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Motive for Killing: What Drives Prey Choice in Wild Predators?


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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 foraging decisions of predators in the wild, but extending NG studies of carnivores 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 nutritional ecology.
Example to illustrate the potential of combining biologging and nutritional geometry to study nutrient selection in a predatory centralplace 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 laboratory ; (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 macronutrientbalanced . 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 regulatory responses to constrained variation in the compositions of available 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 constraint that prevented them from achieving their intake target.
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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
(R. Bshary)
doi: 10.1111/eth.12523
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-
tional ecology.
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-
heimer 2012).
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)
Leatherback sea
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. 1af;
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-
istspecialist 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 fisherieswildlife interactions
Osterblom et al. 2008) and humanwildlife 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.
Literature Cited
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. 41,913.
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. 284291.
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, 411440.
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, 235245.
Bromaghin, J. F., Budge, S. M. & Thiemann, G. W. 2016a:
Should fatty acid signature proportions sum to 1 for diet
estimation? Ecol. Res. 31,110. 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. 7,5159.
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. 41,1929.
Charnov, E. L. 1976: Optimal foraging: the marginal value
theorem. Theor. Pop. Biol. 9, 129136.
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, 993995.
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, 417428.
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, 685690.
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, 401402.
Hawlena, D. & Schmitz, O. J. 2010: Herbivore physiologial
response to predation risk and implications for nutrient
dynamics. Proc. Natl Acad. Sci. USA 107, 1550315507.
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, 10491050.
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, 10391051.
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, 293304.
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, 22122218.
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, 12261233.
Jeschke, J. M. 2007: When carnivores are ‘full and lazy’.
Oecologia 152, 357364.
Jethva, B. D. & Jhala, Y. V. 2004: Computing biomass con-
sumption from prey occurences in Indian wolf scats.
Zoo. Biol. 23, 513520.
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
8, e70438.
Kane, S. A. & Zamani, M. 2014: Falcons pursue prey using
visual motion cues: new perspectives from animal-
borne cameras. J. Exp. Biol. 217, 225234.
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. 273,45450.
Kohl, K. D., Coogan, S. C. P. & Raubenheimer, D. 2015:
Do wild carnivores forage for prey or for nutrients?
BioEssays 6, 701709.
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, 14991507.
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, 24982503.
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, 717724.
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, 355365.
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, 10621066.
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. 33,4353.
Marshall, G. J. 1998: Crittercam: an animal-borne imaging
and data logging system. Mar. Tech. Soc. J. 32,1117.
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.
22, 660668.
Naito, Y. 2004: Bio-logging science. Mem. Natl. Inst. Polar
Res. Spec. Issue 58, 118132.
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, S35S48.
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, 32753278.
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, 179187.
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, 141160.
Raubenheimer, D. & Simpson, S. J. 2006: The challenge of
supplementary feeding: can geometric analysis help
save the kakapo? Notornis 53, 100111.
Raubenheimer, D., Simpson, S. J. & Mayntz, D. 2009:
Nutrition, ecology and nutritional ecology: toward an
integrated framework. Funct. Ecol. 23,416.
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, 223234.
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, 16751682.
Ropert-Coudert, Y. & Wilson, R. P. 2005: Trends and per-
spectives in animal-attached remote sensing. Front.
Ecol. Envir. 3, 437444.
Ropert-Coudert, Y., Beaulieu, M., Hanuise, N. & Kato, A.
2009: Diving into the world of biologging. Endanger.
Species Res. 10,2127.
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, 673682.
Rothman, J. M., Raubenheimer, D. & Chapman, C. A.
2011: Nutritional geometry: gorillas prioritize non-pro-
tein energy while consuming surplus protein. Biol. Lett.
7, 847849.
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.
19, 605611.
Simpson, S. J. & Raubenheimer, D. 2012: The Nature of
Nutrition: An Integrative Framework from Animal
Adaptation to Human Obesity. Princeton Univ. Press,
Princeton, NJ.
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, 381402.
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, 418430.
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, 34813486.
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, S1923S1926.
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, 561594.
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, 27912801.
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.
31, 12911294.
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, 627631.
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. 141174.
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, 895902.
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
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... When evaluating a good diet, emphasis has often been put on calorie or energy net gain (Krebs, 1978;Machovsky-Capuska et al., 2016;Pulliam, 1975;Pyke, 1984). However, foods differ in their nutritional quality in terms of their composition and there are advantages in fulfilling caloric needs from a wider range of foods (Simpson & Raubenheimer, 2012). ...
... For them, the prevailing idea has been that they can gain all the necessary nutrients in the right proportion from their main prey (Kohl et al., 2015;Senior et al., 2015). Thus, a predator's only concern has been thought to be successful foraging, not what they forage for (Kohl et al., 2015;Machovsky-Capuska et al., 2016). To some extent, this is supported by species that are considered generalists but commonly consist of generalist and specialist individuals (Terraube et al., 2014). ...
... A higher diversity of different foods in the natal environment can affect young also via different pathways than nutrition (Kohl et al., 2015;Machovsky-Capuska et al., 2016). Benefits could be associated with learning experiences: many species experience sensitive learning windows at a young age (Dufty Jr et al., 2002;. ...
1. Early-life conditions can have long-term fitness consequences. However, it is still unclear what optimal rearing conditions are, especially for long-lived carnivores. A more diverse diet ('balanced diet') might optimize nutrient availability and allow young to make experiences with a larger diversity of prey, whereas a narrow diet breadth ('specialized diet') might result in overall higher energy net gain. A diet that is dominated by a specific prey type (i.e. fish, 'prey type hypothesis') might be beneficial or detrimental, depending for example, on its toxicity or contaminant load. 2. Generalist predators such as the white-tailed eagle Haliaeetus albicilla provide an interesting possibility to examine the relationship between early life diet and long-term offspring survival. In the Åland Islands, an archipelago in the Baltic Sea, white-tailed eagles live in various coastal habitats and feed on highly variable proportions of birds and fish. 3. We use data from 21,116 prey individuals that were collected from 120 territories during the annual surveys, to examine how early-life diet is associated with apparent annual survival of 574 ringed and molecular-sexed eaglets. We supplement this analysis by assessing the relationships between diet, reproductive performance and nestling physical condition, to consider whether they are confounding with possible long-term associations. 4. We find that early-life diet is associated with long-term fitness: Nestlings that are fed a diverse diet are in lower physical condition but have higher survival rates. Eagles that are fed more fish as nestlings have lower survival as breeding-age adults, but territories associated with fish-rich diets have higher breeding success. 5. Our results show that young carnivores benefit from a high diversity of prey in their natal territory, either through a nutritional or learning benefit, explaining the higher survival rates. The strong relationship between early-life diet and adult survival suggests that early life shapes adult foraging decisions and that eating fish is associated with high costs. This could be due to high levels of contaminants or high competition for fish-rich territories. Long-lasting consequences of early-life diet are likely not only limited to individual-level consequences but have the potential to drive eco-evolutionary dynamics in this population.
... Although the different types of food (hereafter prey) and their energy content have been extensively used to characterize the dietary niche of a species (Lindeman, 1942;Stephens and Krebs, 1986), an extensive body of literature suggests that modelling nutrients will enable to explore the intricacies of foraging, physiology, and ecology (Machovsky-Capuska et al., 2016a;Raubenheimer et al., 2009). To demonstrate the importance of a nutritional dimension into the levels of prey eaten ("prey composition niche") and the nutrient content of the dietary niche under ecological constraints ("realized nutritional niche"), an innovative Multidimensional Nutritional Niche Framework (MNNF) was recently developed (Machovsky-Capuska et al., 2016b). ...
... To demonstrate the importance of a nutritional dimension into the levels of prey eaten ("prey composition niche") and the nutrient content of the dietary niche under ecological constraints ("realized nutritional niche"), an innovative Multidimensional Nutritional Niche Framework (MNNF) was recently developed (Machovsky-Capuska et al., 2016b). This nutritionally explicit framework is particularly relevant to marine apex predators known to forage in complex and fluctuating marine environments (Machovsky-Capuska et al., 2016a;Machovsky-Capuska and Raubenheimer, 2020). While the characterization of nutritional niche breadths of marine predators has shown to be critical to trophic interactions, marine pollution, aquaculture, captivity and rehabilitation, climate change, and conservation and management of endangered species (Machovsky-Capuska and , yet the field remains poorly characterized to few species of seabirds (Machovsky-Capuska et al., 2016c, 2016dMiller et al., 2018;Tait et al., 2014), sharks (Grainger et al., 2020;Machovsky-Capuska and Raubenheimer, 2020), turtles , cetaceans (Denuncio et al., 2017;Machovsky-Capuska et al., 2019) and pinnipeds . ...
... It has been suggested that the abundance of small size demersal fishes, as consequence of overfishing, combined with the historical reduction of the SASL population have modified SAFS diet to focus on demersal/pelagic prey over time (Drago et al., 2017;Szteren et al., 2018). Overall, these findings support previous suggestions that marine predators in the wild explore very dynamic marine nutritional environments (Machovsky-Capuska et al., 2016aDenuncio et al., 2017;Machovsky-Capuska and Raubenheimer, 2020). While we recognized the potential uncertainties in the inherent variability of scats and stomach contents combined with spatiotemporal differences in the proximate compositions of prey, the proposed multimethodological approach is likely to overcome these limitations (Majdi et al., 2018;Tait et al., 2014), and provide a unique opportunity to better understand how sympatric marine mammal species coexist in the wild. ...
Niche segregation has been recognized as a valuable mechanism for sympatric species to reduce interspecific competition and facilitate coexistence. The differential use of habitats is one of the behavioural mechanisms that may shape coexistence among marine predators. In this study, we provide a dietary and nutritional assessment of two pinnipeds, the South American sea lion (SASL) and the South American fur seal (SAFS) and explore their sympatric coexistence within the Warm Temperate Southwestern Atlantic biogeographic province (WTSA province). Pelagic prey species within the WTSA province showed significantly higher proportional composition of lipids than demersal counterparts, evidencing a nutritional variability in a vertical dimension accessible to marine predators. By modelling the dietary niches of these pinnipeds through a nutritional lens, we showed high overlapping prey composition niche breadths suggesting that both species consumed prey with similar nutritional composition; however, distinct realized nutritional niches showed that diets are likely shaped by differences in foraging behaviours. The SAFS combined pelagic and demersal prey, whereas SASL mostly preyed upon demersal species. This paper provides crucial information on how nutritional variability in the water column likely drives the feeding strategies of both pinnipeds in the WTSA province. Given that this variation can influence the stability of the contrasting population trends shown by these two pinnipeds, nutritional dynamics must be taken into consideration when defining conservation strategies.
... Knowledge on whether similar mechanisms operate in free-ranging carnivores remains limited (Kohl et al., 2015;Machovsky-Capuska, Coogan, et al., 2016a). Most of the prey sources on which white sharks differentiated their diets were nutritionally distinct. ...
... Mixing models may assist in accurately identifying individual diet specialisations because this approach holistically evaluates how the combined variance, overlap and position of individuals' isotopic niches relates to, and arises from, the isotopic distributions of the resources they consume. 4.2 | From p-space to N-space: Integrating a nutritional dimension of individual diet specialisation Establishing flexibility in the nutritional niche of individuals, populations and species is paramount for understanding physiological bases of animals' responses to variation in their environment (Machovsky-Capuska, Senior, et al., 2016b; Simpson & Raubenheimer, 2012),although this is challenging to achieve in research involving freeranging species, especially cryptic predators (Machovsky-Capuska,Coogan, et al., 2016a). Our integrative framework offers a new solution for this, and similarities between nutritional estimates from SI and stomach contents for white sharks highlights the efficacy of our approach. ...
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Dietary specialisations are important determinants of ecological structure, particularly in species with high per-capita trophic influence like marine apex predators. These species are, however, among the most challenging in which to establish spatiotemporally integrated diets. We introduce a novel integration of stable isotopes with a multidimensional nutritional niche framework that addresses the challenges of establishing spatiotemporally integrated nutritional niches in wild populations, and apply the framework to explore individual diet specialisation in a marine apex predator, the white shark (Carcharodon carcharias). Sequential tooth files were sampled from juvenile white sharks to establish individual isotopic (δ-space; δ13 C, δ15 N, δ34 S) niche specialisation. Bayesian mixing models were then used to reveal individual-level prey (p-space) specialisation, and further combined with nutritional geometry models to quantify the nutritional (N-space) dimensions of individual specialisation, and their relationships to prey use. Isotopic and mixing model analyses indicated juvenile white sharks as individual specialists within a broader, generalist, population niche. Individual sharks differed in their consumption of several important mesopredator species, which suggested among-individual variance in trophic roles in either pelagic or benthic food webs. However, variation in nutrient intakes was small and not consistently correlated with differences in prey use, suggesting white sharks as nutritional specialists and that individuals could use functionally and nutritionally different prey as complementary means to achieve a common nutritional goal. We identify how degrees of individual specialisation can differ between niche spaces (δ-, p- or N-space), the physiological and ecological implications of this, and argue that integrating nutrition can provide stronger, mechanistic links between diet specialisation and its intrinsic (fitness/performance) and extrinsic (ecological) outcomes. Our time-integrated framework is adaptable for examining the nutritional consequences and drivers of food use variation at the individual, population or species level.
... Currently, our understanding of multi-species pandemic is limited due to the complexity of detecting it on time, gathering relevant data, and influencing its course [30,31,32,33]. However, the study of interacting species has gained popularity in the last decades, constantly revealing new insights about the biological dynamics around us and providing a cornerstone for a broad spectrum of technological developments [34,35,36,37]. Indeed, a particular interest is provided to the study of epidemiology to understand the spread of infectious diseases with the goal to determine pandemic intervention policies to possibly eradicate them [38,39,40,41,42,43,44]. ...
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Small and large scale pandemics are a natural phenomenon repeatably appearing throughout history, causing ecological and biological shifts in ecosystems and a wide range of their habitats. These pandemics usually start with a single strain but shortly become multi-strain due to a mutation process of the pathogen causing the epidemic. In this study, we propose a novel eco-epidemiological model that captures multi-species prey-predator dynamics with a multi-strain pandemic. The proposed model extends and combines the Lotka-Volterra prey-predator model and the Susceptible-Infectious-Recovered (SIR) epidemiological model. We investigate the ecosystem's sensitivity and stability during such a multi-strain pandemic through extensive simulation relying on both synthetic cases as well as two real-world configurations. Our results are aligned with known ecological and epidemiological findings, thus supporting the adequacy of the proposed model in realistically capturing the complex eco-epidemiological properties of the multi-species multi-strain pandemic dynamics.
... A key question at the frontier of nutritional ecology is: do carnivores exhibit SCIT 10,25,54,55 ? To our knowledge, our study is the first to test this hypothesis within the context of amino acids. ...
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In nutritional ecology the intake target is the diet that maximises consumer fitness. A key hypothesis of nutritional ecology is that natural selection has acted upon the behavioural and physiological traits of consumers to result in them Selectively Consuming prey to match the Intake Target (SCIT). SCIT has been documented in some herbivores and omnivores, which experience strong heterogeneity in the nutritional quality of available foods. Although carnivores experience a prey community with a much more homogeneous nutrient composition, SCIT by carnivores has nevertheless been deemed highly likely by some researchers. Here we test for SCIT for micronutrients (amino acids) in two freshwater carnivores: the river blackfish and the two-spined blackfish. Although both blackfishes exhibited non-random consumption of prey from the environment, this resulted in non-random consumption of amino acids in only one species, the river blackfish. Non-random consumption of amino acids by river blackfish was not SCIT, but instead an artefact of habitat-specific foraging. We present hypotheses to explain why wild populations of freshwater carnivores may not exhibit SCIT for amino acids. Our work highlights the need for careful, critical tests of the hypotheses and assumptions of nutritional ecology and its application to wild populations.
... The proposed results are consistent with previous studies that also estimated the nutritional niche ranges of common dolphins and gannets (Machovsky-Capuska et al., 2018;Machovsky-Capuska and Raubenheimer, 2020;Machovsky-Capuska et al., 2020b), although additional sampling might, of course, provide further resolution. Regarding the behavioural findings presented here, there are considerable challenges of collecting behavioural data on dynamic predators foraging in the wild (Machovsky-Capuska et al., 2016c;Hughey et al., 2018). The use of a commercial tourism catamaran as a platform of opportunity to collect such behavioural data further adds to the challenge. ...
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Prey detection and subsequent capture is considered a major hypothesis to explain feeding associations between common dolphins and Australasian gannets. However, a current lack of insight on nutritional strategies with respect to foraging behaviours of both species has until now, prevented any detailed understanding of this conspecific relationship. Here we combine stomach content analysis (SCA), nutritional composition of prey, a multidimensional nutritional niche framework (MNNF) and videography to provide a holistic dietary, nutritional, and behavioural assessment of the feeding association between dolphins and gannets in the Hauraki Gulf, New Zealand. Dolphins consumed ten prey species, including grey mullet (Mugil cephalus) as the most representative by wet mass (33.4%). Gannets preyed upon six species, with pilchards (Sardinops pilchardus) contributing most of the diet by wet mass (32.4%) to their diet. Both predators jointly preyed upon pilchard, jack mackerel (Trachurus spp.), arrow squid (genus Nototodarus), and anchovy (Engraulis australis). Accordingly, the MNNF revealed a moderate overlap in the prey composition niche (0.42) and realized nutritional niche (0.52) between dolphins and gannets. This suggests that both predators coexist in a similar nutritional space, while simultaneously reducing interspecific competition and maximizing the success of both encountering and exploiting patchily distributed prey. Behavioural analysis further indicated that dolphin and gannets feeding associations are likely to be mutually beneficial, with a carouselling foraging strategy and larger pod sizes of dolphins, influencing the diving altitude of gannets. Our approach provides a new, more holistic understanding of this iconic foraging relationship, which until now has been poorly understood.
... Nutrients have the potential to influence communities and ecosystems by influencing the processes underpinning ecological interactions, such as foraging behaviour and physiology (Elser et al., 1998;Machovsky-Capuska, Coogan, Simpson, & Raubenheimer, 2016;Machovsky-Capuska et al., 2018;Potter, Stannard, Greenville, & Dickman, 2018). Certainly, the importance of nutrients for interactions has been demonstrated at various scales of organisation; for example, from the interactions between individual social insects up to the collective behaviour of superorganisms (Lihoreau et al., 2015(Lihoreau et al., , 2014(Lihoreau et al., , 2017. ...
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Nutrients are a critical driver of species interactions (e.g., plant-herbivore, predator-prey and host-parasite) but are not yet integrated into network ecology analyses. Ecological concepts like nutrient-specific foraging and nutrient-dependent functional responses could provide a mechanistic context for complex ecological interactions. These concepts in turn offer an opportunity to predict dynamic network processes such as interaction rewiring and extinction cascades. Here, we propose the concept of nutritional networks. By integrating nutritional data into ecological networks, we envisage significant advances to our understanding of ecological dynamics from individuals to ecosystem scales. We summarise the potential influence of nutrients on the structure and complexity of ecological networks, with specific reference to niche partitioning, predator-prey dynamics, spatiotemporal patterns and robustness. Using an empirical example of an inter-specific trophic network, we show that networks can be constructed with nutritional data to illuminate how nutrients may drive ecological interactions in natural systems. Throughout, we identify fundamental ecological hypotheses that can be explored in a nutritional network context and highlight methodological frameworks to facilitate their operationalisation.
... A more detailed understanding of both their post-capture responses and natural behavior across ecological contexts is thus critical to their management and conservation. Furthermore, given the challenges of fine-scale behavioral observations in cryptic predators generally, improved insights into the behavior of any one species could provide valuable information for understanding the behavioral ecology of predators more broadly (Machovsky-Capuska et al., 2016). ...
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Multisensor biologging provides a powerful tool for ecological research, enabling fine-scale observation of animals to directly link physiology and movement to behavior across ecological contexts. However, applied research into behavioral disturbance and recovery following human interventions (e.g., capture and translocation) has mostly relied on coarse location-based tracking or unidimensional approaches (e.g., dive profiles and activity/energetic metrics) that may not resolve behaviors and recovery processes. Biologging can improve insights into both disturbed and natural behavior, which is critical for management and conservation initiatives, although challenges remain in objectively identifying distinct behavioral modes from complex multisensor datasets. Using white sharks ( Carcharodon carcharias ) released from a non-lethal catch-and-release shark bite mitigation program, we explored how combining multisensor biologging (video, depth, accelerometers, gyroscopes, and magnetometers), track reconstruction and behavioral state modeling using hidden Markov models (HMMs) can improve our understanding of behavioral processes and recovery. Biologging tags were deployed on eight white sharks, recording their continuous behaviors, movements, and environmental context (habitat, interactions with other organisms/objects) for periods of 10–87 h post-release. Dive profiles and tailbeat analysis (as a standard, activity-based method for assessing recovery) indicated an immediate “disturbed” period of offshore movement, displaying rapid tailbeats and an average tailbeat-derived recovery period of 9.7 h, with evidence of smaller individuals having longer recoveries. However, further integrating magnetometer-derived headings, track reconstruction and HMM modeling revealed a cryptic shift to diurnal clockwise-counterclockwise circling behavior, which we argue represents compelling new evidence for hypothesized unihemispheric sleep amongst elasmobranchs. By simultaneously providing critical information toward conservation-focused shark management and understudied aspects of shark behavior, our study highlights how integrating multisensor information through HMMs can improve our understanding of both post-release and natural behavior, especially in species that are difficult to observe directly.
... If maintained on fixed diets that differ from the optimal macronutrient composition, individuals change their self-selected preferences in a way that will allow them to redress the nutritional imbalance forced upon them (Mayntz et al., 2005). These findings have created a new view of the foraging strategy of generalist feeders: rather than foraging for particular food types, the essence of their strategy may be to forage for a particular combination of macronutrients (Kohl et al., 2015;Machovsky-Capuska, Coogan, et al., 2016). Thus, generalist feeders may take advantage of their broad-scale acceptance of diverse food types to obtain a diet with a nutritional composition that corresponds to their immediate needs. ...
Description of animals’ trophic niches help us understand interactions between species in biological communities that are not easily observed. Analyses of macronutrient niches, i.e. the range of macronutrient (protein:lipid:carbohydrate) ratios selected by generalist feeders, may be a useful alternative approach to inter‐species comparisons of diets, especially within taxonomic assemblages of predators where species with similar nutritional requirements are likely to accept similar types of prey. Here we analysed the macronutritional niches of a woodland assemblage of seven harvestman species, all supposed to be predators with omnivorous tendencies. Five species (Mitopus morio, Leiobunum gracile, Oligolophus tridens, O. hanseni, Paroligolophus agrestis) were native and two species (Opilio canestrinii, Dicranopalpus ramosus) were recent invaders into the community. We compare the fundamental (FMN) and realized (RMN) macronutritional niche positions of the species using a ‘double‐test procedure’, which provides information on whether the species were food limited in their natural habitat, and whether they were limited by specific macronutrients. All seven species were food limited and six species were non‐protein limited in the field; of these, four species were carbohydrate limited, and in one species females were lipid limited and males carbohydrate limited. These findings add to the notion that predators are mainly non‐protein limited in the field. The FMN positions of the assemblage fell within 46‐50% protein, 29‐38% lipid, and 16‐22% carbohydrate. The amount of carbohydrate in the self‐selected diet combined with carbohydrate limitation confirms that the species are zoophytophagous. Two morphological clusters of species (large long‐legged vs. small short‐legged species) differed not only in microhabitat (upper vs. lower forest strata) but also in macronutrient selection, where large long‐legged species selected higher proportion of carbohydrate than small short‐legged species. Thus, morphologically similar species occupy the same habitat stratum and have similar macronutritional niches. We discuss the hypothesis that the invasive O. canestrinii might have an impact on native species as it allegedly had in urban environments previously. Two basic assumptions about interspecific resource competition were fulfilled, i.e. high overlap of nutritional requirements and limitation by food and macronutrients.
Lactation is a crucial reproductive stage in eutherian mammals and the most energetically and nutritionally expensive part of the life cycle. However, data on lactation parameters in cetaceans remain scattered and incomplete. In this paper, we present novel information on the nutritional compositions and fatty acid (FA) profiles of the milks of two small odontocete species that inhabit coastal waters of the southwest Atlantic Ocean: the endangered Franciscana dolphin (Pontoporia blainvillei) and the Burmeister's porpoise (Phocoena spinipinnis). Milk samples were collected from the fresh carcasses of two lactating females stranded in Argentina. The milk profiles of both species were consistent with existing knowledge on odontocetes worldwide, being characterised by low proportions of carbohydrates and high proportions of proteins and lipids. The FA profiles exhibited a predominance of long-chain FAs (≥18 C), with monounsaturated FAs representing nearly 50% of the total FAs. In both species, the main FAs were 18:1n9 and 16:0, which are common FAs in coastal prey species in this region. These findings are particularly relevant for the conservation of Franciscana dolphins, which are at risk of extinction throughout their distribution range.
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It is widely believed that predators maximise their energy intake while foraging and consume prey that are nutritionally similar. We combined GPS data loggers, miniaturised cameras, dietary sampling and nutritional geometry to examine the nutritional variability in the prey and selected diet, and foraging performance, of the masked booby (Sula dactylatra tasmani), a wild carnivore and marine top predator. Data loggers also revealed no significant differences between sexes in the foraging performance of chick-rearing adults. Females provided more food to their chicks than the males and, regardless of the nutritional variability of prey consumed, both sexes showed similar amounts of protein and lipid in their diets. Miniaturised cameras combined with nutritional analysis of prey provided, for the first time, fine-scale detail of the amounts of macronutrients consumed in each plunge dive and the overall foraging trip. Our methodology could be considered for future studies that aim to contribute to the general understanding of the behavioural and physiological mechanisms and ecological and evolutionary significance of animal foraging (e.g. energy expenditure budgets and prey selection for self- and offspring-feeding that could lead to sex-specific foraging strategies).
Conference Paper
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The foraging and feeding ecology of gray wolves is an essential component to understanding the role that top carnivores play in shaping the structure and function of terrestrial ecosystems. In Yellowstone National Park (YNP), predation studies on a highly visible, reintroduced population of wolves are increasing our understanding of this aspect of wolf ecology. Wolves in YNP feed primarily on elk, despite the presence of other ungulate species. Patterns of prey selection and kill rates in winter have varied seasonally each year from 1995 to 2004 and changed in recent years as the wolf population has become established. Wolves select elk based on their vulnerability as a result of age, sex, and season and therefore kill primarily calves, old cows, and bulls that have been weakened by winter, Summer scat analysis reveals an increased variety in diet compared with observed winter diets, including other ungulate species, rodents, and vegetation. Wolves in YNP hunt in packs and, upon a successful kill, share in the evisceration and consumption of highly nutritious organs first, followed by major muscle tissue, and eventually bone and hide. Wolves are adapted to a feast-or-famine foraging pattern, and YNP packs typically kill and consume an elk every 2-3 d. However, wolves in YNP have gone without fresh meat for several weeks by scavenging off old carcasses that consist mostly of bone and hide. As patterns of wolf density, prey density, weather, and vulnerability of prey change, in comparision with the conditions of the study period described here, we predict that there will also be significant changes in wolf predation patterns and feeding behavior.
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The foraging challenge for predators is to find and capture food with adequate levels of energy and nutrients. Marine predators require particularly sophisticated foraging strategies that enable them to balance self- and offspring-feeding, and also in many circumstances simultaneously consider the nutritional constraints of their partners. Here we combined the use of dietary analysis, proximate composition and nutritional geometry (right-angled mixture triangle nutritional models) to examine the macronutrient preferences of Australasian gannets (Morus serrator) at Farewell Spit gannetry in New Zealand. Our results showed intra- and inter-specific variation in the protein, lipid and water composition of prey captured by our sample of 111 Australasian gannets. In addition, we observed significant differences in the Australasian gannets’ nutritional niche between seasons. We provide evidence of sex-specific macronutrient foraging strategies in a successful marine predator in the wild. We have shown that in spite of fluctuations in the nutritional composition of foods available to Australasian gannets, males consistently capture prey with higher protein-to-lipid ratios and lower lipid-to-water ratios than females. These results aid to better understand the evolutionary relationship between macronutrient selection and sex-specific traits in wild animals. They also suggest an incentive for these predators to combine individually imbalanced but nutritionally complementary foods to achieve dietary balance, further highlighting the likelihood that prey selection is guided by the balance of macronutrients, rather than energy alone.
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Knowledge of carnivore nutritional requirements offers a potentially powerful aid for conservation and management strategies, yet has received little attention. We discuss how nutritional ecology, nutritional geometry, and the concept of macronutrient (protein, lipid, and carbohydrate) balance can be used to further our understanding of behavioral regulatory mechanisms that may influence food-related human–wildlife conflict, focusing on North American grizzly bears (Ursus arctos). We propose that the macronutrient preferences of omnivorous grizzly bears are a strong driver of their conflict with humans due to nutrient-specific foraging behavior, which we predict will be particularly noticeable during periods in which “key” natural foods high in lipid or carbohydrate are limiting. We demonstrate how nutritional geometry can be used to investigate the concept of nutrient balance by integrating recent research on the macronutrient selection of the grizzly bear with nutritional estimates of potentially consumed anthropogenic foods. Our geometric analysis utilizing right-angled mixture triangles suggested that anthropogenic foods offer grizzly bears nonprotein energy sources that may allow them to optimize macronutrient intake. This macronutrient-focused approach gives rise to fundamentally different predictions (and potentially management strategies) than the conventional food and energy-focused approaches. This article also provides insight into food-related conflict among other bear and carnivore species, and human–carnivore conflict more generally, by outlining a nutritionally explicit predictive framework for understanding the potentially volatile interface between anthropogenic environments and the behavior of wild animals.
Knowledge of predator diets, including how diets might change through time or differ among predators, provides essential insights into their ecology. Diet estimation therefore remains an active area of research within quantitative ecology. Quantitative fatty acid signature analysis (QFASA) is an increasingly common method of diet estimation. QFASA is based on a data library of prey signatures, which are vectors of proportions summarizing the fatty acid composition of lipids, and diet is estimated as the mixture of prey signatures that most closely approximates a predator’s signature. Diets are typically estimated using proportions from a subset of all fatty acids that are known to be solely or largely influenced by diet. Given the subset of fatty acids selected, the current practice is to scale their proportions to sum to 1.0. However, scaling signature proportions has the potential to distort the structural relationships within a prey library and between predators and prey. To investigate that possibility, we compared the practice of scaling proportions with two alternatives and found that the traditional scaling can meaningfully bias diet estimators under some conditions. Two aspects of the prey types that contributed to a predator’s diet influenced the magnitude of the bias: the degree to which the sums of unscaled proportions differed among prey types and the identifiability of prey types within the prey library. We caution investigators against the routine scaling of signature proportions in QFASA.
The dietary generalist-specialist distinction plays a pivotal role in theoretical and applied ecology, conservation, invasion biology, and evolution and yet the concept remains poorly characterised. Diets, which are commonly used to define niche breadth, are almost exclusively considered in terms of foods, with little regard for the mixtures of nutrients and other compounds they contain. We use nutritional geometry (NG) to integrate nutrition with food-level approaches to the dietary niche and illustrate the application of our framework in the important context of invasion biology. We use an example that involves a model with four hypothetical nonexclusive scenarios. We additionally show how this approach can provide fresh theoretical insight into the ways nutrition and food choices impact trait evolution and trophic interactions.