Functional Ecology 2009, 23, 4–16
doi: 10.1111/j.1365-2435.2008.01522.x
NUTRITIONAL ECOLOGY
Blackwell Publishing Ltd
Nutrition, ecology and nutritional ecology: toward
an integrated framework
David Raubenheimer1*, Steven J. Simpson2 and David Mayntz3,4
1
Institute of Natural Resources and New Zealand Institute for Advanced Study, Massey University, Albany, New Zealand;
School of Biological Sciences, University of Sydney, Sydney, Australia; 3Department of Ecology and Genetics,
University of Aarhus, Aarhus, Denmark; and 4Department of Zoology, University of Oxford, Oxford, UK
2
Summary
1. The science of nutritional ecology spans a wide range of fields, including ecology, nutrition,
behaviour, morphology, physiology, life history and evolutionary biology. But does nutritional
ecology have a unique theoretical framework and research program and thus qualify as a field of
research in its own right?
2. We suggest that the distinctive feature of nutritional ecology is its integrative nature, and that the
field would benefit from more attention to formalizing a theoretical and quantitative framework for
developing this.
3. Such a framework, we propose, should satisfy three minimal requirements: it should be
nutritionally explicit, organismally explicit, and ecologically explicit.
4. We evaluate against these criteria four existing frameworks (Optimal Foraging Theory, Classical
Insect Nutritional Ecology, the Geometric Framework for nutrition, and Ecological Stoichiometry),
and conclude that each needs development with respect to at least one criterion.
5. We end with an initial attempt at assessing the expansion of our own contribution, the Geometric
Framework, to better satisfy the criterion of ecological explicitness.
Key-words: nutritional models, nutritional ecology, optimal foraging theory, ecological stoichiometry,
geometric framework
Introduction
The range of studies that go by the label ‘nutritional ecology’
encompasses an impressive diversity of taxa, methods,
concepts, interests and goals, spanning, inter alia, behaviour,
morphology, developmental biology, physiology, life history,
ecology and evolution, with emphasis both on function
and on mechanism. Such cross-disciplinary breadth provides broad conceptual and methodological foundations, and
imbues the discipline with wide-ranging relevance. But it
also presents challenges. Foremost among these is that to
progress beyond the status of label and qualify as a field of
research in its own right (Shettleworth 2000), nutritional
ecology needs an identity more distinct than a diffuse confluence of methods and interests united within the general
areas of nutrition and ecology.
What would be the cornerstone of that identity? In our
judgement, the single most distinctive characteristic of
nutritional ecology is its propensity to probe the gaps between
disparate fields, yielding integrative insights that would
*Corresponding author: E-mail: [email protected]
otherwise not be obtained. The hiatus that is most closely
associated with the subject is that between field ecology (e.g.
resource quality and distribution) and animal phenotypes
(e.g. foraging behaviour, functional morphology, digestive
physiology). Progress in bridging this gap has, however, been
piecemeal and incomplete, as is evidenced by growing
concern in the literature for greater integration between the
study of phenotypes and ecology (e.g. Jones & Lawton 1995;
Fryxell & Lundberg 1997; Olff et al. 1999; McGill et al. 2006;
Schmitz 2008). We believe that nutritional ecology would
be better equipped for achieving this integration if more
attention was paid to developing frameworks that systematically define the panoply of salient components in organism–
environment interactions and explicitly model their integration. In other words, frameworks are needed that provide
a scaffold for melding nutrition and ecology into an integrated
nutritional ecology.
The primary aim of this article is to state what we consider
to be the necessary basic properties of such a scheme, and
evaluate in relation to these some frameworks that are
currently in use: Optimal Foraging Theory, Classical Insect
Nutritional Ecology, the Geometric Framework for nutrition,
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
Nutrition, ecology and nutritional ecology 5
and Ecological Stoichiometry. Our survey reveals that all four
approaches have provided local foci of conceptual and/or
methodological cohesion within nutritional ecology, but a
truly integrative framework would involve an expansion or
synthesis of existing frameworks. A second aim of this article
is to address the expansion of our own contribution, the
Geometric Framework, to questions of community ecology.
Nutritional ecology: components and
interactions
The core components of a general conceptual framework
for nutritional ecology are set out in Fig. 1. Most generally,
these are the organism, the ecological environment, and
the nutritional basis of the interaction between organism
and environment – and here we use ‘nutritional’ in the broad
sense of any property of a food that affects the animal
(Westoby 1974).
We believe that the representation of these three components and interactions should be explicit, in the sense that
the framework can enable research to be structured so as
directly to address questions pertaining to each. In general,
this means that the components should be represented in
models as parameters or, preferably, variables, but not as constants. Thus, models that treat foods as unitary resources, that
is, do not discriminate among their constituents (Raubenheimer
& Simpson 1995), or assume a priori that a single component
(e.g. energy) is pre-eminent, are not nutritionally explicit,
as they cannot partition the actual roles of specific food
components in nutritional ecology interactions (see also
Boggs 2009). Furthermore, links among the components
(depicted by arrows in Fig. 1) should be bidirectional, thus
enabling the research to be structured in a way that addresses
causal effects in either direction or in both simultaneously
(i.e. reciprocal causality – e.g. Cardinale et al. 2006).
We further note that the arrows connecting elements in Fig. 1
are but a sub-set of a more complex network of biologically
relevant interactions that are potentially of interest to nutritional
ecology studies. There has, for example, been a recent intensification of interest in the question of how community
processes and patterns influence evolution (Johnson &
Stinchcombe 2007). If the context of such a study was nutritionally explicit, then it would warrant an arrow linking
organism ‘function’ directly with ‘community’, or the interaction might possibly involve ‘history’ (e.g. if phylogeography
were an important component). A useful term for such a
network in which elements can be viewed as interacting
with other elements that occur in two or more components
(e.g. ‘function’ vs. ‘community’ and/or ‘history’) is ‘heterarchical’ (Gunji & Kamiura 2004).
In the remainder of this section we briefly expand on the
role of the organism, the environment and nutrition in the
scheme.
THE ORGANISM
Fig. 1. Conceptual scheme depicting the components of an integrative
framework for nutritional ecology. The organism is considered from
the viewpoints of function, mechanism, development and history
(Tinbergen 1963), while the environment is partitioned into biotic
and abiotic components. The nutritional interactions that take place
between organism and environment involve both the effects of the
environment on phenotypes (downward arrows) and the impact of
phenotypes on the ecological environment (upward arrows). A
nutritional ecology framework should also cope with horizontal
interactions (dashed arrows), for example between biotic and abiotic
components of the environment, or between mechanistic and
functional aspects of phenotypes.
The organism is central in nutritional ecology. As is true in
many other areas in organismal biology, nutritional ecology
can trace important influences to the classical ethological
movement of 1930–1960’s. Ethology, too, is a fundamentally
integrative science, in two respects that are relevant to our
discussion here. First is the emphasis in ethology on understanding animal phenotypes in relation to their ecological
environment, which has likewise historically been associated
with the emergence of the term ‘nutritional ecology’ (e.g.
Schneider 1967; Stanley Price 1978) and has continued to be
central to the identity of the field. Second, ethology’s ‘manifesto’,
as famously articulated by Niko Tinbergen (1963), is based on
an integrative approach which urges animal behaviourists
to combine in their thinking about behaviour four levels
of analysis: its mechanisms, development, function and
evolutionary history.
Tinbergen’s framework remains hugely influential and in
our opinion could make a valuable contribution to integration
in nutritional ecology. Specifically, it provides a more refined
depiction of the organism in nutritional ecology research,
through explicitly distinguishing the links between nutritional
environments on the one hand, and on the other mechanistic,
developmental, functional and phylogenetic aspects of
phenotypes (Fig. 1). The Tinbergen scheme was developed
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
6
D. Raubenheimer et al.
and is most frequently applied in the context of behaviour,
but in nutritional ecology it would apply to all aspects of
the phenotype, including physiology, morphology and life
history.
THE ENVIRONMENT AND ORGANISM-ENVIRONMENT
INTERACTIONS
The component of the environment that is usually at the
centre of nutritional ecology studies is food, but other biotic
(e.g. predators and parasites) and abiotic (e.g. temperature,
photoperiod) factors are, of course, also relevant (Slansky &
Rodriguez 1987) and might even be pre-eminent (Schmitz
2008). Being focused primarily on the organism, nutritional
ecology studies most commonly emphasise the downward
arrows in Fig. 1, the ways that organisms respond to the
ecological environment at various time-scales: behavioural
and physiological responses, phenotypic plasticity (e.g. in
oral and gut morphology), development and life history (e.g.
age at maturity), and adaptation on an evolutionary time-scale.
However, as noted above, some authors have also used
‘nutritional ecology’ for studies that proceed in the opposite
direction, addressing questions of how phenotypes impact on
population – (e.g. Simpson et al. 2006) and community-level
processes, or the reciprocal impacts of communities and
phenotypes (e.g. Schmitz 2008). We believe that nutritional
ecology is well-placed to make a substantial contribution
to the question of how phenotypes impact on ecological
communities, particularly through dialogue with other foci of
integration within population, community and ecosystems
ecology (S.J. Simpson et al., under review).
NUTRITION
A framework that is nutritionally explicit enables the
questions to be addressed: ‘which nutrient(s) and other food
components are important to an animal in a given situation?’,
‘how does each of these influence the animal’s (e.g. homeostatic)
responses?’, and ‘what are the performance and ecological
consequences for the animal of responding in the way that it
does?’
There exists surprisingly little information on how
specific nutrients influence the homeostatic and performance
responses of animals, and even less on how these influences
in turn impact on populations and communities. The reason
for this is that studies are most frequently conducted within
frameworks that do not systematically disentangle the roles
of specific food components, or else a priori identify one
component (usually nitrogen, energy or plant secondary
compounds) as paramount and code this as an input to the
study rather than an experimental outcome. Even where
the focal component is correctly identified, an important
part of the story might be overlooked in this approach. This
is because foods are complex mixtures, and the impact of
specific components is usually contingent on and/or exerted
through other components. For example, in many animals the
ingestive regulatory systems weight protein more strongly
than other nutrients, with the consequence that they overingest other nutrients when eating low-protein foods – the
‘protein leverage’ effect (Simpson & Raubenheimer 2005).
In such cases, protein would correctly be identified as the
pre-eminent nutrient, and yet the major constraint on protein
gain might be the inability of the animal to ingest large
excesses of some other food component(s), and the major
health impact due to the excess of these components that
they do ingested (Raubenheimer, Lee & Simpson 2005;
Boggs 2009).
We consider it a high priority in nutritional ecology to
adopt nutritionally explicit frameworks which systematically
identify the individual and interactive roles of different food
components.
Frameworks in nutritional ecology
In this section we evaluate against the criteria set above some
of the frameworks currently in use in nutritional ecology.
We cannot hope to do justice within the space constraints to
the diversity of modelling approaches that have been applied
to specific questions in nutritional ecology, and our coverage
is therefore restricted to four frameworks that we consider
to be particularly relevant to the question of integration:
Optimal Foraging Theory, Classical Insect Nutritional Ecology,
the Geometric Framework for nutrition, and ecological
stoichiometry. We believe, however, that the main points our
survey illustrates are robust to the inclusion of any framework
in use in nutritional ecology.
OPTIMAL FORAGING THEORY
Optimal Foraging Theory (OFT) is an evolutionarily-inspired
framework that aims to ‘explain and predict’ (Pyke et al. 1977)
the patterns of food choice and foraging by animals. It is
based on the premise that foraging can be viewed as a process
that has been optimized by natural selection to maximize
fitness, and thus optimization mathematics is an appropriate
tool for developing foraging models (Maynard Smith 1978).
Typically, the focal variable is not fitness itself, but a ‘currency’
assumed to be a proxy for fitness, such as rate of energy gain
(maximized) or predation risk (minimized). Although the
optimality approach is used most frequently to model
behavioural aspects of foraging, it has also been applied to
physiological aspects such as food processing times and
digestion efficiencies (e.g. Raubenheimer & Simpson 1998).
OFT is concerned primarily with the effects of the environment on the phenotypes of animals (i.e. the downward arrows
in Fig. 1), but it has also been applied in the reverse direction,
exploring how the functional characteristics of organisms
influence ecological communities (e.g. Belovsky 1986; Petchey
et al. 2008). OFT is, therefore, clearly a framework for studying
the nutritional relations between animals and their environments, and for this reason is relevant to our consideration of
nutritional ecology. The key question, however, is the extent
to which in its current form OFT is sufficiently nutritionally
explicit to carry out the nutritional ecology agenda.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
Nutrition, ecology and nutritional ecology 7
Where the currency in OFT models is nutritional (as
opposed to, e.g. time minimization or survival maximization),
it usually involves energy, although other nutritional currencies
are occasionally involved (e.g. protein – Berteaux et al. 1998).
In this respect, OFT is a uni-dimensional approach which
assumes a priori that a single food component is limiting
to the animal, and elevates that component to the status of
currency. Other food components, such as toxins and
nutrients that are not represented as currency, are coded as
constraints within which the animal has to work in its
attempts to achieve the postulated foraging goal (e.g. Westoby
1974; Pulliam 1975; Belovsky 1990; Hirakawa 1995). This is
often, but not always (e.g. Pulliam 1975), done using linear
programming (Westoby 1974; Belovsky 1990).
OFT has clearly experienced many successes (Stephens
et al. 2006), but an improved understanding of nutritional
processes is not among them. We believe the reason for this to
be that OFT does not comfortably fulfil the criterion of a
nutritionally explicit framework. First, while it might arguably
be true that at any one time an animal is limited by a single
nutrient (the currency), it is an open and important question
as to the dynamics and time-scale of such limitation. At the
one extreme, single-nutrient limitation might be a perpetual
feature of an animal’s nutritional ecology, as is proposed by
White (1983) to be generally the case for nitrogen in many
ecosystems. At the other extreme, for an animal that switches
between food types frequently, the limiting component(s)
might change daily, hourly, or even within a single meal
(Chambers et al. 1995). Second, energy is itself not a nutrient
but a property of the macronutrient groups protein, lipid and
carbohydrate. Without explicitly distinguishing among these
energetic components, caloric measures present the risk that
foraging aimed at maximizing one or more of these macronutrients, or optimizing their balance, is confounded with
energy maximization. Finally, it is often difficult, impossible,
or meaningless to distinguish between ‘constraint’ and
‘adaptive strategy’. We therefore consider it a better heuristic
to view nutritional processes as a ‘network of interconnected
trade-offs with a global optimum’ (Illius, Tolkamp & Yearsley
2002).
Nonetheless, in addition to its successes in furthering the
understanding of animal decision making, the optimalitybased approach to foraging has made a substantial contribution
to the development of nutritional ecology. It set the bar for
conceptual and quantitative rigour in the study of foraging,
and provided a foundation which is increasingly becoming
integrated with other approaches in the study of nutritional
ecology (Simpson et al. 2004; Newman 2006). Additionally,
in its earlier formulations, OFT provided a point of contrast
against which other approaches could develop. In the present
context, the most relevant of these is Classical Insect
Nutritional Ecology.
CLASSICAL INSECT NUTRITIONAL ECOLOGY
The development of what we refer to as ‘Classical Insect
Nutritional Ecology’ (CINE) was seeded by the convergence
in the 1950’s and 1960’s of several strands of research which
shared a common interest in the factors that govern food
selection by animals. Notable among these was the work of
Reginald Painter (e.g. 1936), who developed the view that
variation in the nutrient composition of plants is central to
the patterns of food choice and performance responses
by phytophagous insects. A second line of interest, more
closely associated with the field of plant–animal co-evolution,
asserted that food selection in phytophagous insects is driven
not by nutrients, but by plant secondary compounds (e.g.
Fraenkel 1959). These discussions took place in a climate
of growing interest among ecologists in the extent to which
the nutritional quality of plant tissues limits herbivore
populations (Schmitz 2008).
Against this background, there was clearly a need in the
study of animal foraging for a paradigm that approached
more directly than did OFT the question of which currencies actually drive the foraging decisions and population
responses of animals (Mitchell 1981; Waldbauer & Friedman
1991) – i.e. for an approach that was nutritionally explicit. The
requisite paradigm was adopted from the experimental
psychology literature, where it had been shown in the work
of Curt Richter and others that rats can self-select from a
range of nutritionally incomplete foods a diet that sustains
good performance, and can alter their patterns of food
selection to compensate for surgically-induced nutritional
perturbations (Galef 1991). The dietary self-selection
paradigm was introduced to CINE by Gil Waldbauer and
colleagues (e.g. Waldbauer et al. 1984). It has since been
demonstrated using this approach that dietary self-selection
is ubiquitous among animals. Some combination of the
macronutrient groups protein, carbohydrate and fats are
regulated independently by many (if not most) animals, and
so too are particular vitamins (Markison 2001), amino acids
(Markison et al. 2000; Yamamoto et al. 2000), mineral salts
(Denton et al. 1993) and the macromineral calcium (Tordoff
2001) regulated by some. These data underscore the importance for nutritional ecology of adopting a framework that is
nutritionally explicit.
Gil Waldbauer also made another highly influential contribution to CINE, in introducing a quantitative framework
for representing the nutritional responses of animals to their
foods (Waldbauer 1968). Waldbauer’s ‘quantitative nutrition’
is a budgetary approach, which expresses the relationships
between food intake and utilization as rates and efficiencies
that can be used comparatively – for example, to compare
growth in insects that have different consumption rates. The
proposed ratio-based nutritional indices – relative consumption
rate (RCR), approximate digestibility (AD), efficiency of
conversion of ingested food (ECI), and efficiency of conversion of digested food (ECD) – rapidly became the industry
standard within CINE (e.g. Scriber & Slansky 1981).
By the late 1980s the field had matured to the point where
Slansky & Rodriguez (1987) could propose a general conceptual
framework for research in CINE. Their recommended framework would involve: (i) determining the performance of an
animal in circumstances (relating to nutrition, as well as its
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
8
D. Raubenheimer et al.
interactions with other factors such as temperature and predation) which maximize fitness; (ii) determining how realistic
changes in these circumstances influence the animal’s
performance, its compensatory responses for ameliorating the
impacts on performance, and the trade-offs that it encounters
in responding to the altered circumstances; (iii) performing
comparative studies, in which the patterns in (i) and (ii) are
related more generally to phylogeny, development and ecology.
Slansky and Rodriguez recommended Waldbauer’s (1968)
ratio-based indices as a quantitative approach for carrying
out this agenda.
In our view, the major general contribution of CINE was to
recognize explicitly the fact that the nutritional ecology of
animals is complex, involving interactions among numerous
environmental factors (e.g. nutrient and non-nutrient food
components, temperature etc.) and animal responses (e.g.
foraging, feeding, food utilization, growth). The Slansky–
Rodriguez manifesto constituted an elegant approach to
conceptualizing the issue, but CINE did not produce a framework that was up to the task of quantifying and interpreting
these multifarious interactions. The paradigm of dietary
self-selection provided a means to demonstrate cases where
animals feed non-randomly on foods differing in composition, and to identify the nutrients that are involved in the
patterns of food selection. It could not, however, deal with the
critical interactive effects of these nutrients on the patterns of
food selection and post-ingestive and performance responses.
Similarly, in introducing terms representing key homeostatic
processes (intake, nutrient assimilation, growth and excretion),
Waldbauer’s quantitative budgetary approach emphasized
the active role of the organism in nutritional ecology, but
fell short as regards integration. Ostensibly, the nutritional
indices that he proposed did represent an integration of different
homeostatic responses, because each index includes two or
more of the critical regulatory variables. However, compounding
several variables into a single index usually does not reveal the
relationships among them, but obscures these relationships
(Raubenheimer & Simpson 1992). To be sure, Waldbauer’s
aim in recommending these indices was not integration,
but standardization: they enabled responses (e.g. growth) to
be compared across animals that differed in other relevant
aspects (e.g. consumption). Unbeknownst to Waldbauer,
however, a literature was subsequently to emerge demonstrating that there are statistical problems with the use of
ratios for standardizing variables in this way (see Raubenheimer & Simpson 1992 and citations therein). Some explicit
attempts at integration have been made by plotting ratio
indices against each other (e.g. Scriber & Slansky 1981;
Beaupre et al. 1993), but this too can lead to serious statistical
and interpretative problems (Raubenheimer 1995; Brett 2004).
THE GEOMETRIC FRAMEWORK
To address the challenge of integration, we have developed a
graphical approach, the Geometric Framework (GF), which
models the key relationships among relevant variables in
nutritional ecology (Raubenheimer & Simpson 1993, 1994,
1997; Simpson & Raubenheimer 1993, 1995, 1999). GF is
based on the logic of state–space geometry, where relevant
variables are expressed and related to each other within a
geometric space defined by two or more relevant food
components. The variables represented within this space
might include one or more foods, the organism’s current and
optimal nutritional states, the impact on its nutritional state
of eating each food, its body composition, the efficiency of
nutrient utilization, the rates of excretion, and whatever
performance consequences might be of interest. A model so
constructed can be used to conceptualize problems that involve
two or more food components, and to design and interpret
experiments for resolving these problems. GF has been applied
to a range of biological questions involving diverse taxa (see
Table S1, in electronic Supporting Information).
As shown in Fig. 2a–c, the main components of the
Waldbauer nutritional indices (e.g. intake, growth, nutrient
utilization) are represented within GF models, as is dietary
self-selection (Fig. 2d). The handling of these issues is,
however, very different under GF. First, graphical models
enable the interactions among the model components to be
visualized, rather than subsumed within nutritional ratios.
Second, representation of two or more food components
within a model enables their interactive effects to be quantified.
A third point of difference, and one on which we would like
to briefly elaborate, is that in common with OFT – and in
the spirit of the Slansky–Rodriguez manifesto – GF models
explicitly incorporate the notion of functional optima.
This is done by distinguishing estimates of optimal values
for nutrient intake and utilization (e.g. the Intake Target,
Nutrient Target and Growth Target) from realized values.
The inclusion of functional targets in a model enables
nutrient budgets to be constructed that are based on functional
classification of components, rather than a methodological
classification as is standard in CINE (Raubenheimer &
Simpson 1995). In a methodological classification:
I=R+D
eqn 1
where I is ingested nutrient, R that which is retained by the
organism (i.e. reflected in body composition) and D is dissociated
(i.e. not retained). In a functional classification both terms on
the right hand side of eqn 1 are partitioned into the components
that contribute to fitness and those that do not:
I = R(u) + R(w) + D(u) +D(w)
eqn 2
where R(u) and R(w) are, respectively, components that are
retained beneficially (utilized for fitness gains) and nonbeneficially (e.g. surplus lipid storage in obesity), and
similarly D(u) and D(w) represent dissociated nutrient that
is utilized (e.g. energy metabolism, defensive secretions) and
wasted (excreted in the faeces, urine or via diet-induced
thermogenesis – Zanotto et al. 1997).
One advantage of distinguishing functional components
in this way is that it greatly increases the predictive power
of models, because homeostatic regulatory systems will tend
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
Nutrition, ecology and nutritional ecology 9
Fig. 2. Hypothetical two-dimensional geometric models showing four budgetary scenarios. (a) Balanced diet. The intake target (IT) is the
amount and balance of the two nutrients the animal needs to eat within the stipulated period to achieve maximal fitness, and the line originating
at the origin is a nutritional rail representing the carbohydrate : protein balance of food Fa. Since the nutritional rail intersects IT, the animal
is able to match its intake (Io) to its optimal requirements by eating this food – that is, it is a nutritionally balanced diet. The growth target (GT)
shows the optimal amount of ingested protein (R(u)p) and carbohydrate (R(u)c) that should be utilized for ‘growth’ (i.e. retained in the body),
while the nutrient target (NT) describes the amount of nutrient that should be ingested to optimally satisfy nutrient requirements for all fitnessenhancing functions, including components that are retained in the body (GT) and utilized for purposes that involve their dissociation (loss)
from the body (e.g. respiration, useful secretions etc. – collectively represented by D(u)p and D(u)c). For an animal that is 100% efficient at
converting ingested nutrient to functional gain, NT = IT. However, to the extent that there is a degree of constrained inefficiency in nutrient
utilization, optimal intake needs to be over-specified by D(w)p and D(w)c. In the case modelled, NT is shaped as an asymmetrical ellipse oriented
along a gradient of approximately –1, this shape reflecting the underlying cost structure for the investment of ingested nutrients (Simpson et al.
2004). Such an ellipse might, for example, reflect the fact that protein and carbohydrate are to some extent interchangeable (e.g. as sources of
energy), and therefore optimal utilization requirements can be met using any combination of the two nutrients whose coordinate falls on this
ellipse (further illustrated in graph b). By definition, if optimal intake is achieved (i.e. Io = IT), then observed overall utilization (Nuo) will fall
on NT and observed growth (Go) will equal GT. (b). Constrained intake, with nutrient inter-conversion: Model where the animal has available
only nutritionally imbalanced food Fb, which contains surplus protein relative to carbohydrate, and therefore cannot reach IT but must choose
between intake scenarios [I1] (satisfies requirement for protein, but suffers a deficit of carbohydrate), [I2] (gains required level of carbohydrate,
but surplus protein) and [I3] (moderate protein surplus and carbohydrate deficit). It can, however, ameliorate the impact of the ingestive
constraint by judiciously allocating the ingested surplus and/or deficit among budgetary components. For reference, budgetary allocations
where Io = IT (i.e. from model a) are shown by the length of the grey lines, while constrained allocations are shown by the length of the black
arrows. In the case modelled, the animal has regulated intake to [I3] and has thus ingested both a surplus of protein and a deficit of
carbohydrate. Assuming that D(w)c has a fixed lower limit (i.e. where Io = IT utilization efficiency of carbohydrate is at a maximum), the
intake deficit of carbohydrate must be absorbed by R(u)c and/or D(u)c. In this case growth is defended (R(u)c and R(u)p are unchanged), but
carbohydrate allocated to fuel energy metabolism (D(u)c) is reduced. However, a portion of the surplus ingested protein (extended portion
of the arrow representing D(u)p) is deaminated and channelled into energy metabolism, thus compensating for the reduction in D(u)c.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
10
D. Raubenheimer et al.
towards behavioural and physiological responses that produce
functionally favourable outcomes (e.g. Simpson et al. 2004).
As illustrated in Fig. 2, it also greatly increases the analytical
power of a model.
In terms of our stated criteria for models of nutritional
ecology, GF clearly is nutritionally explicit, being designed to
disentangle the individual and interactive effects on animals
of various food components. It is also organismally explicit,
being capable of addressing questions concerning the
relationships of nutrition across Tinbergen’s four categories –
function, mechanism, ontogeny and phylogeny (Simpson &
Raubenheimer 1993). GF is, at least partly, ecologically explicit,
as it is designed with the fundamental goal of examining the
ways that the nutritional environments of animals impact on
phenotypes (downwards arrows in Fig. 1). Less well-developed,
however, is its application to questions of how the phenotypes
of animals impact on their ecological environments (upwards
arrows in Fig. 1). We return below to the prospects for GF
of modelling such questions, and thus qualifying as ecologically explicit sensu stricto.
ECOLOGICAL STOICHIOMETRY
Ecological Stoichiometry (ES) is ‘the study of the balance of
energy and multiple chemical elements in ecological interactions’ (Elser 2006). As suggested by this definition, there are
some interesting parallels between ES and GF (Raubenheimer
& Simpson 2004). Like GF, ES grew out of the realization
that there are complexities to biological systems that cannot
be captured using models based on energy alone (Reiners
1986), and therefore frameworks are needed that model the
interactions among multiple currencies – that is, both GF and
ES are nutritionally explicit, multi-currency frameworks. Also
like GF, ES is fundamentally integrative, overtly aiming at
interrelating causes and effects across multiple biological levels
from ‘molecules to ecosystems’ (Sterner & Elser 2002). A third
similarity between the two approaches relates to the core tenet
of ES, the mass balance equation, which applies the laws of
conservation of matter to trophic exchanges in ecosystems.
Although couched in the terminology of chemistry (‘stoichiometry’), mass balance equations are essentially equivalent to
the nutrient budgets developed in CINE and modelled in GF
(Raubenheimer & Simpson 2004). Such parallels have lead
some to consider ES ‘the most recent outgrowth’ of nutritional
ecology (Schmitz 2008), while McGill et al. (2006) consider
both approaches to be examples of the kind of ‘studies in
functional ecology that community ecologists would benefit
from incorporating into their thinking’.
There are also fundamental differences between ES and
GF. An important point of distinction relates to the terra
firma of the two approaches. As detailed above, GF was
developed as a multi-currency nutritionally explicit approach
to modelling nutritional phenotypes, and the question of
how well-suited it is for extension to modelling ecosystem
processes remains open (more on which below). By contrast,
the fundamental inspiration in ES relates to the flow of matter
and energy through ecosystems (Reiners 1986). Organisms
are, of course, a component of ES models – indeed, are central
to these models, as they constitute the primary conduits for
the flow of energy and matter through ecosystems. But the
generality needed for modelling the effects of interactions
among organisms on ecosystem processes has been bought by
ES at the cost of simplifying aspects of phenotypes which are
central to more-organism-focused approaches. An important
question that arises in the present context is how these
simplifications impact on the extent to which ES models
can be considered sufficiently organismally explicit to qualify
as a general framework for nutritional ecology.
Organisms are represented in ES models primarily through
their body composition, usually expressed as the ratio of key
elements – nitrogen (N), phosphorus (P) and/or carbon (C).
Central to predictions of ES are comparisons of the elemental
composition of consumers and their resources. In accordance
with the law of mass balance, a consumer can maintain its
elemental composition only by feeding on foods with similar
elemental composition or by specifically increasing the rates
at which surplus elements are excreted – i.e. by decreasing
their ‘gross growth efficiency’ (GGE) for the surplus elements.
In the simplest scenario, the optimal food – considered to be
that which supports maximal production while minimizing
wastage (Anderson et al. 2005) – would have identical elemental
composition to the body of the consumer, but biological
As a result, overall macronutrient utilization is defended (Nuo coincides with NT), and any fitness costs due to Io not coinciding with IT must
be attributed to other factors such as the need to excrete surplus protein (increased D(w)p). (c) Constrained intake without nutrient
interconversion: An alternative response to constrained intake I3. IT, NT and GT are in the same positions as in panels a. and b. However, in
this case the animal is taken to be incapable of deaminating amino acids for use in energy metabolism, and as a result IT, NT and GT are more
localized than in the previous examples. The ingested deficit of carbohydrate, combined with inability to reduce D(w)c, results in failure to meet
the nutrient target – Nuo is displaced from NT in the carbohydrate dimension. The animal prioritizes the allocation of ingested carbohydrate
to energy metabolism (maintains D(u)c), and as a consequence suffers reduced carbohydrate-derived growth (R(u)c). Furthermore, to
maintain proportional body composition the level of protein allocated to growth (R(u)p) is reduced, resulting in Nuo being displaced from NT
also in the protein dimension. By definition the displacement of Nuo from NT incurs fitness costs, and additionally the animal has an
increased burden of surplus ingested protein to excrete (increased D(w)p). (d) Nutritionally complementary foods: Here the animal has
available two nutritionally imbalanced foods, Fb and Fc. However, since the protein-carbohydrate nutritional rails for these foods fall on
opposite sides of IT, the animal can nonetheless reach IT (and hence NT and GT, not shown) by mixing its intake from the two foods (i.e. these
are nutritionally complementary with respect to protein and carbohydrate). One possible intake trajectory is shown by the arrows, in which the
animal takes meal m1 from Fb, then for meal m2 switches foods and so takes the trajectory defined by Fc, before returning to Fb for m3 and
so on. Other patterns might include frequent switches within meals, or several consecutive meals on one food followed by several on the other.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
Nutrition, ecology and nutritional ecology 11
Fig. 3. Comparison of ingestive regulation of macronutrients and elements. The data, taken from Chambers et al. (1995), represent selected
intake points by fifth stadium locusts, Locusta migratoria . Each point represents the mean selected intake over 6 days of locusts fed one of four
food pairings. The food pairings were (%protein : %carbohydrate): 14 : 28 + 14 : 7; 14 : 28 + 28 : 14; 7 : 14 + 14 : 7; or 7 : 14 + 28 : 14. Since the
animals given each pairing had to distribute their feeding between the foods in very different ways to reach the same point of intake, clustered
intake points represent homeostatic regulation. Such regulation is revealed when the data are plotted in terms of macronutrient intake (a), but
not in terms of elements (b).
constraints on the efficiency with which elements can be
converted to body tissue (i.e. GGE < 1) preclude this. Therefore, foods should be weighted for the maximal GGE of each
element, such that the optimal food is defined as follows
(Anderson et al. 2005):
ideal food x : y = consumer x : y (max GGE.x /max GGE.y)
eqn 3
where x and y are two elements, and max GGE.x and
max GGE.y are the maximum efficiency with which the
consumer can convert x and y, respectively, to body tissue. If
the above equality does not apply, growth and reproduction
of the organism are limited by the element in short supply,
and/or by the cost incurred in excreting the excessive element
(Anderson et al. 2005; Boersma & Elser 2006).
An important simplification in this approach is the
adoption of elements as the chosen currency. ES studies
have occasionally focused on biochemicals (Anderson 1994;
Anderson & Pond 2000; Anderson et al. 2004), but the vast
majority involve elements – indeed, ES has been defined as the
‘biology of elements’ (Sterner & Elser 2002). Elements have
the advantages for ecological studies that they are easy to
measure, constitute a common denominator relevant to all
organisms in ecological communities, and provide a link to
inorganic fluxes within ecosystems. They have the disadvantage, however, that in terms both of function and mechanism,
heterotrophs relate to their nutritional environments not via
the C, N and P, but via heterogeneous molecular complexes of
which these elements are components. Elemental analysis will
thus predict the food choices, post-ingestive responses and
functional consequences for a foraging animal only to the
extent that they approximate the nutritional value of the
foods. In some cases such correlations likely do apply – for
example, the nitrogen content of foods has been successfully
used in many studies as a proxy for its protein and amino acid
content – but even here there might be complexities due to the
presence of other nitrogenous compounds and the fact that
proteins vary in their nitrogen content (e.g. Lourenco et al.
2002). In other contexts, however, the approximation breaks
down, because two or more functionally distinct molecular
complexes can yield similar elemental composition. Where
this is the case, element-based analyses can fall short in
predicting the responses of organisms.
For example, the fractional contribution of different
carbohydrates to foods has profoundly different nutritional
implications for herbivores, but is indistinguishable within
standard ES models (e.g. see Anderson et al. 2004). An
illustration is provided in Fig. 3, which shows data from an
experiment using synthetic foods to investigate nutritional
regulation in locusts (Locusta migratoria). Nutritional
analysis reveals tight, target-like, homeostatic regulation of
the balance and amounts of nutrients eaten, a phenomenon
that profoundly influences foraging choices (see for example
Raubenheimer & Jones 2006). Elemental analysis of the same
data suggested regulation of nitrogen intake, as might be
expected because protein was the only source of nitrogen in
the diet and nitrogen intake thus provided a perfect proxy for
protein intake. There is, by contrast, no apparent regulation
of carbon, and elemental analysis would thus fail to detect a
powerful predictor of food choice and feeding behaviour. The
reason for the different results is that nutritional analysis
reflects the animals’ ingestive responses in distinguishing
between non-nutritional (indigestible) cellulose and nutritional
sources of carbon (in this case principally sucrose, dextrin,
and amino acids), whereas elemental analysis confounds
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
12
D. Raubenheimer et al.
carbon derived from these different sources. Different carbon
sources likewise have different post-ingestive consequences.
Thus virtually no carbon from ingested cellulose is retained
by locusts (i.e. GGE = 0), but surplus carbon derived from
digestible carbohydrates is associated with increased body fat
(GGE > 0) (Raubenheimer & Simpson 1997). The broader
implication is that in order to derive the GGE for carbon, and
hence to evaluate the ‘suitability’ of the food in relation to the
consumer’s body composition (see above), separate GGE’s for
different carbon sources would be needed (Anderson et al. 2004).
These could only be derived using biochemical analyses.
A second important simplification usually adopted in ES
models is the emphasis on proportional body composition as
a metric against which to evaluate diet quality. For ecological
studies this is convenient, as body composition can readily be
measured and compared across diverse organisms. But its
utility in the context of organismal biology is limited, because
this approach is based on a methodological classification of
budgetary terms (eqn 1) and neglects fitness-enhancing
components of the ingesta that are dissociated (eqn 2 and
Fig. 2). For example, ingested carbon that is used to fuel
life-supporting energy metabolism is coded only implicitly,
as a constraint on max GGEcarbon, and not represented as a
fitness-relevant component of the ingesta in its own right
(Fig. 2). Neither is it distinguished from other categories of
carbon that are dissociated at max GGE, which might not
contribute to fitness and should thus correctly be classified as
wasted rather than utilized carbon (D(w) rather than D(u)
in eqn 2) (but see Anderson et al. 2004; Hessen & Anderson
2008). Emphasis on proportional body composition also
neglects a fundamental component of fitness (e.g. Kingsolver
& Huey 2008), body size. Thus, standard ES models would
not distinguish an animal that achieved optimal growth (e.g.
Go in Fig. 2b) from an animal that had the same proportional
body composition but was overall smaller (Go in Fig. 2c).
Finally, emphasis on proportional body composition could
lead to the mistaken conclusion that an animal with depleted
fat stores (and hence lower body C : N) requires a lower
proportion of fat in its diet than does a member of the same
species that is in better condition.
For many ecological applications the element and the
body composition simplifications might be amply justified,
because they enable ecological analyses to be performed
where measurements of macronutrients and functional
components other than body composition might not be
feasible. Their success, however, depends on the extent to
which these proxy measures represent the causal variables
(biochemistry and overall fitness, including both retained and
dissociated components) and in many contexts this will not be
the case. We suspect, in particular, that analyses involving
carbon will be less reliable than those involving nitrogen and
phosphorus. This is partly because, as noted above, carbon is
a major dietary component that is spread across several
functionally distinct biochemicals (e.g. cellulose, starch,
sugars, lipids, amino acids). Furthermore, fuel for energy
metabolism comprises a substantial component of ingested
carbon which contributes critically to fitness but is not
retained in the body (i.e. falls into D(u)), and in standard
stoichiometric equations will not be distinguished from
wasted carbon. Finally, the functional implications of excess
dietary C are substantially more complex than is implied by
simple comparisons of the composition of consumers and
their resources (Hessen & Anderson 2008).
A promising development of the stoichiometric approach
is dynamic energy budget (DEB) theory (Kooijman 2000).
DEB models use differential equations to describe the rates at
which individual organisms assimilate and utilize energy from
food for maintenance, growth, reproduction and development,
while taking into account constraints on the fluxes of elements.
A fundamental construct within DEB theory is the ‘synthesizing
unit’ (SU), which is a generalization of the classical enzyme
concept to complex reactions involving more than one
potentially limiting substrate. SU kinetics is used in DEB
to model the process whereby ingested substrates are transformed into ‘reserves’ that are in turn transformed for growth
and metabolic functions (i.e. ‘assimilation’).
DEB thus provides a more fully-specified characterization
of the organism than does traditional ES, founded on fundamental physicochemical principles. These principles provide
a powerful means for integrating sub-cellular, organismal and
ecological processes. It remains to be seen, however, whether
the DEB abstraction of organisms is sufficiently versatile to
provide a useful platform for nutritional ecology research.
An issue that warrants particular attention is the practical
difficulties of estimating the DEB parameters (van der Meer
2006). Further, to achieve the generality aimed at in DEB
models, species-specific detail is relegated to the ‘residual’,
whereas in the organismally explicit approach such details are
the grist that feeds the mill of generalizations. The ability of
DEB to provide insights into the evolution of diverse and
complex phenotypes has thus yet to be demonstrated (Nisbet
et al. 2000). We are currently working with proponents of
DEB to explore these issues further.
Toward an organismally explicit community
ecology
Above we have alluded to the trade-off between species-level
detail and generality in modelling community-level processes:
GF is in terms of organismal detail more highly specified than
is ES, whereas the simplified depiction in ES of nutrients (as
elements) and organisms (principally body composition)
reduces the burden of species-specific detail and thereby more
readily provides generality. The question we wish to address
in this section regards the extent to which GF models are able
to enlighten population- and community-level processes.
One recent example of an application of GF to a populationlevel phenomenon is the demonstration that the combination
of protein and salt shortage in the environment, coupled with
organismal regulatory responses to these nutrients, explains
mass migration driven by cannibalism in Mormon crickets,
Anubis simplex (Simpson et al. 2006). The question of how
GF might reveal the impact of nutritional phenotypes on
communities has previously been discussed in the literature
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
Nutrition, ecology and nutritional ecology 13
(Raubenheimer & Simpson 2004; Kearney & Porter 2006),
and was recently investigated empirically. Behmer & Joern
(2008) tested whether co-existence of seven species of generalist
grasshoppers that feed on a similar group of host plants can
be explained on the basis of niche partitioning at the level
of nutrients. Their results revealed significant differences
in selected protein : carbohydrate intake targets between all
pairwise contrasts except one. Furthermore, peak performance
(in terms of development time and growth rate) corresponded
with the intake points selected by these species, thus linking
nutrient selection to demographic responses. These data
support the idea that competition for nutrients might drive
niche partitioning in the form of divergent intake targets.
Behmer and Joern’s study provides a clear illustration of
how models of macronutrient regulation and its links to
animal performance may contribute to an understanding
of species interactions. We concur, however, with their assessment that this study represents a starting point, and substantial conceptual and empirical ground remains to be covered.
It is, for example, critically important to maintain a realistic
perspective on the role that nutrients play in community-level
processes, and thus on the role they should play in models
of these processes. In particular, ecological interactions
take place between organisms (both consumers and consumed),
and not between nutrients, and in most cases the proper
focus for the analysis would thus be the organism. Nutrients
can, however, play an indispensible role in explaining and
predicting the interactions among organisms, especially if
the model is sufficiently organismally explicit to embody
the notions of evolutionary function and homeostasis (e.g.
Behmer & Joern 2008).
In the system studied by Behmer & Joern (2008), for example,
it is tempting to characterize the interactions among the
component species as competition for nutrients, but in
ecological terms what is actually being competed for is not
nutrients but foods (in this case plants). Thus, two herbivore
species that had widely dissimilar intake targets might
nonetheless come into direct competition if they relied on
different parts of the same plant – for example, seeds vs. leaves.
In this case the unit being competed for is plants, and nutrients
are relevant only in so far as they constitute the functional
reason for that competition. Any model that does not consider
this fails to meet the criterion of ecological explicitness, and
falls into the same trap as do element-based models of organismal
responses: they represent an inappropriate level of reduction.
With this caveat in mind, we believe that models which
are both nutritionally and organismally explicit have considerable potential to enlighten community-level processes. In
Fig. 4 we present an example that explores this potential by
modelling in the context of food webs interactions between
macronutrient balance, body composition, and energetic
maintenance requirements. The model, which follows the
same structure as Fig. 2, demonstrates several points:
1. As trophic levels are ascended, homeostatic feeding and
growth responses will progressively limit the range of
body compositions and shift the mean composition
towards a higher protein ratio (Fig. 4d). This would
explain the observations from ES that N% rises across
trophic levels, and that the ratio of carbon to nitrogen
between foods and consumers (C : N resource/C : N consumer) narrows progressively moving up trophic levels
(Denno & Fagan 2003; note the similarity between fig. 2c
in that article and Fig. 4d in our article).
2. Consumers become progressively carbohydrate and fat
limited across trophic levels – not nitrogen limited as suggested by ES (Denno & Fagan 2003). Thus the nutritional
incentives to feed down the food chain, rather than up the
food chain (Denno & Fagan 2003), become greater at higher
trophic levels as metabolic energy becomes increasingly scarce.
3. Collectively, these nutritional effects, in conjunction with
the progressive loss of energy across trophic levels (the
trophic pyramid effect), might explain why food webs are
typically limited to fewer than 4–6 trophic levels. We are
exploring this possibility further at present.
In closing, we note that both the model in Fig. 4 and the
study of Behmer & Joern (2008) represent ‘as is’ applications
of GF to questions of community ecology. To achieve a broader
applicability, GF would need to be extended to capture the
spatial and temporal dynamics of ecological communities. This
project is already underway (Simpson et al., under review).
Conclusions
We have suggested that an over-arching framework for
nutritional ecology would be nutritionally, organismally
and ecologically explicit, and should be heterarchically structured. Numerous frameworks have been applied within
nutritional ecology but, like OFT, most are based on singlecurrency models and are thus not nutritionally explicit. CINE
was influential in highlighting the need for a nutritionally
explicit approach, although it failed to produce a modelling
framework for dealing with multiple currencies. ES, in contrast,
has provided a useful multi-currency tool for ecological
studies, but its focus on elements and on body composition
as a proxy for fitness has reduced its utility for organismal
studies. DEB theory more fully specifies phenotypes than
does ES, but faces challenges of parameterization and
incorporating species-specific detail. GF, on the other hand,
was developed as a nutritionally explicit approach for
organismal biology, but its potential to contribute to community ecology has yet to be proven. While each paradigm
has yielded valuable contributions in their own right, we
believe that an over-arching framework for nutritional
ecology could only be achieved by combining aspects of these
approaches. Such attempts are already underway. For example,
in ES models Anderson et al. (2005) and Boersma & Elser
(2006) have considered the costs of nutrient excesses, Hessen
& Anderson (2008) explored the complexities of the notion
‘excess C’, and a few studies have focussed on biochemicals
rather than elements (Anderson 1994, Anderson & Pond 2000;
Anderson et al. 2004). Simpson et al. (2004) incorporated
into GF the a priori predictive approach on which OFT is
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
14
D. Raubenheimer et al.
Fig. 4. Implications for food webs of interactions between macronutrient balance, body composition, and energetic maintenance requirements.
(a) Herbivore: The positions of the growth target (GT) and nutrient target (NT) for a hypothetical herbivore are indicated in a protein (P),
carbohydrate/lipid (CL) space. The range of food compositions available to the herbivore is indicated by the grey segment. The position of the
NT is near the middle of the distribution of plant food compositions, under the assumption that animals evolve to centre their intake
requirements with respect to their nutritional environments. The line E (=D(u)p + D(u)cl) indicates the energetic requirements of the animal, and
D(u)cl/D(u)p the optimal C + L : P fuel blend for meeting these requirements; in this case, principally carbohydrate and lipid, with some
contribution from protein, as for many herbivores. From theory, and our locust data (Raubenheimer & Simpson 1993), it is expected that
D(u)cl + D(u)p will be approximately 10 times the sum of energy at the GT; that is, there is a 10% trophic energy transfer efficiency. (b) Primary
carnivore: On the assumption (again based on data, e.g. Fig. 7 in Raubenheimer & Simpson 2004) that our herbivores have been 50% efficient in
regulating their body compositions relative to the range of diets available in (a), the range of food compositions offered by our population of
herbivores to the next trophic level is narrowed relative to the compositions of plants available to herbivores. Importantly, the narrowing is not
symmetrical. Because the GT is protein-biased (animal tissues, whatever their trophic level, contain high levels of protein), the range of herbivore
body compositions is shifted towards a higher mean P : CL ratio than was seen among plants. The NT, assumed for simplicity to comprise the
same sum of CL and P as the herbivore (i.e. to fall on line AB), has shifted to reflect the distribution of prey body compositions, such that now
more of E must come from protein. (c) Secondary carnivore. The progression continues in the transfer from primary predator to secondary
predator. Again there has been a 50% narrowing of body compositions in predators relative to their prey (primary carnivores), and again this
has been asymmetrical, being constrained by the high protein content of the growth target. By the fifth trophic level (not shown) there will very
little variation in prey body compositions left. The overall effect (shown in d.) is that as trophic levels are ascended, homeostatic feeding and
growth responses will progressively limit the range of body compositions and shift the mean composition towards a higher protein ratio.
founded, and Raubenheimer & Simpson (2004) explored
similarities and contrasts between ES and GF. More recently,
Behmer & Joern (2008) have applied GF in an empirical study
to questions of community ecology, and in the present article
we have explored some implications of geometrical analysis
for theoretical ecology. We consider further expansions/
syntheses of existing models to be a priority goal, which stands
to benefit nutrition, ecology and nutritional ecology alike.
Acknowledgement
Authors thank Tom Anderson, Mike Kearney, Carol Boggs, Spencer Behmer
and two anonymous referees for useful suggestions which improved this article.
References
Anderson, T.R. (1994) Relating C : N ratios in zooplankton food and faecal
pellets using a biochemical model. Journal of Experimental Marine Biology
and Ecology, 184, 183–199.
Anderson, T.R. & Pond, D.W. (2000) Stoichiometric theory extended to
micronutrients: comparison of the roles of essential fatty acids, carbon, and
nitrogen in the nutrition of marine copepods. Limnology and Oceanography,
45, 1162–1167.
Anderson, T.R., Boersma, M. & Raubenheimer, D. (2004) Stoichiometry: linking
elements to biochemicals. Ecology, 85, 1193–1202.
Anderson, T.R., Hessen, D.O., Elser, J.J. & Urabe, J. (2005) Metabolic
stoichiometry and the fate of excess carbon and nutrients in consumers.
American Naturalist, 165, 1–15.
Beaupre, S.J., Dunham, A.E. & Overall, K.L. (1993) The effects of consumption
rate and temperature on apparent digestibility coefficient, urate production,
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
Nutrition, ecology and nutritional ecology 15
metabolizable energy coefficient and passage time in canyon lizards
(Sceloporus merriami) from two populations. Functional Ecology, 7, 272–
280.
Behmer, S.T. & Joern, A. (2008) Coexisting generalist herbivores occupy
unique nutritional feeding niches. Proceedings of the National Academy of
Sciences, USA, 105 (6), 1977–1982.
Belovsky, G.E. (1986) Optimal foraging and community structure – implications for a guild of generalist grassland herbivores. Oecologia, 70, 35–52.
Belovsky, G.E. (1990) How important are nutrient constraints in optimal
foraging models or are spatial/temporal factors more important? Behavioural
Mechanisms of Food Selection (ed. R.N. Hughes), pp. 255–278. SpringerVerlag, Berlin.
Berteaux, D., Crete, M., Huot, J., Maltais, J. & Ouellet, J.-P. (1998) Food choice
by white-tailed deer in relation to protein and energy content of the diet: a
field experiment. Oecologia, 115, 84–92.
Boersma, M. & Elser, J.J. (2006) Too much of a good thing: On stoichiometrically
balanced diets and maximal growth. Ecology, 87, 1325–1330.
Boggs, C (2009) Understanding insect life histories and senescence through a
resource allocation lens Functional Ecology, 23.
Brett, M.T. (2004) When is a correlation between non-independent variables
‘spurious’? Oikos, 105, 647–656.
Cardinale, B.J., Weis, J.J., Forbes, A.E., Tilmon, K.J. & Ives, A.R. (2006)
Biodiversity as both a cause and consequence of resource availability:
A study of reciprocal causality in a predator-prey system. Journal of Animal
Ecology, 75, 497–505.
Chambers, P.G., Simpson, S.J. & Raubenheimer, D. (1995) Behavioural
mechanisms of nutrient balancing in Locusta migratoria. Animal Behaviour,
50, 1513–1523.
Denno, R.F. & Fagan, W.F. (2003) Might nitrogen limitation promote
omnivory among carnivorous arthropods? Ecology, 84, 2522–2531.
Denton, D.A., Eichberg, J.W., Shade, R. & Weisinger, R.S. (1993) Sodium
appetite in response to sodium deficiency in baboons. American Journal of
Physiology, 264, R539–R543.
Elser, J. (2006) Biological stoichiometry: A chemical bridge between ecosystem
ecology and evolutionary biology. American Naturalist, 168, S25–S35.
Fraenkel, G.S. (1959) The raison d’etre of secondary plant substances. Science,
129, 1466–1470.
Fryxell, J.M. & Lundberg, P. (1997) Individual Behaviour and Community
Dynamics. Chapman & Hall, London.
Galef, B.G. (1991) A contrarian view of the wisdom of the body as it relates to
dietary self-selection. Psychological Review, 98, 218–223.
Gunji, Y.P. & Kamiura, M. (2004) Observational heterarchy enhancing active
coupling. Physica D-Nonlinear Phenomena, 198, 74–105.
Hessen, D.O. & Anderson, T.R. (2008) Excess carbon in aquatic organisms and
ecosystems: Physiological, ecological, and evolutionary implications.
Limnology and Oceanography, 53, 1685–1696.
Hirakawa, H. (1995) Diet optimization with a nutrient or toxin constraint.
Theoretical Population Biology, 7, 331–346.
Illius, A.W., Tolkamp, B.J. & Yearsley, J. (2002) The evolution of the control of
food intake. Proceedings of the Nutrition Society, 61, 465–472.
Johnson, M.T.J. & Stinchcombe, J.R. (2007) An emerging synthesis between
community ecology and evolutionary biology. Trends in Ecology and
Evolution, 22, 250–257.
Jones, C.G. & Lawton, J.H. (1995) Linking Species and Ecosystems. Chapman
& Hall, New York.
Kearney, M. & Porter, W.P. (2006) Ecologists have already started rebuilding
community ecology from functional traits. Trends in Ecology & Evolution,
21, 481–482.
Kingsolver, J.G. & Huey, R.B. (2008) Size, temperature, and fitness: three rules.
Evolutionary Ecology Research, 10, 251–268.
Kooijman, S.A.L. (2000) Dynamic Energy Budgets in Biological Systems.
Cambridge University Press, Cambridge.
Lourenco, S.O., Barbarino, E., De-Paula, J.C., Pereira, L.O. & Lanfer Marquez,
U.M. (2002) Amino acid composition, protein content and calculation of
nitrogen-to-protein conversion factors for 19 tropical seaweeds. Phycological
Research, 50, 233–241.
Markison, S. (2001) The role of taste in the recovery from specific nutrient
deficiencies in rats. Nutritional Neuroscience, 4, 1–14.
Markison, S., Thompson, B.L., Smith, J.C. & Spector, A.C. (2000) Time course
and pattern of compensatory ingestive behavioral adjustments to lysine
deficiency in rats. Journal of Nutrition, 130, 1320–1328.
Maynard Smith, J. (1978) Optimization theory in evolution. Annual Review of
Ecology and Systematics, 9, 31–56.
McGill, B.J., Enquist, B.J., Weiher, E. & Westoby, M. (2006) Rebuilding
community ecology from functional traits. Trends in Ecology and Evolution,
21, 178–185.
Mitchell, R. (1981) Insect behavior, resource exploitation, and fitness. Annual
Review of Entomology, 26, 373–396.
Newman, J.A. (2006) Herbivory. Foraging: Behaviour and Ecology (eds
D.W. Stephens, J.C. Brown & R.C. Ydenberg), pp. 175–218. The University
of Chicago Press, Chicago.
Nisbet, R.M., Muller, E.B., Lika, K. & Kooijman, S.A.L. (2000) From molecules
to ecosystems through dynamic energy budget models. Journal of Animal
Ecology, 69, 913–926.
Olff, H., Brown, V.K. & Drent, R.H. (1999) Herbivores: Between Plants and
Predators. 38th Symposium of the British Ecological Society. Blackwell
Science, Oxford.
Painter, R.H. (1936) The food of insects and its relation to resistance of plants
to insect attack. The American Naturalist, 70, 547–566.
Petchey, O.L., Beckerman, A.P., Riede, J.O. & Warren, P.H. 2008 Size, foraging,
and food web structure. Proceedings of the National Academy of Sciences,
USA, 105, 4191–4196.
Pulliam, R.H. (1975) Diet optimization with nutrient constraints. American
Naturalist, 109, 765–768.
Pyke, G.H., Pulliam, H.R. & Charnov, D.L. (1977) Optimal foraging: a selective
review of theory and tests. Quarterly Review of Biology, 52, 137–154.
Raubenheimer, D. & Jones, S.A. (2006) Nutritional imbalance in an extreme
generalist omnivore: tolerance and recovery through complementary food
selection. Animal Behaviour, 71, 1253–1262.
Raubenheimer, D. & Simpson, S.J. (1992) Analysis of covariance: an alternative
to nutritional indices. Entomologia Experimentalis et Applicata, 62, 221–231.
Raubenheimer, D. & Simpson, S.J. (1993) The geometry of compensatory
feeding in the locust. Animal Behaviour, 45, 953–964.
Raubenheimer, D. & Simpson, S.J. (1994) The analysis of nutrient budgets.
Functional Ecology, 8, 783–791.
Raubenheimer, D. & Simpson, S.J. (1995) Constructing nutrient budgets.
Entomologia Experimentalis et Applicata, 77, 99–104.
Raubenheimer, D. & Simpson, S.J. (1997) Integrative models of nutrient
balancing: application to insects and vertebrates. Nutrition Research Reviews,
10, 151–179.
Raubenheimer, D. & Simpson, S.J. (1998) Nutrient transfer functions: the site
of integration between feeding behaviour and nutritional physiology.
Chemoecology, 8, 61–68.
Raubenheimer, D. & Simpson, S.J. (2004) Organismal stoichiometry:
quantifying non-independence among food components. Ecology, 85,
1203–1216.
Raubenheimer, D. (1995) Problems with ratio analysis in nutritional studies.
Functional Ecology, 9, 21–29.
Raubenheimer, D., Lee, K.-P. & Simpson, S.J. (2005) Does Bertrand’s rule
apply to macronutrients? Proceedings of the Royal Society B, 272, 2429–
2434.
Reiners, W.A. (1986) Complementary models for ecosystems. American
Naturalist, 127, 59–73.
Schmitz, O.J. (2008) Herbivory from Individuals to ecosystems. Annual Review
of Ecology and Systematics, 39, in press.
Schneider, H.A. (1967) Ecological ectocrines in experimental epidemiology.
Science, 158, 597–603.
Scriber, J.M. & Slansky, F. (1981) The nutritional ecology of immature insects.
Annual Review of Entomology, 26, 183–211.
Shettleworth, S.J. (2000) Cognitive ecology: field or label? Trends in Ecology and
Evolution, 15, 161–162.
Simpson, S.J. & Raubenheimer, D. (1993) A multi-level analysis of feeding
behaviour: the geometry of nutritional decisions. Philosophical Transactions
of the Royal Society B, 342, 381–402.
Simpson, S.J. & Raubenheimer, D. (1995) The geometric analysis of feeding
and nutrition: a user’s guide. Journal of Insect Physiology, 7, 545–553.
Simpson, S.J. & Raubenheimer, D. (1999) Assuaging nutritional complexity: a
geometrical approach. Proceedings of the Nutrition Society, 58, 779–789.
Simpson, S.J. & Raubenheimer, D. (2005) Obesity: the protein leverage hypothesis.
Obesity Reviews, 6, 133–142.
Simpson, S.J., Sibly, R.M., Lee, K.P., Behmer, S.T. & Raubenheimer, D. (2004)
Optimal foraging when regulating intake of multiple nutrients. Animal
Behaviour, 68, 1299–1311.
Simpson, S.J., Sword, G.A., Lorch, P.D. & Couzin, I.D. (2006) Cannibal crickets on
a forced march for protein and salt. Proceedings of the National Academy of
Sciences, USA, 103, 4152–4156.
Slansky, F. & Rodriguez, J.G. (1987) Nutritional ecology of insects, mites,
spiders, and related invertebrates: an overview. Nutritional Ecology of
Insects, Mites, and Related Invertebrates (eds F. Slansky & J.G. Rodriguez),
pp. 1–69. Wiley, New York.
Stanley Price, M.R. (1978) The nutritional ecology of coke’s hartebeest
(Alcelaphus buselaphus cokei) in kenya. Journal, 15, 33–49.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16
16
D. Raubenheimer et al.
Stephens, D.W., Brown, J.S. & Ydenberg, R.C. (2006) Foraging: Behavior and
Ecology. The University of Chicago Press, Chicago.
Sterner, R.W. & Elser, J.J. (2002) Ecological Stoichiometry: the Biology of
Elements from Molecules to the Biosphere. Princeton University Press,
Princeton.
Tinbergen, N. (1963) On aims and methods in Ethology. Zeitschrift fur
Tierpsychologie, 20, 410–433.
Tordoff, M.G. (2001) Calcium: Taste, intake, and appetite. Physiological
Reviews, 81, 1567–1459.
Van Der Meer, J. (2006) An introduction to Dynamic Energy Budget (DEB)
models with special emphasis on parameter estimation. Journal of Sea
Research, 56, 85–102.
Waldbauer, G.P. (1968) The consumption and utilization of food by insects.
Recent Advances in Insect Physiology, 5, 229–288.
Waldbauer, G.P. (1982) Citation classic – the consumption and utilization of
food by insects. Current Contents/Agriculture Biology and Environmental
Sciences, (7), 22.
Waldbauer, G.P. & Friedman, S. (1991) Self-selection of optimal diets by
insects. Annual Review of Entomology, 36, 43–63.
Waldbauer, G.P., Cohen, R.W. & Friedman, S. (1984) Self-selection of an
optimal nutrient mix from defined diets by larvae of the corn earworm,
Heliothis zea (Boddie). Physiological Zoology, 57, 590–597.
Westoby, M. (1974) An analysis of diet selection by large generalist herbivores.
American Naturalist, 108, 290–304.
White, T.C.R. (1983) The Inadequate Environment: Nitrogen and the Abundance
of Animals. Springer, Berlin.
Yamamoto, T., Shima, T., Furuita, H., Shiraishi, M., Sanchez-Vazquez, F.J. &
Tabata, M. (2000) Self-selection of diets with different amino acid profiles by
rainbow trout (Oncorhynchus mykiss). Aquaculture, 187, 375–386.
Zanotto, F.P., Gouveia, S.M., Simpson, S.J., Raubenheimer, D. & Calder, P.C.
(1997) Nutritional homeostasis in locusts: is there a mechanism for
increased energy expenditure during carbohydrate overfeeding? Journal of
Experimental Biology, 200, 2473–2448.
Received 12 July 2008; accepted 17 November 2008
Handling Editor: Carol Boggs
Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Table S1. Some examples of published applications of the
Geometric Framework.
Please note: Wiley-Blackwell are not responsible for the
content or functionality of any supporting materials supplied
by the authors. Any queries (other than missing material)
should be directed to the corresponding author for the article.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 4–16