Functional Ecology

2009,

23

, 4–16 doi: 10.1111/j.1365-2435.2008.01522.x

© 2009 The Authors. Journal compilation © 2009 British Ecological Society

Blackwell Publishing Ltd

NUTRITIONAL ECOLOGY

Nutrition, ecology and nutritional ecology: toward

an integrated framework

David Raubenheimer

1

*, Steven J. Simpson

2

and David Mayntz

3,4

1

Institute of Natural Resources and New Zealand Institute for Advanced Study, Massey University, Albany, New Zealand;

2

School of Biological Sciences, University of Sydney, Sydney, Australia;

3

Department of Ecology and Genetics,

University of Aarhus, Aarhus, Denmark; and

4

Department of Zoology, University of Oxford, Oxford, UK

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 nutritionalecology have a unique theoretical framework and research program and thus qualify as a field ofresearch in its own right?

2.

We suggest that the distinctive feature of nutritional ecology is its integrative nature, and that thefield would benefit from more attention to formalizing a theoretical and quantitative framework fordeveloping this.

3.

Such a framework, we propose, should satisfy three minimal requirements: it should benutritionally explicit, organismally explicit, and ecologically explicit.

4.

We evaluate against these criteria four existing frameworks (Optimal Foraging Theory, ClassicalInsect 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 GeometricFramework, 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 functionand on mechanism. Such cross-disciplinary breadth pro-vides broad conceptual and methodological foundations, andimbues the discipline with wide-ranging relevance. But italso presents challenges. Foremost among these is that toprogress beyond the status of label and qualify as a field ofresearch in its own right (Shettleworth 2000), nutritionalecology needs an identity more distinct than a diffuse con-fluence of methods and interests united within the generalareas of nutrition and ecology.

What would be the cornerstone of that identity? In ourjudgement, the single most distinctive characteristic ofnutritional ecology is its propensity to probe the gaps betweendisparate fields, yielding integrative insights that would

otherwise not be obtained. The hiatus that is most closelyassociated with the subject is that between field ecology (e.g.resource quality and distribution) and animal phenotypes(e.g. foraging behaviour, functional morphology, digestivephysiology). Progress in bridging this gap has, however, beenpiecemeal and incomplete, as is evidenced by growingconcern in the literature for greater integration between thestudy 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 wouldbe better equipped for achieving this integration if moreattention was paid to developing frameworks that system-atically define the panoply of salient components in organism–environment interactions and explicitly model their inte-gration. In other words, frameworks are needed that providea scaffold for melding nutrition and ecology into an integratednutritional ecology.

The primary aim of this article is to state what we considerto be the necessary basic properties of such a scheme, andevaluate in relation to these some frameworks that arecurrently in use: Optimal Foraging Theory, Classical InsectNutritional Ecology, the Geometric Framework for nutrition,

*Corresponding author: E-mail: d.raubenheimer@massey.ac.nz

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and Ecological Stoichiometry. Our survey reveals that all fourapproaches have provided local foci of conceptual and/ormethodological cohesion within nutritional ecology, but atruly integrative framework would involve an expansion orsynthesis of existing frameworks. A second aim of this articleis to address the expansion of our own contribution, theGeometric Framework, to questions of community ecology.

Nutritional ecology: components and

interactions

The core components of a general conceptual frameworkfor nutritional ecology are set out in Fig. 1. Most generally,these are the organism, the ecological environment, andthe nutritional basis of the interaction between organismand environment – and here we use ‘nutritional’ in the broadsense of any property of a food that affects the animal(Westoby 1974).

We believe that the representation of these three com-ponents and interactions should be

explicit

, in the sense thatthe framework can enable research to be structured so asdirectly 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 con-stants. Thus, models that treat foods as unitary resources, thatis, 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 foodcomponents in nutritional ecology interactions (see alsoBoggs 2009). Furthermore, links among the components(depicted by arrows in Fig. 1) should be bidirectional, thusenabling the research to be structured in a way that addressescausal 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 biologicallyrelevant interactions that are potentially of interest to nutritionalecology studies. There has, for example, been a recent inten-sification of interest in the question of how communityprocesses and patterns influence evolution (Johnson &Stinchcombe 2007). If the context of such a study was nutri-tionally explicit, then it would warrant an arrow linkingorganism ‘function’ directly with ‘community’, or the inter-action might possibly involve ‘history’ (e.g. if phylogeographywere an important component). A useful term for such anetwork in which elements can be viewed as interactingwith other elements that occur in two or more components(e.g. ‘function’ vs. ‘community’ and/or ‘history’) is ‘heter-archical’ (Gunji & Kamiura 2004).

In the remainder of this section we briefly expand on therole of the organism, the environment and nutrition in thescheme.

THE

ORGANISM

The organism is central in nutritional ecology. As is true inmany other areas in organismal biology, nutritional ecologycan trace important influences to the classical ethologicalmovement of 1930–1960’s. Ethology, too, is a fundamentallyintegrative science, in two respects that are relevant to ourdiscussion here. First is the emphasis in ethology on under-standing animal phenotypes in relation to their ecologicalenvironment, which has likewise historically been associatedwith the emergence of the term ‘nutritional ecology’ (e.g.Schneider 1967; Stanley Price 1978) and has continued to becentral to the identity of the field. Second, ethology’s ‘manifesto’,as famously articulated by Niko Tinbergen (1963), is based onan integrative approach which urges animal behaviouriststo combine in their thinking about behaviour four levelsof analysis: its mechanisms, development, function andevolutionary history.

Tinbergen’s framework remains hugely influential and inour opinion could make a valuable contribution to integrationin nutritional ecology. Specifically, it provides a more refineddepiction of the organism in nutritional ecology research,through explicitly distinguishing the links between nutritionalenvironments on the one hand, and on the other mechanistic,developmental, functional and phylogenetic aspects ofphenotypes (Fig. 1). The Tinbergen scheme was developed

Fig. 1. Conceptual scheme depicting the components of an integrativeframework for nutritional ecology. The organism is considered fromthe viewpoints of function, mechanism, development and history(Tinbergen 1963), while the environment is partitioned into bioticand abiotic components. The nutritional interactions that take placebetween organism and environment involve both the effects of theenvironment on phenotypes (downward arrows) and the impact ofphenotypes on the ecological environment (upward arrows). Anutritional ecology framework should also cope with horizontalinteractions (dashed arrows), for example between biotic and abioticcomponents of the environment, or between mechanistic andfunctional aspects of phenotypes.

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and is most frequently applied in the context of behaviour,but in nutritional ecology it would apply to all aspects ofthe phenotype, including physiology, morphology and lifehistory.

THE

ENVIRONMENT

AND

ORGANISM

-

ENVIRONMENT

INTERACTIONS

The component of the environment that is usually at thecentre 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 (Schmitz2008). Being focused primarily on the organism, nutritionalecology studies most commonly emphasise the downwardarrows in Fig. 1, the ways that organisms respond to theecological environment at various time-scales: behaviouraland physiological responses, phenotypic plasticity (e.g. inoral 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 oppositedirection, addressing questions of how phenotypes impact onpopulation – (e.g. Simpson

et al

. 2006) and community-levelprocesses, or the reciprocal impacts of communities andphenotypes (e.g. Schmitz 2008). We believe that nutritionalecology is well-placed to make a substantial contributionto the question of how phenotypes impact on ecologicalcommunities, particularly through dialogue with other foci ofintegration within population, community and ecosystemsecology (S.J. Simpson

et al

., under review).

NUTRIT ION

A framework that is nutritionally explicit enables thequestions to be addressed: ‘which nutrient(s) and other foodcomponents 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 ecologicalconsequences for the animal of responding in the way that itdoes?’

There exists surprisingly little information on howspecific nutrients influence the homeostatic and performanceresponses of animals, and even less on how these influencesin turn impact on populations and communities. The reasonfor this is that studies are most frequently conducted withinframeworks that do not systematically disentangle the rolesof specific food components, or else

a priori

identify onecomponent (usually nitrogen, energy or plant secondarycompounds) as paramount and code this as an input to thestudy rather than an experimental outcome. Even wherethe focal component is correctly identified, an importantpart of the story might be overlooked in this approach. Thisis because foods are complex mixtures, and the impact ofspecific components is usually contingent on and/or exertedthrough other components. For example, in many animals theingestive regulatory systems weight protein more strongly

than other nutrients, with the consequence that they over-ingest other nutrients when eating low-protein foods – the‘protein leverage’ effect (Simpson & Raubenheimer 2005).In such cases, protein would correctly be identified as thepre-eminent nutrient, and yet the major constraint on proteingain might be the inability of the animal to ingest largeexcesses of some other food component(s), and the majorhealth impact due to the excess of these components thatthey do ingested (Raubenheimer, Lee & Simpson 2005;Boggs 2009).

We consider it a high priority in nutritional ecology toadopt nutritionally explicit frameworks which systematicallyidentify the individual and interactive roles of different foodcomponents.

Frameworks in nutritional ecology

In this section we evaluate against the criteria set above someof the frameworks currently in use in nutritional ecology.We cannot hope to do justice within the space constraints tothe diversity of modelling approaches that have been appliedto specific questions in nutritional ecology, and our coverageis therefore restricted to four frameworks that we considerto be particularly relevant to the question of integration:Optimal Foraging Theory, Classical Insect Nutritional Ecology,the Geometric Framework for nutrition, and ecologicalstoichiometry. We believe, however, that the main points oursurvey illustrates are robust to the inclusion of any frameworkin use in nutritional ecology.

OPTIMAL

FORAGING

THEORY

Optimal Foraging Theory (OFT) is an evolutionarily-inspiredframework that aims to ‘explain and predict’ (Pyke

et al.

1977)the patterns of food choice and foraging by animals. It isbased on the premise that foraging can be viewed as a processthat has been optimized by natural selection to maximizefitness, and thus optimization mathematics is an appropriatetool 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 theoptimality approach is used most frequently to modelbehavioural aspects of foraging, it has also been applied tophysiological aspects such as food processing times anddigestion efficiencies (e.g. Raubenheimer & Simpson 1998).

OFT is concerned primarily with the effects of the environ-ment on the phenotypes of animals (i.e. the downward arrowsin Fig. 1), but it has also been applied in the reverse direction,exploring how the functional characteristics of organismsinfluence ecological communities (e.g. Belovsky 1986; Petchey

et al

. 2008). OFT is, therefore, clearly a framework for studyingthe nutritional relations between animals and their environ-ments, and for this reason is relevant to our consideration ofnutritional ecology. The key question, however, is the extentto which in its current form OFT is sufficiently nutritionallyexplicit to carry out the nutritional ecology agenda.

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Where the currency in OFT models is nutritional (asopposed to, e.g. time minimization or survival maximization),it usually involves energy, although other nutritional currenciesare occasionally involved (e.g. protein – Berteaux

et al

. 1998).In this respect, OFT is a uni-dimensional approach whichassumes

a priori

that a single food component is limitingto the animal, and elevates that component to the status ofcurrency. Other food components, such as toxins andnutrients that are not represented as currency, are coded asconstraints within which the animal has to work in itsattempts to achieve the postulated foraging goal (e.g. Westoby1974; Pulliam 1975; Belovsky 1990; Hirakawa 1995). This isoften, but not always (e.g. Pulliam 1975), done using linearprogramming (Westoby 1974; Belovsky 1990).

OFT has clearly experienced many successes (Stephens

et al.

2006), but an improved understanding of nutritionalprocesses is not among them. We believe the reason for this tobe that OFT does not comfortably fulfil the criterion of anutritionally explicit framework. First, while it might arguablybe true that at any one time an animal is limited by a singlenutrient (the currency), it is an open and important questionas to the dynamics and time-scale of such limitation. At theone extreme, single-nutrient limitation might be a perpetualfeature of an animal’s nutritional ecology, as is proposed byWhite (1983) to be generally the case for nitrogen in manyecosystems. At the other extreme, for an animal that switchesbetween 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 nutrientbut a property of the macronutrient groups protein, lipid andcarbohydrate. Without explicitly distinguishing among theseenergetic components, caloric measures present the risk thatforaging aimed at maximizing one or more of these macro-nutrients, or optimizing their balance, is confounded withenergy maximization. Finally, it is often difficult, impossible,or meaningless to distinguish between ‘constraint’ and‘adaptive strategy’. We therefore consider it a better heuristicto view nutritional processes as a ‘network of interconnectedtrade-offs with a global optimum’ (Illius, Tolkamp & Yearsley2002).

Nonetheless, in addition to its successes in furthering theunderstanding of animal decision making, the optimality-based approach to foraging has made a substantial contributionto the development of nutritional ecology. It set the bar forconceptual and quantitative rigour in the study of foraging,and provided a foundation which is increasingly becomingintegrated with other approaches in the study of nutritionalecology (Simpson

et al

. 2004; Newman 2006). Additionally,in its earlier formulations, OFT provided a point of contrastagainst which other approaches could develop. In the presentcontext, the most relevant of these is Classical InsectNutritional Ecology.

CLASSICAL

INSECT

NUTRIT IONAL

ECOLOGY

The development of what we refer to as ‘Classical InsectNutritional Ecology’ (CINE) was seeded by the convergence

in the 1950’s and 1960’s of several strands of research whichshared a common interest in the factors that govern foodselection by animals. Notable among these was the work ofReginald Painter (e.g. 1936), who developed the view thatvariation in the nutrient composition of plants is central tothe patterns of food choice and performance responsesby phytophagous insects. A second line of interest, moreclosely associated with the field of plant–animal co-evolution,asserted that food selection in phytophagous insects is drivennot by nutrients, but by plant secondary compounds (e.g.Fraenkel 1959). These discussions took place in a climateof growing interest among ecologists in the extent to whichthe nutritional quality of plant tissues limits herbivorepopulations (Schmitz 2008).

Against this background, there was clearly a need in thestudy of animal foraging for a paradigm that approachedmore directly than did OFT the question of which curren-cies

actually

drive the foraging decisions and populationresponses of animals (Mitchell 1981; Waldbauer & Friedman1991) – i.e. for an approach that was nutritionally explicit. Therequisite paradigm was adopted from the experimentalpsychology literature, where it had been shown in the workof Curt Richter and others that rats can self-select from arange of nutritionally incomplete foods a diet that sustainsgood performance, and can alter their patterns of foodselection to compensate for surgically-induced nutritionalperturbations (Galef 1991). The dietary self-selectionparadigm was introduced to CINE by Gil Waldbauer andcolleagues (e.g. Waldbauer

et al.

1984). It has since beendemonstrated using this approach that dietary self-selectionis ubiquitous among animals. Some combination of themacronutrient groups protein, carbohydrate and fats areregulated independently by many (if not most) animals, andso 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 (Tordoff2001) regulated by some. These data underscore the impor-tance for nutritional ecology of adopting a framework that isnutritionally explicit.

Gil Waldbauer also made another highly influential con-tribution to CINE, in introducing a quantitative frameworkfor representing the nutritional responses of animals to theirfoods (Waldbauer 1968). Waldbauer’s ‘quantitative nutrition’is a budgetary approach, which expresses the relationshipsbetween food intake and utilization as rates and efficienciesthat can be used comparatively – for example, to comparegrowth in insects that have different consumption rates. Theproposed ratio-based nutritional indices – relative consumptionrate (RCR), approximate digestibility (AD), efficiency ofconversion of ingested food (ECI), and efficiency of con-version of digested food (ECD) – rapidly became the industrystandard within CINE (e.g. Scriber & Slansky 1981).

By the late 1980s the field had matured to the point whereSlansky & Rodriguez (1987) could propose a general conceptualframework for research in CINE. Their recommended frame-work would involve: (i) determining the performance of ananimal in circumstances (relating to nutrition, as well as its

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interactions with other factors such as temperature and pre-dation) which maximize fitness; (ii) determining how realisticchanges in these circumstances influence the animal’sperformance, its compensatory responses for ameliorating theimpacts on performance, and the trade-offs that it encountersin responding to the altered circumstances; (iii) performingcomparative studies, in which the patterns in (i) and (ii) arerelated more generally to phylogeny, development and ecology.Slansky and Rodriguez recommended Waldbauer’s (1968)ratio-based indices as a quantitative approach for carryingout this agenda.

In our view, the major general contribution of CINE was torecognize explicitly the fact that the nutritional ecology ofanimals is complex, involving interactions among numerousenvironmental factors (e.g. nutrient and non-nutrient foodcomponents, temperature etc.) and animal responses (e.g.foraging, feeding, food utilization, growth). The Slansky–Rodriguez manifesto constituted an elegant approach toconceptualizing the issue, but CINE did not produce a frame-work that was up to the task of quantifying and interpretingthese multifarious interactions. The paradigm of dietaryself-selection provided a means to demonstrate cases whereanimals feed non-randomly on foods differing in com-position, and to identify the nutrients that are involved in thepatterns of food selection. It could not, however, deal with thecritical interactive effects of these nutrients on the patterns offood selection and post-ingestive and performance responses.Similarly, in introducing terms representing key homeostaticprocesses (intake, nutrient assimilation, growth and excretion),Waldbauer’s quantitative budgetary approach emphasizedthe active role of the organism in nutritional ecology, butfell short as regards integration. Ostensibly, the nutritionalindices that he proposed

did

represent an integration of differenthomeostatic responses, because each index includes two ormore of the critical regulatory variables. However, compoundingseveral variables into a single index usually does not reveal therelationships among them, but obscures these relationships(Raubenheimer & Simpson 1992). To be sure, Waldbauer’saim in recommending these indices was not integration,but standardization: they enabled responses (e.g. growth) tobe compared across animals that differed in other relevantaspects (e.g. consumption). Unbeknownst to Waldbauer,however, a literature was subsequently to emerge demon-strating that there are statistical problems with the use ofratios for standardizing variables in this way (see Rauben-heimer & Simpson 1992 and citations therein). Some explicitattempts at integration have been made by plotting ratioindices against each other (e.g. Scriber & Slansky 1981;Beaupre

et al.

1993), but this too can lead to serious statisticaland interpretative problems (Raubenheimer 1995; Brett 2004).

THE

GEOMETRIC

FRAMEWORK

To address the challenge of integration, we have developed agraphical approach, the Geometric Framework (GF), whichmodels the key relationships among relevant variables innutritional ecology (Raubenheimer & Simpson 1993, 1994,

1997; Simpson & Raubenheimer 1993, 1995, 1999). GF isbased on the logic of state–space geometry, where relevantvariables are expressed and related to each other within ageometric space defined by two or more relevant foodcomponents. The variables represented within this spacemight include one or more foods, the organism’s current andoptimal nutritional states, the impact on its nutritional stateof eating each food, its body composition, the efficiency ofnutrient utilization, the rates of excretion, and whateverperformance consequences might be of interest. A model soconstructed can be used to conceptualize problems that involvetwo or more food components, and to design and interpretexperiments for resolving these problems. GF has been appliedto a range of biological questions involving diverse taxa (seeTable S1, in electronic Supporting Information).

As shown in Fig. 2a–c, the main components of theWaldbauer nutritional indices (e.g. intake, growth, nutrientutilization) are represented within GF models, as is dietaryself-selection (Fig. 2d). The handling of these issues is,however, very different under GF. First, graphical modelsenable the interactions among the model components to bevisualized, rather than subsumed within nutritional ratios.Second, representation of two or more food componentswithin a model enables their interactive effects to be quantified.A third point of difference, and one on which we would liketo briefly elaborate, is that in common with OFT – and inthe spirit of the Slansky–Rodriguez manifesto – GF modelsexplicitly incorporate the notion of functional optima.

This is done by distinguishing estimates of optimal valuesfor 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 enablesnutrient 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 theorganism (i.e. reflected in body composition) and

D

is dissociated(i.e. not retained). In a functional classification both terms onthe right hand side of eqn 1 are partitioned into the componentsthat 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 areretained beneficially (utilized for fitness gains) and non-beneficially (e.g. surplus lipid storage in obesity), andsimilarly

D

(

u

) and

D

(

w

) represent dissociated nutrient thatis utilized (e.g. energy metabolism, defensive secretions) andwasted (excreted in the faeces, urine or via diet-inducedthermogenesis – Zanotto

et al

. 1997).One advantage of distinguishing functional components

in this way is that it greatly increases the predictive powerof models, because homeostatic regulatory systems will tend

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Fig. 2. Hypothetical two-dimensional geometric models showing four budgetary scenarios. (a) Balanced diet. The intake target (IT) is theamount and balance of the two nutrients the animal needs to eat within the stipulated period to achieve maximal fitness, and the line originatingat the origin is a nutritional rail representing the carbohydrate : protein balance of food Fa. Since the nutritional rail intersects IT, the animalis 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 fitness-enhancing 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 atconverting ingested nutrient to functional gain, NT = IT. However, to the extent that there is a degree of constrained inefficiency in nutrientutilization, 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 orientedalong 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 ofenergy), and therefore optimal utilization requirements can be met using any combination of the two nutrients whose coordinate falls on thisellipse (further illustrated in graph b). By definition, if optimal intake is achieved (i.e. Io = IT), then observed overall utilization (Nuo) will fallon NT and observed growth (Go) will equal GT. (b). Constrained intake, with nutrient inter-conversion: Model where the animal has availableonly nutritionally imbalanced food Fb, which contains surplus protein relative to carbohydrate, and therefore cannot reach IT but must choosebetween 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 ingestiveconstraint by judiciously allocating the ingested surplus and/or deficit among budgetary components. For reference, budgetary allocationswhere 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 blackarrows. In the case modelled, the animal has regulated intake to [I3] and has thus ingested both a surplus of protein and a deficit ofcarbohydrate. Assuming that D(w)c has a fixed lower limit (i.e. where Io = IT utilization efficiency of carbohydrate is at a maximum), theintake 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), butcarbohydrate allocated to fuel energy metabolism (D(u)c) is reduced. However, a portion of the surplus ingested protein (extended portionof the arrow representing D(u)p) is deaminated and channelled into energy metabolism, thus compensating for the reduction in D(u)c.

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towards behavioural and physiological responses that producefunctionally favourable outcomes (e.g. Simpson et al. 2004).As illustrated in Fig. 2, it also greatly increases the analyticalpower of a model.

In terms of our stated criteria for models of nutritionalecology, GF clearly is nutritionally explicit, being designed todisentangle the individual and interactive effects on animalsof various food components. It is also organismally explicit,being capable of addressing questions concerning therelationships 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 theways that the nutritional environments of animals impact onphenotypes (downwards arrows in Fig. 1). Less well-developed,however, is its application to questions of how the phenotypesof animals impact on their ecological environments (upwardsarrows in Fig. 1). We return below to the prospects for GFof modelling such questions, and thus qualifying as ecolog-ically explicit sensu stricto.

ECOLOGICAL STOICHIOMETRY

Ecological Stoichiometry (ES) is ‘the study of the balance ofenergy and multiple chemical elements in ecological interac-tions’ (Elser 2006). As suggested by this definition, there aresome interesting parallels between ES and GF (Raubenheimer& Simpson 2004). Like GF, ES grew out of the realizationthat there are complexities to biological systems that cannotbe captured using models based on energy alone (Reiners1986), and therefore frameworks are needed that model theinteractions among multiple currencies – that is, both GF andES are nutritionally explicit, multi-currency frameworks. Alsolike GF, ES is fundamentally integrative, overtly aiming atinterrelating causes and effects across multiple biological levelsfrom ‘molecules to ecosystems’ (Sterner & Elser 2002). A thirdsimilarity between the two approaches relates to the core tenetof ES, the mass balance equation, which applies the laws ofconservation of matter to trophic exchanges in ecosystems.Although couched in the terminology of chemistry (‘stoichi-ometry’), mass balance equations are essentially equivalent to

the nutrient budgets developed in CINE and modelled in GF(Raubenheimer & Simpson 2004). Such parallels have leadsome to consider ES ‘the most recent outgrowth’ of nutritionalecology (Schmitz 2008), while McGill et al. (2006) considerboth approaches to be examples of the kind of ‘studies infunctional ecology that community ecologists would benefitfrom incorporating into their thinking’.

There are also fundamental differences between ES andGF. An important point of distinction relates to the terra

firma of the two approaches. As detailed above, GF wasdeveloped as a multi-currency nutritionally explicit approachto modelling nutritional phenotypes, and the question ofhow well-suited it is for extension to modelling ecosystemprocesses remains open (more on which below). By contrast,the fundamental inspiration in ES relates to the flow of matterand energy through ecosystems (Reiners 1986). Organismsare, of course, a component of ES models – indeed, are centralto these models, as they constitute the primary conduits forthe flow of energy and matter through ecosystems. But thegenerality needed for modelling the effects of interactionsamong organisms on ecosystem processes has been bought byES at the cost of simplifying aspects of phenotypes which arecentral to more-organism-focused approaches. An importantquestion that arises in the present context is how thesesimplifications impact on the extent to which ES modelscan be considered sufficiently organismally explicit to qualifyas a general framework for nutritional ecology.

Organisms are represented in ES models primarily throughtheir body composition, usually expressed as the ratio of keyelements – nitrogen (N), phosphorus (P) and/or carbon (C).Central to predictions of ES are comparisons of the elementalcomposition of consumers and their resources. In accordancewith the law of mass balance, a consumer can maintain itselemental composition only by feeding on foods with similarelemental composition or by specifically increasing the ratesat which surplus elements are excreted – i.e. by decreasingtheir ‘gross growth efficiency’ (GGE) for the surplus elements.In the simplest scenario, the optimal food – considered to bethat which supports maximal production while minimizingwastage (Anderson et al. 2005) – would have identical elementalcomposition 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 mustbe attributed to other factors such as the need to excrete surplus protein (increased D(w)p). (c) Constrained intake without nutrientinterconversion: An alternative response to constrained intake I3. IT, NT and GT are in the same positions as in panels a. and b. However, inthis 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 morelocalized than in the previous examples. The ingested deficit of carbohydrate, combined with inability to reduce D(w)c, results in failure to meetthe nutrient target – Nuo is displaced from NT in the carbohydrate dimension. The animal prioritizes the allocation of ingested carbohydrateto energy metabolism (maintains D(u)c), and as a consequence suffers reduced carbohydrate-derived growth (R(u)c). Furthermore, tomaintain proportional body composition the level of protein allocated to growth (R(u)p) is reduced, resulting in Nuo being displaced from NTalso in the protein dimension. By definition the displacement of Nuo from NT incurs fitness costs, and additionally the animal has anincreased burden of surplus ingested protein to excrete (increased D(w)p). (d) Nutritionally complementary foods: Here the animal hasavailable two nutritionally imbalanced foods, Fb and Fc. However, since the protein-carbohydrate nutritional rails for these foods fall onopposite 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. theseare nutritionally complementary with respect to protein and carbohydrate). One possible intake trajectory is shown by the arrows, in which theanimal 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 andso on. Other patterns might include frequent switches within meals, or several consecutive meals on one food followed by several on the other.

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constraints on the efficiency with which elements can beconverted to body tissue (i.e. GGE < 1) preclude this. There-fore, foods should be weighted for the maximal GGE of eachelement, 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 andmax GGE.y are the maximum efficiency with which theconsumer can convert x and y, respectively, to body tissue. Ifthe above equality does not apply, growth and reproductionof 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 theadoption of elements as the chosen currency. ES studieshave occasionally focused on biochemicals (Anderson 1994;Anderson & Pond 2000; Anderson et al. 2004), but the vastmajority involve elements – indeed, ES has been defined as the‘biology of elements’ (Sterner & Elser 2002). Elements havethe advantages for ecological studies that they are easy tomeasure, constitute a common denominator relevant to allorganisms in ecological communities, and provide a link toinorganic fluxes within ecosystems. They have the disadvan-tage, however, that in terms both of function and mechanism,heterotrophs relate to their nutritional environments not viathe C, N and P, but via heterogeneous molecular complexes ofwhich these elements are components. Elemental analysis willthus predict the food choices, post-ingestive responses andfunctional consequences for a foraging animal only to theextent that they approximate the nutritional value of the

foods. In some cases such correlations likely do apply – forexample, the nitrogen content of foods has been successfullyused in many studies as a proxy for its protein and amino acidcontent – but even here there might be complexities due to thepresence of other nitrogenous compounds and the fact thatproteins vary in their nitrogen content (e.g. Lourenco et al.2002). In other contexts, however, the approximation breaksdown, because two or more functionally distinct molecularcomplexes can yield similar elemental composition. Wherethis is the case, element-based analyses can fall short inpredicting the responses of organisms.

For example, the fractional contribution of differentcarbohydrates to foods has profoundly different nutritionalimplications for herbivores, but is indistinguishable withinstandard ES models (e.g. see Anderson et al. 2004). Anillustration is provided in Fig. 3, which shows data from anexperiment using synthetic foods to investigate nutritionalregulation in locusts (Locusta migratoria). Nutritionalanalysis reveals tight, target-like, homeostatic regulation ofthe balance and amounts of nutrients eaten, a phenomenonthat profoundly influences foraging choices (see for exampleRaubenheimer & Jones 2006). Elemental analysis of the samedata suggested regulation of nitrogen intake, as might beexpected because protein was the only source of nitrogen inthe diet and nitrogen intake thus provided a perfect proxy forprotein intake. There is, by contrast, no apparent regulationof carbon, and elemental analysis would thus fail to detect apowerful predictor of food choice and feeding behaviour. Thereason for the different results is that nutritional analysisreflects the animals’ ingestive responses in distinguishingbetween non-nutritional (indigestible) cellulose and nutritionalsources of carbon (in this case principally sucrose, dextrin,and amino acids), whereas elemental analysis confounds

Fig. 3. Comparison of ingestive regulation of macronutrients and elements. The data, taken from Chambers et al. (1995), represent selectedintake points by fifth stadium locusts, Locusta migratoria . Each point represents the mean selected intake over 6 days of locusts fed one of fourfood 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 theanimals given each pairing had to distribute their feeding between the foods in very different ways to reach the same point of intake, clusteredintake points represent homeostatic regulation. Such regulation is revealed when the data are plotted in terms of macronutrient intake (a), butnot in terms of elements (b).

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carbon derived from these different sources. Different carbonsources likewise have different post-ingestive consequences.Thus virtually no carbon from ingested cellulose is retainedby locusts (i.e. GGE = 0), but surplus carbon derived fromdigestible carbohydrates is associated with increased body fat(GGE > 0) (Raubenheimer & Simpson 1997). The broaderimplication is that in order to derive the GGE for carbon, andhence to evaluate the ‘suitability’ of the food in relation to theconsumer’s body composition (see above), separate GGE’s fordifferent carbon sources would be needed (Anderson et al. 2004).These could only be derived using biochemical analyses.

A second important simplification usually adopted in ESmodels is the emphasis on proportional body composition asa metric against which to evaluate diet quality. For ecologicalstudies this is convenient, as body composition can readily bemeasured and compared across diverse organisms. But itsutility in the context of organismal biology is limited, becausethis approach is based on a methodological classification ofbudgetary terms (eqn 1) and neglects fitness-enhancingcomponents of the ingesta that are dissociated (eqn 2 andFig. 2). For example, ingested carbon that is used to fuellife-supporting energy metabolism is coded only implicitly,as a constraint on max GGEcarbon, and not represented as afitness-relevant component of the ingesta in its own right(Fig. 2). Neither is it distinguished from other categories ofcarbon that are dissociated at max GGE, which might notcontribute to fitness and should thus correctly be classified aswasted rather than utilized carbon (D(w) rather than D(u)in eqn 2) (but see Anderson et al. 2004; Hessen & Anderson2008). Emphasis on proportional body composition alsoneglects a fundamental component of fitness (e.g. Kingsolver& Huey 2008), body size. Thus, standard ES models wouldnot distinguish an animal that achieved optimal growth (e.g.Go in Fig. 2b) from an animal that had the same proportionalbody composition but was overall smaller (Go in Fig. 2c).Finally, emphasis on proportional body composition couldlead to the mistaken conclusion that an animal with depletedfat stores (and hence lower body C : N) requires a lowerproportion of fat in its diet than does a member of the samespecies that is in better condition.

For many ecological applications the element and thebody composition simplifications might be amply justified,because they enable ecological analyses to be performedwhere measurements of macronutrients and functionalcomponents other than body composition might not befeasible. Their success, however, depends on the extent towhich these proxy measures represent the causal variables(biochemistry and overall fitness, including both retained anddissociated components) and in many contexts this will not bethe case. We suspect, in particular, that analyses involvingcarbon will be less reliable than those involving nitrogen andphosphorus. This is partly because, as noted above, carbon isa major dietary component that is spread across severalfunctionally distinct biochemicals (e.g. cellulose, starch,sugars, lipids, amino acids). Furthermore, fuel for energymetabolism comprises a substantial component of ingestedcarbon which contributes critically to fitness but is not

retained in the body (i.e. falls into D(u)), and in standardstoichiometric equations will not be distinguished fromwasted carbon. Finally, the functional implications of excessdietary C are substantially more complex than is implied bysimple comparisons of the composition of consumers andtheir resources (Hessen & Anderson 2008).

A promising development of the stoichiometric approachis dynamic energy budget (DEB) theory (Kooijman 2000).DEB models use differential equations to describe the rates atwhich individual organisms assimilate and utilize energy fromfood for maintenance, growth, reproduction and development,while taking into account constraints on the fluxes of elements.A fundamental construct within DEB theory is the ‘synthesizingunit’ (SU), which is a generalization of the classical enzymeconcept to complex reactions involving more than onepotentially limiting substrate. SU kinetics is used in DEBto model the process whereby ingested substrates are trans-formed into ‘reserves’ that are in turn transformed for growthand metabolic functions (i.e. ‘assimilation’).

DEB thus provides a more fully-specified characterizationof the organism than does traditional ES, founded on funda-mental physicochemical principles. These principles providea powerful means for integrating sub-cellular, organismal andecological processes. It remains to be seen, however, whetherthe DEB abstraction of organisms is sufficiently versatile toprovide a useful platform for nutritional ecology research.An issue that warrants particular attention is the practicaldifficulties of estimating the DEB parameters (van der Meer2006). Further, to achieve the generality aimed at in DEBmodels, species-specific detail is relegated to the ‘residual’,whereas in the organismally explicit approach such details arethe grist that feeds the mill of generalizations. The ability ofDEB to provide insights into the evolution of diverse andcomplex phenotypes has thus yet to be demonstrated (Nisbetet al. 2000). We are currently working with proponents ofDEB to explore these issues further.

Toward an organismally explicit community

ecology

Above we have alluded to the trade-off between species-leveldetail and generality in modelling community-level processes:GF is in terms of organismal detail more highly specified thanis ES, whereas the simplified depiction in ES of nutrients (aselements) and organisms (principally body composition)reduces the burden of species-specific detail and thereby morereadily provides generality. The question we wish to addressin this section regards the extent to which GF models are ableto enlighten population- and community-level processes.

One recent example of an application of GF to a population-level phenomenon is the demonstration that the combinationof protein and salt shortage in the environment, coupled withorganismal regulatory responses to these nutrients, explainsmass migration driven by cannibalism in Mormon crickets,Anubis simplex (Simpson et al. 2006). The question of howGF might reveal the impact of nutritional phenotypes oncommunities has previously been discussed in the literature

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(Raubenheimer & Simpson 2004; Kearney & Porter 2006),and was recently investigated empirically. Behmer & Joern(2008) tested whether co-existence of seven species of generalistgrasshoppers that feed on a similar group of host plants canbe explained on the basis of niche partitioning at the levelof nutrients. Their results revealed significant differencesin selected protein : carbohydrate intake targets between allpairwise contrasts except one. Furthermore, peak performance(in terms of development time and growth rate) correspondedwith the intake points selected by these species, thus linkingnutrient selection to demographic responses. These datasupport the idea that competition for nutrients might driveniche partitioning in the form of divergent intake targets.

Behmer and Joern’s study provides a clear illustration ofhow models of macronutrient regulation and its links toanimal performance may contribute to an understandingof species interactions. We concur, however, with their assess-ment that this study represents a starting point, and sub-stantial conceptual and empirical ground remains to be covered.It is, for example, critically important to maintain a realisticperspective on the role that nutrients play in community-levelprocesses, and thus on the role they should play in modelsof these processes. In particular, ecological interactionstake place between organisms (both consumers and consumed),and not between nutrients, and in most cases the properfocus for the analysis would thus be the organism. Nutrientscan, however, play an indispensible role in explaining andpredicting the interactions among organisms, especially ifthe model is sufficiently organismally explicit to embodythe 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 thecomponent species as competition for nutrients, but inecological terms what is actually being competed for is notnutrients but foods (in this case plants). Thus, two herbivorespecies that had widely dissimilar intake targets mightnonetheless come into direct competition if they relied ondifferent parts of the same plant – for example, seeds vs. leaves.In this case the unit being competed for is plants, and nutrientsare relevant only in so far as they constitute the functionalreason for that competition. Any model that does not considerthis fails to meet the criterion of ecological explicitness, andfalls into the same trap as do element-based models of organismalresponses: they represent an inappropriate level of reduction.

With this caveat in mind, we believe that models whichare both nutritionally and organismally explicit have consid-erable potential to enlighten community-level processes. InFig. 4 we present an example that explores this potential bymodelling in the context of food webs interactions betweenmacronutrient balance, body composition, and energeticmaintenance requirements. The model, which follows thesame structure as Fig. 2, demonstrates several points:

1. As trophic levels are ascended, homeostatic feeding andgrowth responses will progressively limit the range ofbody compositions and shift the mean composition

towards a higher protein ratio (Fig. 4d). This wouldexplain the observations from ES that N% rises acrosstrophic levels, and that the ratio of carbon to nitrogenbetween foods and consumers (C : N resource/C : N con-sumer) narrows progressively moving up trophic levels(Denno & Fagan 2003; note the similarity between fig. 2cin that article and Fig. 4d in our article).

2. Consumers become progressively carbohydrate and fatlimited across trophic levels – not nitrogen limited as sug-gested by ES (Denno & Fagan 2003). Thus the nutritionalincentives to feed down the food chain, rather than up thefood chain (Denno & Fagan 2003), become greater at highertrophic levels as metabolic energy becomes increasingly scarce.

3. Collectively, these nutritional effects, in conjunction withthe progressive loss of energy across trophic levels (thetrophic pyramid effect), might explain why food webs aretypically limited to fewer than 4–6 trophic levels. We areexploring this possibility further at present.

In closing, we note that both the model in Fig. 4 and thestudy of Behmer & Joern (2008) represent ‘as is’ applicationsof GF to questions of community ecology. To achieve a broaderapplicability, GF would need to be extended to capture thespatial and temporal dynamics of ecological communities. Thisproject is already underway (Simpson et al., under review).

Conclusions

We have suggested that an over-arching framework fornutritional ecology would be nutritionally, organismallyand ecologically explicit, and should be heterarchically struc-tured. Numerous frameworks have been applied withinnutritional ecology but, like OFT, most are based on single-currency models and are thus not nutritionally explicit. CINEwas influential in highlighting the need for a nutritionallyexplicit approach, although it failed to produce a modellingframework for dealing with multiple currencies. ES, in contrast,has provided a useful multi-currency tool for ecologicalstudies, but its focus on elements and on body compositionas a proxy for fitness has reduced its utility for organismalstudies. DEB theory more fully specifies phenotypes thandoes ES, but faces challenges of parameterization andincorporating species-specific detail. GF, on the other hand,was developed as a nutritionally explicit approach fororganismal biology, but its potential to contribute to com-munity ecology has yet to be proven. While each paradigmhas yielded valuable contributions in their own right, webelieve that an over-arching framework for nutritionalecology could only be achieved by combining aspects of theseapproaches. 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 biochemicalsrather than elements (Anderson 1994, Anderson & Pond 2000;Anderson et al. 2004). Simpson et al. (2004) incorporatedinto GF the a priori predictive approach on which OFT is

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founded, and Raubenheimer & Simpson (2004) exploredsimilarities and contrasts between ES and GF. More recently,Behmer & Joern (2008) have applied GF in an empirical studyto questions of community ecology, and in the present articlewe have explored some implications of geometrical analysisfor theoretical ecology. We consider further expansions/syntheses of existing models to be a priority goal, which standsto benefit nutrition, ecology and nutritional ecology alike.

Acknowledgement

Authors thank Tom Anderson, Mike Kearney, Carol Boggs, Spencer Behmerand two anonymous referees for useful suggestions which improved this article.

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Received 12 July 2008; accepted 17 November 2008

Handling Editor: Carol Boggs

Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Table S1. Some examples of published applications of theGeometric Framework.

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