
offered by biologging and, in particular, AVEDs could
thus signiﬁcantly advance the science of nutritional
ecology and its applications, by helping insights
derived from laboratory studies to be interpreted and
extended in the context of the wild. Biologging tech-
nology has been successfully integrated with tradi-
tional methods of diet estimation in predators, such
as scats, stable isotopes and fatty acid analyses
(Iverson et al. 2004; Zeppelin et al. 2015) and
recently NG (Machovsky-Capuska et al. 2016b).
AVEDS clearly offer the advantage of providing an
animal’s perspective on dietary intake by directly
recording what is selected and consumed. A power-
ful approach would be to combine biologging tech-
nology, in particular AVEDS, with indirect methods
such as scat analysis, stable isotopes, nutritional
analysis of prey and NG in laboratory and ﬁeld-based
research projects. Such information can help to tri-
angulate diverse information across temporal and
geographical scales to better understand the nutri-
tional ecology of predators.
We would like to thank R. Bshary and the anony-
mous referees for useful comments that have
enhanced the manuscript. This research was funded
by Faculty of Veterinary Science DV Compact
Research fund (The University of Sydney). GEMC is
supported by the Loxton research fellowship from the
Faculty of Veterinary Science, The University of Syd-
ney. DR and SJS are funded by Australian Research
Council grant LP140100235.
Arthur, K. E., O’Neil, J. M., Limpus, C. J., Abernathy, K. &
Marshall, G. J. 2007: Using animal-borne imaging to
assess green turtle (Chelonia mydas) foraging ecology in
Moreton Bay, Australia. Mar. Tech. Soc. J. 41,9—13.
Bigg, M. A. & Fawcett, I. 1985: Two biases in diet determi-
nation of northern fur seals (Callorhinus ursinus). In:
Marine Mammals and Fisheries. (Beddington, J., Bever-
ton, R., Lavigne, D., eds). George Allen and Unwin Ltd,
London, pp. 284—291.
Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C.
2011: On the use of stable isotopes in trophic ecology.
Annu. Rev. Ecol. Evol. Syst. 42, 411—440.
Bowen, W. D., Tully, D., Boness, D. J., Bulheier, B. M. &
Marshall, G. J. 2002: Prey dependent foraging tactics
and prey proﬁtability in a marine mammal. Mar. Ecol.
Prog. Ser. 244, 235—245.
Bromaghin, J. F., Budge, S. M. & Thiemann, G. W. 2016a:
Should fatty acid signature proportions sum to 1 for diet
estimation? Ecol. Res. 31,1—10. doi:10.1007/s11284-
Bromaghin, J. F., Budge, S. M., Thiemann, G. W. & Rode,
K. D. 2016b: Assessing the robustness of quantitative
fatty acid signature analysis to assumption violations.
Methods Ecol. Evol. 7,51—59.
Calambokidis, J., Schorr, G. S., Steiger, G. H., Francis, J.,
Bakhtiari, M., Marshall, G. J., Oleson, E. M., Gendron,
D. & Robertson, K. 2007: Insights into the underwater
diving, feeding, and calling behavior of blue whales
from a suction-cup-attached video-imaging tag (CRIT-
TERCAM). Mar. Tech. Soc. J. 41,19—29.
Charnov, E. L. 1976: Optimal foraging: the marginal value
theorem. Theor. Pop. Biol. 9, 129—136.
Coogan, S. C. P. & Raubenheimer, D. 2016: Might
macronutrient requirements inﬂuence grizzly-bear
human conﬂict? Insights from nutritional geometry
Ecosphere 7, e01204.
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH6
Motive for Killing G. E. Machovsky-Capuska et al.
Coogan, S. C. P., Raubenheimer, D., Stenhouse, G. B. &
Nielsen, S. E. 2014: Macronutrient optimization and
seasonal diet mixing in a large omnivore, the grizzly
bear: a geometric analysis. PLoS ONE 9, e97968.
Davis, R. W., Fuiman, L. A., Williams, T. M., Collier, S. O.,
Hagey, W. P., Kanatous, S. B., Kohin, S. & Horning, M.
1999: Hunting behaviour of a marine mammal beneath
the Antarctic fast ice. Science 283, 993—995.
Dell, A. I., Bender, J. A., Branson, K., Couzin, I. D., de
Polavieja, G. G., Noldus, L. P., P
erez-Escudero, A., Per-
ona, P., Straw, A. D., Wikelski, M. & Brose, U. 2014:
Automated image-based tracking and its application in
ecology. Trends Ecol. Evol. 29, 417—428.
Depauw, S., Hesta, M., Whitehouse-Tedd, K., Vanhaecke,
L., Verbrugghe, A. & Janssens, G. P. J. 2013: Animal
ﬁbre: the forgotten nutrient in strict carnivores? First
insights in the cheetah. J. An. Physiol. An. Nut. 97,
Eisert, R. 2011: Hypercarnivory and the brain: protein
requirements of cats reconsidered. J. Comp. Physiol. B.
Felton, A. M., Felton, A., Raubenheimer, D., Simpson, S.
J., Foley, W. J., Wood, J. T., Wallis, I. R. & Linden-
mayer, D. B. 2009: Protein content of diets dictates the
daily energy intake of a free-ranging primate. Behav.
Ecol. 20, 685—690.
emillet, D., Mullers, R. H., Moseley, C., Pichegru, L.,
Coetzee, J. C., Sabarros, P. S., Van Der Lingen, C. D.,
Ryan, P. G., Kato, A. & Ropert-Coudert, Y. 2010: Sea-
birds, ﬁsheries, and cameras. Front. Ecol. 8, 401—402.
Hawlena, D. & Schmitz, O. J. 2010: Herbivore physiologial
response to predation risk and implications for nutrient
dynamics. Proc. Natl Acad. Sci. USA 107, 15503—15507.
Heaslip, S. G., Iverson, S. J., Bowen, W. D. & James, M. C.
2012: Jellyﬁsh support high energy intake of leather-
back sea turtles (Dermochelys coriacea): video evidence
from animal-borne cameras. PLoS ONE 7, e33259.
Heithaus, M. R., Marshall, G. J., Buhleier, B. M. & Dill, L.
M. 2001: Employing Crittercam to study habitat use
and behavior of large sharks. Mar. Ecol. Prog. Ser. 209,
Heithaus, M. R., McLash, J. J., Frid, A., Dill, L. M. & Mar-
shall, G. J. 2002: Novel insights into green sea turtle
behaviour using animal-borne video cameras. J. Mar.
Biol. Assoc. 82, 1049—1050.
Hewson-Hughes, A. K., Hewson-Hughes, V. L., Miller,
A. T., Hall, S. R., Simpson, S. J. & Raubenheimer,
D. 2011: Geometric analysis of macronutrient selec-
tion in the adult domestic cat, Felis catus. J. Exp.
Biol. 214, 1039—1051.
Hewson-Hughes, A. K., Hewson-Hughes, V. L., Colyer, A.,
Miller, A. T., Hall, S. R., Simpson, S. J. & Raubenheimer,
D. 2012: Geometric analysis of macronutrient selection
in breeds of the domestic dog, Canis lupus familiaris.
Behav. Ecol. 24, 293—304.
Hewson-Hughes, A. K., Hewson-Hughes, V. L., Colyer, A.,
Miller, A. T., Hall, S. R., Raubenheimer, D. & Simpson, S.
J. 2013: Consistent proportional macronutrient intake
selected by adult domestic cats (Felis catus) despite varia-
tions in macronutrient and moisture content of foods
offered. J. Comp. Physiol. B. 183,525—
Irwin, M. T., Raharison, J. L., Raubenheimer, D. R., Chap-
man, C. A. & Rothman, J. M. 2015: The nutritional
geometry of resource scarcity: effects of lean seasons
and habitat disturbance on nutrient intakes and balanc-
ing in wild sifakas. PLoS ONE 10, e0128046.
Iverson, S. J., Field, C., Don Bowen, W. & Blanchard, W.
2004: Quantitative fatty acid signature analysis: a new
method of estimating predator diets. Ecol. Monogr. 74,
Jensen, K., Mayntz, D., Toft, S., Clissold, F. J., Hunt, J.,
Raubenheimer, D. & Simpson, S. J. 2012: Optimal for-
aging for speciﬁc nutrients in predatory beetles. Proc. R.
Soc. Lond. B Biol. Sci. 279, 2212—2218.
Jensen, K., Simpson, S. J., Nielsen, V. H., Hunt, J.,
Raubenheimer, D. & Mayntz, D. 2014: Nutrient-speciﬁc
compensatory feeding in a mammalian carnivore, the
mink, Neovison vison. Br. J. Nutr. 112, 1226—1233.
Jeschke, J. M. 2007: When carnivores are ‘full and lazy’.
Oecologia 152, 357—364.
Jethva, B. D. & Jhala, Y. V. 2004: Computing biomass con-
sumption from prey occurences in Indian wolf scats.
Zoo. Biol. 23, 513—520.
Johnson, C. A., Raubenheimer, D., Rothman, J. M.,
Clarke, D. & Swedell, L. 2013: 30 days in the life: daily
nutrient balancing in a wild chacma baboon. PLoS ONE
Kane, S. A. & Zamani, M. 2014: Falcons pursue prey using
visual motion cues: new perspectives from animal-
borne cameras. J. Exp. Biol. 217, 225—234.
Kane, S. A., Fulton, A. H. & Rosenthal, L. J. 2015: When
hawks attack: animal-borne video studies of goshawk
pursuit and prey-evasion strategies. J. Exp. Biol. 218,
Kitaysky, A., Kitaiskaia, E., Piatt, J. & Wingﬁeld, J. 2006: A
mechanistic link between chick diet and decline in sea-
birds? Proc. Roy. Soc. B. Biol. Sci. 273,45—450.
Kohl, K. D., Coogan, S. C. P. & Raubenheimer, D. 2015:
Do wild carnivores forage for prey or for nutrients?
BioEssays 6, 701—709.
Koojiman, S. A. L. M., Andersen, T. & Kooi, B. W. 2004:
Dynamic energy budget representations of stoichiomet-
ric constraints on population dynamics. Ecology 85,
Layman, C. A., Araujo, M. S., Boucek, R., Hammerschlag-
Peyer, C. M., Harrison, E., Jud, Z. R., Matich, P., Rosen-
blatt, A. E., Vaudo, J. J., Yeager, L. A. & Post, D. M.
2012: Applying stable isotopes to examine food-web
structure: an overview of analytical tools. Biol. Rev. 87,
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH 7
G. E. Machovsky-Capuska et al. Motive for Killing
Le Couteur, D. G., Tay, S. S., Solon-Biet, S. M., Bertolino,
P., McMahon, A. C., Cogger, V. C., Colakoglu, F., War-
ren, A., Holmes, A. J., Pichaud, N., Horan, M., Correa,
C., Melvin, R. G., Turner, N., Ballard, J. W. O., Ruoho-
nen, K., Raubenheimer, D. & Simpson, S. J. 2014: The
inﬂuence of macronutrients on splanchnic and hepatic
lymphocytes in aging mice. J. Gerontol. A Biol. Sci.
Med. Sci. 70, 1499—1507.
Lee, K. P., Simpson, S. J., Clissold, F. J., Brooks, R., Bal-
lard, J. W. O., Taylor, P. W., Soran, N. & Raubenheimer,
D. 2008: Lifespan and reproduction in Drosophilia: new
insights from nutritional geometry. Proc. Natl Acad. Sci.
USA 105, 2498—2503.
Lei, W., Ravoninjohary, A., Li, X., Margolskee, R. F., Reed,
D. R., Beauchamp, G. K. & Jiang, P. 2015: Functional
analyses of bitter taste receptors in domestic cats (Felis
catus). PLoS ONE 10, e0139670.
Lenky, C., Eisert, R., Oftedal, O. T. & Metcalf, V. 2012:
Proximate composition and energy density of notothe-
niid and myctophid ﬁsh in McMurdo Sound and the
Ross Sea, Antarctica. Polar Biol. 35, 717—724.
Machovsky-Capuska, G. E., Hauber, M. E., Dassis, M., Libby,
E., Wikelski, M. C., Schuckard, R., Melville, D., Cook, W.,
Houston, M. & Raubenheimer, D. 2014: Foraging beha-
viour and habitat use of chick rearing Australasian Gan-
Machovsky-Capuska, G. E., Priddel, D., Leong, P. H. W.,
Jones, P., Carlile, N., Shannon, L., Portelli, D., McEwan,
A., Chaves, A. & Raubenheimer, D. 2016a: Coupling
bio-logging with nutritional geometry to reveal novel
insights into the foraging behaviour of a plunge-diving
marine predator. New Zeal. J. Mar. Fresh. Res.
Machovsky-Capuska, G. E., Senior, A. M., Benn, E., Tait,
A. H., Schuckard, R., Stockin, K. A., Cook, W., Ogle, M.,
Barna, K., Melville, D., Wright, B., Purvin, C. &
Raubenheimer, D. 2016b: Sex-speciﬁc macronutrient
foraging strategies in a successful marine predator: the
Australasian gannet. Mar. Biol. 163, 75.
Machovsky-Capuska, G. E., Senior, A. M., Simpson, S. J. &
Raubenheimer, D. 2016c: The multi-dimensional nutri-
tional niche. Trends Ecol. Evol. 31, 355—365.
Maklakov, A. A., Simpson, S. J., Zajitschek, F., Hall, M. D.,
Dessman, J., Clissold, F. J., Zajitschek, F., Lailvaux, S.
P., Raubenheimer, D., Bonduriansky, R. & Brooks, R. C.
2008: Sex-speciﬁc ﬁtness effects of nutrient intake on
reproduction and lifespan. Curr. Biol. 18, 1062—1066.
Marker, L. L., Muntifering, J. R., Dickman, A. J., Mills, M.
G. L. & Macdonald, D. W. 2003: Quantifying prey pref-
erences of free-ranging Namibian cheetahs. S. Afr. J.
Wildl. Res. 33,43—53.
Marshall, G. J. 1998: Crittercam: an animal-borne imaging
and data logging system. Mar. Tech. Soc. J. 32,11—17.
Mayntz, D., Nielsen, V. H., Sørensen, A., Toft, S., Rauben-
heimer, D., Hejlesen, C. & Simpson, S. J. 2009:
Balancing of protein and lipid intake by a mammalian
carnivore, the mink, Mustela vison. Anim. Behav. 77,
Mills, M. G. L. 1992: A comparison of methods used to
study food habits of large African carnivores. In: Wildlife
2001: Populations. Springer Netherlands, Dordrecht, pp.
Moll, R. J., Millspaugh, J. J., Beringer, J., Sartwell, J. &
He, Z. 2007: A new “view” of ecology and conservation
through animal-borne video systems. Trends Ecol. Evol.
Naito, Y. 2004: Bio-logging science. Mem. Natl. Inst. Polar
Res. Spec. Issue 58, 118—132.
Nie, Y., Zhang, Z., Raubenheimer, D., Elser, J. J., Wei, W.
& Wei, F. 2014: Obligate herbivory in an ancestrally
carnivorous lineage: the giant panda and bamboo from
the perspective of nutritional geometry. Funct. Ecol. 29,
Nifong, J. C., Nifong, R. L., Silliman, B. R., Lowers, R. H.,
Guillette, L. J. Jr, Ferguson, J. M., Welsh, M., Aber-
nathy, K. & Marshall, G. 2014: Animal-borne imaging
reveals novel insights into the foraging behaviors and
Diel activity of a large-bodied apex predator, the Ameri-
can alligator (Alligator mississippiensis). PLoS ONE 9,
Orians, G. H. & Pearson, N. E. 1979: On the Theory of Cen-
tral Place Foraging. Analysis of Ecological Systems. Ohio
State Univ. Press, Columbus, OH.
Osterblom, H., Olsson, O., Blenckner, T. & Furness, R. W.
2008: Junk-food in marine ecosystems. Oikos 117,
Plantinga, E. A., Bosch, G. & Hendriks, W. H. 2011: Esti-
mation of the dietary nutrient proﬁle of free-roaming
feral cats: possible implications for nutrition of domestic
cats. Br. J. Nutr. 106, S35—S48.
Ponganis, P. J., Van Dam, R. P., Marshall, G., Knower, T. &
Levenson, D. H. 2000: Sub-ice foraging behavior of
emperor penguins. J. Exp. Biol. 203, 3275—3278.
Rapson, J. A. & Bernard, R. T. F. 2007: Interpreting the
diet of lions (Panthera leo); a comparison of various
methods of analysis. S. Afr. J. Wildl. Res. 37, 179—187.
Raubenheimer, D. & Simpson, S. J. 1993: The geometry of
compensatory feeding in the locust. Anim. Behav. 45,
Raubenheimer, D. & Rothman, J. M. 2013: Nutritional
ecology of entomophagy in humans and other primates.
Ann. Rev. Entomol. 58, 141—160.
Raubenheimer, D. & Simpson, S. J. 2006: The challenge of
supplementary feeding: can geometric analysis help
save the kakapo? Notornis 53, 100—111.
Raubenheimer, D., Simpson, S. J. & Mayntz, D. 2009:
Nutrition, ecology and nutritional ecology: toward an
integrated framework. Funct. Ecol. 23,4—16.
Raubenheimer, D., Simpson, S. J. & Tait, A. H. 2012:
Match and mismatch: conservation physiology,
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH8
Motive for Killing G. E. Machovsky-Capuska et al.
nutritional ecology and the timescales of biological
adaptation. Philos. Trans. R. Soc. Lond. B. 367,
Raubenheimer, D., Machovsky-Capuska, G. E., Chapman,
C. A. & Rothman, J. M. 2015: Geometry of nutrition in
ﬁeld studies: an illustration using wild primates. Oecolo-
gia 177, 223—234.
Remonti, L., Balestrieri, A., Raubenheimer, D. & Saino, N.
2015: Functional implications of omnivory for dietary
nutrient balance. Oikos. doi:10.1111/oik.02801.
Robbins, C. T., Fortin, J. K., Rode, K. D., Farley, S. D.,
Shipley, L. A. & Felicetti, L. A. 2007: Optimizing protein
intake as a foraging strategy to maximize mass gain in
an omnivore. Oikos 10, 1675—1682.
Ropert-Coudert, Y. & Wilson, R. P. 2005: Trends and per-
spectives in animal-attached remote sensing. Front.
Ecol. Envir. 3, 437—444.
Ropert-Coudert, Y., Beaulieu, M., Hanuise, N. & Kato, A.
2009: Diving into the world of biologging. Endanger.
Species Res. 10,21—27.
Rothman, J. M., Plumptre, A. J., Dierenfeld, E. S. & Pell,
A. N. 2007: Nutritional composition of the diet of the
gorilla (Gorilla beringei): a comparison between two
montane habitats. J. Trop. Ecol. 23, 673—682.
Rothman, J. M., Raubenheimer, D. & Chapman, C. A.
2011: Nutritional geometry: gorillas prioritize non-pro-
tein energy while consuming surplus protein. Biol. Lett.
Sakamoto, K. Q., Takahashi, A., Iwata, T. & Trathan, P. N.
2009: From the eye of the albatrosses: a bird-borne cam-
era shows an association between albatrosses and a killer
whale in the Southern Ocean. PLoS ONE 4, e7322.
Salomon, M., Mayntz, D. & Lubin, Y. 2008: Colony nutri-
tion skews reproduction in a social spider. Behav. Ecol.
Simpson, S. J. & Raubenheimer, D. 2012: The Nature of
Nutrition: An Integrative Framework from Animal
Adaptation to Human Obesity. Princeton Univ. Press,
Simpson, S. J., Sibly, R. M., Lee, K. P., Behmer, S. T. &
Raubenheimer, D. 2004: Optimal foraging when regu-
lating intake of multiple nutrients. Anim. Behav. 68,
Simpson, S. J., Raubenheimer, D., Charleston, M. A. &
Clissold, F. J. 2010: Modelling nutritional interactions:
from individuals to communities. Trends Ecol. Evol. 21,
Simpson, S. J. & Raubenheimer, D. 1993: A multi-level
analysis of feeding behaviour: The geometry of nutri-
tional decisions. Philos. Trans. R. Soc. B. 342, 381—402.
Solon-Biet, S. M., McMahon, A. C., Ballard, J. W. O., Ruo-
honen, K., Wu, L. E., Cogger, V. C., Warren, A., Huang,
X., Pichaud, N., Melvin, R. G., Gokam, R., Khalil, M.,
Turner, N., Cooney, G. J., Sinclair, D. A.,
Raubenheimer, D., Le Couteur, D. G. & Simpson, S. J.
2014: The ratio of macronutrients, not caloric intake,
dictates cardiometabolic health, aging, and longevity in
ad libiutm-fed mice. Cell Metab. 19, 418—430.
Solon-Biet, S. M., Walters, K. A., Simanainen, U. K.,
McMahon, A. C., Ruohonen, K., Ballard, J. W. O.,
Raubenheimer, D., Handelsman, D. J., Le Couteur, D.
G. & Simpson, S. J. 2015: Macronutrient balance, repro-
ductive function, and lifespan in aging mice. Proc. Natl
Acad. Sci. USA 112, 3481—3486.
Stahler, D. R., Smith, D. W. & Guernsey, D. S. 2006: Forag-
ing and feeding ecology of the gray wolf (Canis lupus):
lessons from Yellowstone National Park, Wyoming,
USA. J. Nutr. 136, S1923—S1926.
Stephens, D. W. & Krebs, J. R. 1986: Foraging Theory.
Princeton Univ. Press, Princeton, NJ.
Symondson, W. O. C., Sunderland, K. D. & Greenstone,
M. H. 2002: Can generalist predators be effective bio-
control agents? Annu. Rev. Entomol. 47, 561—594.
Tait, A., Raubenheimer, D., Stockin, K. A., Merriman, M.
& Machovsky-Capuska, G. E. 2014: Nutritional geome-
try of gannets and the challenges in ﬁeld studies. Mar.
Biol. 12, 2791—2801.
Takahashi, A., Kokubun, N., Mori, Y. & Shin, H. C.
2008: Krill-feeding behaviour of gentoo penguins as
shown by animal-borne camera loggers. Polar Biol.
Thiebault, A., Mullers, R., Pistorius, P., Meza-Torres, M.
A., Dubroca, L., Green, D. & Tremblay, Y. 2014:
From colony to ﬁrst patch: processes of prey searching
and social information in Cape gannets. Auk 131,
Votier, S. C., Bicknell, A., Cox, S. L., Scales, K. L. & Patrick,
S. C. 2013: A bird’s eye view of discard reforms: bird-
borne cameras reveal seabird/ﬁshery interactions. PLoS
ONE 8, e57376.
Wachter, B., Blanc, A. S., Melzheimer, J., H€
oner, O. P.,
Jago, M. & Hofer, H. 2012: An advanced method to
assess the diet of free-ranging large carnivores based on
scats. PLoS ONE 7, e38066.
Westoby, M. 1978: What are the biological bases of varied
diets? Am. Nat. 112, 627—631.
Whelan, C. J. & Schmidt, K. A. 2007: Food acquisition,
processing, and digestion. In: Foraging. (Stephens, D.
W., Brown, J. S., Ydenberg, R. C., eds). The Univ. of
Chicago Press, Chicago, IL, pp. 141—174.
Wilder, S. M., Norris, M., Lee, R. W., Raubenheimer, D. &
Simpson, S. J. 2013: Arthropod food webs become
increasingly lipid-limited at higher trophic levels. Ecol.
Lett. 16, 895—902.
Zeppelin, T. K., Johnson, D. S., Kuhn, C. E., Iverson, S. J.
& Ream, R. R. 2015: Stable isotope models predict forag-
ing habitat of northern fur seals (Callorhinus ursinus)in
Alaska. PLoS ONE 10, e0127615.
Ethology 122 (2016) 1–9 ©2016 Blackwell Verlag GmbH 9
G. E. Machovsky-Capuska et al. Motive for Killing