Nutritional indicators and their uses in ecology



The nutrition of animal consumers is an important regulator of ecological processes due to its effects on their physiology, life-history and behaviour. Understanding the ecological effects of poor nutrition depends on correctly diagnosing the nature and strength of nutritional limitation. Despite the need to assess nutritional limitation, current approaches to delineating nutritional constraints can be non-specific and imprecise. Here, we consider the need and potential to develop new complementary approaches to the study of nutritional constraints on animal consumers by studying and using a suite of established and emerging biochemical and molecular responses. These nutritional indicators include gene expression, transcript regulators, protein profiling and activity, and gross biochemical and elemental composition. The potential applications of nutritional indicators to ecological studies are highlighted to demonstrate the value that this approach would have to future studies in community and ecosystem ecology.


The nutrition of animal consumers varies widely within and among ecosystems. Poor nutrition alters many aspects of consumer performance (e.g. growth), behaviour (e.g. foraging rate and strategy) and their ecological interactions within food webs (Sterner & Elser 2002; Frost et al. 2005; Raubenheimer et al. 2009). For example, poor food quality can slow the growth of aquatic consumers, delay their reproduction and reduces their reproductive rates, alter the quality of their offspring, and increase their mortality rates (Frost et al. 2005). Within and among ecosystems, nutrition interacts with the traits and the diversity of organisms in ecosystems to determine the rates and ratios of nutrient cycling (Elser & Urabe 1999, Hooper et al. 2005). As this important role of nutrition in ecology is relevant across levels from organisms to ecosystems (Dodson 1998), determining the causes and consequences of poor animal nutrition on food webs and ecosystems should be of high priority (Frost et al. 2002; Raubenheimer et al. 2009).

The evaluation of the nutritional status of consumers has generally relied on imprecise and non-specific response variables. One indication of whether food is providing adequate nutrition is found by comparing the nutrient content of food to a consumer's body content (Sterner & Schulz 1998; Hillebrand et al. 2009a). A similar but more refined approach compares food nutrient content to consumer threshold elemental ratios calculated using assimilation efficiencies, metabolic losses and body content of multiple elements (Urabe et al. 1995; Sterner & Elser 2002; Frost et al. 2006). While both of these approaches assume consumer growth is constrained by relatively low nutrient content in food, neither provides direct evidence of whether the nutrient of interest is actually limiting consumer performance. In addition, these approaches rely on parameter estimates (e.g. body nutrient content and assimilation efficiencies) that are also sensitive to low food nutrient content (Frost et al. 2006). Comparisons of food nutrient content and consumer nutrient requirements are further open to the criticism that they can neither definitively identify nor characterise diet quality of consumers due to spatial variability in food sources, variable food quality through time, flexible nutrient assimilation efficiencies and/or selective feeding by consumers on nutrient rich food sources (Frost & Elser 2002).

More direct assessments of consumer nutritional state involve growth bioassays that measure growth and/or reproduction of animals provided with foods that vary in nutrient content (e.g. Elser et al. 2001; Lukas et al. 2011). These assessments usually use food carefully formulated under laboratory conditions (e.g. Sterner et al. 1993; Von Elert 2002) or collected from different ecosystems (e.g. Tessier & Woodruff 2002), which are suspected to vary in some nutritional component. The identification of nutritional constraints using dietary supplementation approaches have also been widely employed and are relatively straightforward in identifying limiting dietary compounds (e.g. Sperfeld et al. 2012). Although these approaches are valuable, they usually rely on a response variable (i.e. growth) that can respond to a low supply of any nutrient, require laboratory culturing of food and/or animals and are potentially confounded by changes in food or environment that occur during laboratory or field manipulations. Given these criticisms of both food-animal comparisons and growth bioassays, there is an apparent need for complementary indicators, which can provide an independent and in situ characterisation of consumer nutritional state. The goal of complementary indicators would be to quantitatively assess the nutritional status of a consumer and thereby eliminate uncertainty created by the imprecise and non-specific methods described above. Here, we present the case that the molecular and biochemical responses of consumers to poor food quality can provide the next generation of nutritional indicators and will play an important role in the further development of nutritional ecology.

The ideal nutritional indicator

The development of a reliable and precise nutritional indicator could start by considering what organismal characteristics would avoid or limit the previously described criticisms. At a minimum, an indicator should show a strong and rapid monotonic (increasing or decreasing) response to a single type of nutrition limitation experienced in a consumer. Nutrient-specificity is desired as it would permit individual and interactive roles of multiple nutritional resources to be identified and separated. Indicators that are specific to animal taxa or particular consumers would be useful when separating responses of animals from ingested food or other non-target taxa. Finally, nutritional indicators would ideally be measured directly on the consumer of interest, be relatively non-invasive or non-lethal for larger organisms, require minimal quantities of animal tissues or body mass, and be of low cost and methodological simplicity. One can imagine a suite of such complementary indicators for each limiting nutrient, more or less fitting some or all of these criteria, which could provide a thorough characterisation of the type(s) and severity of nutritional limitation being experienced by an animal.

Identification and development of nutritional indicators

A potential source of nutritional indicators that possess many of these ‘optimal’ characteristics is based on the physiological responses (i.e. changes in metabolic pathways) of animal consumers to nutrient limitation (DeMott et al. 1998; Frost et al. 2005; Mutch et al. 2005). Specifically, the molecular and biochemical pathways involved in nutrient uptake, incorporation and mobilisation would represent a potential source of novel indicators (van Ommen & Stierum 2002). Similar to nutrigenomics approaches used in systems biology (e.g. Ruffel et al. 2010), nutritional indicators would include responses and regulators of gene expression, quantity and types of proteins and their activity, and biomolecular and/or elemental content of specific tissues or the whole organism (Table 1).

Table 1. Potential indicators of animal nutritional state
TypeExample response variableAnalytical methods/toolsConsumer-specificNutrient-specificAdvantagesDisadvantagesExamples
  1. Abbreviations: N; No, Y; Yes, P; Probable.

Transcript profilingTranscriptomicscDNA, pyrosequencingYYHigh results probabilityCost, complexityBoer et al. (2003)
Specific gene expressionqRT-PCRYPLow mass requirementsAmong consumer-specificitySugiura et al. (2003)
Transcriptional/Translation regulatorsmicroRNA expressionqRT-PCRYYLow mass requirementsAmong consumer-specificityReferences within Scheible et al. (2011)
Protein profilingProteomics2-D gel electrophoresisNYHigh results probabilityCost and sample preparationGivskov et al. 1994
Specific protein contentELISA/Western blotting with use of specific antibodiesNPEnsures only one enzyme/ protein is measuredPotentially consumer-specificLaRoche et al. (1996)
Enzyme activity




NPCost effective, simple methodologyNeed to isolate enzyme of interestMcCarthy et al. (2010); Wagner & Frost (2012)
Lipid ProfilingLipidomicsESI-MSYYHigh results probabilitySample preparationHan & Gross (2003)
Steroid hormones, eicosanoidsImmunoassays, LC-MS, GC-MS, ESI-MSYYHigh results probabilitySample preparationMartin-Creuzburg et al. (2007); Han & Gross (2003)
Cellular membranesPhospholipidsGC-MSNYHigh results probabilitySample isolation, consumer-specificityVan Mooy et al. (2009)
Specific lipid content



NPHigh results probabilityConsumer-specificity to changesGuschina & Harwood (2009)
Metabolite compositionMetabolite profiling



NYHigh results probabilityMass requirements, complexityBoer et al. (2010)
Specific metabolite contentHPLCNPHigh results probabilityInterference from gut contentsTweeddale et al. 1998)
Biomolecular contentDNA : RNA ratiosFluorescence determinationNNMass requirements


Baseline knowledge needed

Elser et al. (2000); Wagner et al. (1998)
%Lipids, proteins, carbohydratesColorimetric determinationNNEase of measurementInterference from gut contentsSterner et al. (1992)
PhysiologyElemental content

UV/Vis Spec

Colorimetric determination

NNEase of measurementInterference from gut contentsElser et al. (2001); Wagner & Frost (2012)
Growth rate

Length-weight regressions

Indication of mass

NNEase of measurement


Inference with gut contents

Sterner et al. (1993); Frost & Elser (2002)
Nutrient release rates

UV/Vis Spec

Colorimetric determination

NNEase of measurementInterference from gut contentsUrabe et al. (1995)

The development and use of nutritional indicators could follow a common approach: 1) profiling of system responses to nutritional stress, 2) identification of combinations of response variables unique to each nutrient and 3) careful validation of nutritional profiles in animals. During the initial phase, the identification of nutritional indicators would clearly benefit from emerging information-rich fields (e.g. genomics, transcriptomics, proteomics) due to their ability to profile systematic changes in consumer metabolism in response to nutrient deprivation (van Ommen & Stierum 2002). Profiling or descriptive shotgun studies of consumer nutrition stress can capture whole-scale responses of organisms to different nutritional stressors (Tanzer et al. 2003). Characterisation of such system-wide responses has permitted the isolation and use of specific indicators that are intimately connected to different nutrients of interest in studies of plants and microbes (Table 1).

Although large quantities of data are produced by ‘-omic’ approaches, data reduction techniques could be employed to identify unique responses to each nutritional stressor. Data reduction techniques, including multivariate statistics (e.g. principle components analysis) or nonlinear black box modelling techniques (e.g. artificial neural network), are typically employed to identify unique stress indicators (Lancashire et al. 2009). For example, identification of nutrient-specific changes from system wide responses could follow the approach described by Boer et al. (2003, 2010), who used cluster analysis to identify unique gene expression responses to specific forms of nutrient limitation. The precise growth limiting metabolites can also be identified when they exhibit positive relationships with the external supply of the nutrient in question (Boer et al. 2010). These techniques could be used separately or together to identify nutrient-specific (‘key’) indicators that could then be used to diagnose the strength and nature of consumer nutrient limitation.

Following their identification, potential nutrient-specific indicators would require further validation by examining their responses to different degrees of nutrient-stress and to limitation by other nutrients. This validation process would also need to verify the nature and strength of responses in different animal genotypes because genetic heterogeneity among individuals or populations could produce different responses to the same type or degree of limitation. In addition, some indicators may vary among ontogenetic stages as nutrient demands change with life-history stage or due to changing metabolic demands for nutrients. The nature of this ontogenetic plasticity would need to be examined and accounted for prior to wide scale or routine use of this approach. Some nutritional indicators have already seen wide use within and among animal taxa, which indicates that these potential complications may not pose irremediable obstacles to the development and application nutritional profiling. For example, the blood content of haemoglobin and ferritin are widely used as indicators of iron status in humans and in other mammalian taxa (National Research Council 1979; Denic & Agarwal 2007) despite considerable potential for ontogenetic, population and species level variability in these indicators. In addition, nutritional indicators would need to be examined for their interactions with confounding environmental variables. Such environmental sensitivity would require special consideration given the wide physico-chemical conditions animals experience within and among the diverse ecosystems of Earth. After validation, biochemically based indicators could be added routinely to studies of the nutritional ecology of animal consumers (Fig. 1).

Figure 1.

Linkages among the nutritional environment of consumers, internal cellular and physiological processes controlling animal nutrient use efficiency and the role of nutrition in ecology.

Once developed and validated, nutritional profiles could be used to carefully track the form and intensity of nutritional limitation in animal consumers. A nutritional profile would necessarily contain multiple indicators to aid in the differentiation of various potential forms of nutrient limitation (i.e. food quantity, nitrogen, phosphorus or fatty acid/sterol limitation). Ideally, these sets of indicators would show high nutrient specificity of their responses, be relatively invariant to variability caused by ontogeny or genetics, and exhibit minimal or predictable changes to changes in the ambient environment. Provided these conditions are met, profiles derived from animals consuming unknown diets could be quantitatively compared with the data matrix produced by known types of nutritional limitation. This type of analysis would assess the similarity between the nutritional profile of animals consuming known diets and that of the unknown consumer of interest. This approach would thus generate likelihood statements about which element is limiting the animal under study and thereby provide a quantitative assessment of an animal's nutritional state.

Nutritional Indicators

The development and application of biochemically based nutritional indicators will involve the measurement of organismal responses (e.g. metabolomics) that may not be commonly used or familiar to many ecologists. Here, we present brief descriptions of molecular and biochemical responses that could provide important information regarding the nutritional state of animal consumers. For each, we review the nature of these response variables of interest and explain how each connects nutrition and metabolism. We further present examples of how these biochemical responses have been used in previous studies that examined links between nutrient supply and the ecology of different species. Although this work has largely been completed on non-animal taxa (i.e. microbes and plants), it nonetheless illustrates the potential usefulness and value of this approach for those studying animal consumers. We also briefly highlight the benefits of and potential constraints to the application of each class of indicator to ecological studies of nutrition.

Gene expression

Patterns of gene expression could be a rich source of information on the nutritional state of animal consumers. Previous work with model organisms, including Escherichia coli (Hua et al. 2004), Saccharomyces cerevisiae (Boer et al. 2003; Wu et al. 2004), Arabidopsis thaliana (Morcuende et al. 2007) and Daphnia pulex (Jeyasingh et al. 2011), has demonstrated strong links between nutrition and gene expression. These studies have all identified groups of genes that are highly induced or suppressed by a single type of nutrient limitation. For example, there were 484 nutrient-specific gene expression responses in yeast (S. cerevisiae) subjected to low supplies of glucose, N, P and sulphur (Boer et al. 2003). Many of these gene-based indicators of nutrition involve nutrient transport, regulation of nutrient fluxes and pools (e.g. increases in amino acid transporters), and mobilisation of stored nutrients mainly through increased catabolism (Boer et al. 2003).

Given the considerable number of genes that appear linked to nutrient stress, it seems likely that at least a small number of individual genes could be identified and used to diagnose the nature and strength of animal nutrient limitation (Table 1). This approach of tracking the expression of a single or few genes is currently being used to provide evidence of nutritional limitation in fish and has been advocated for its potential uses to aquaculture (Panserat & Kaushik 2010). Specifically, greater relative expression of sodium phosphate co-transporter genes has been linked to P-limitation in rainbow trout and provides strong evidence of this form of nutrient limitation in fish growing in aquaculture (Sugiura et al. 2003). Beyond this, patterns of gene expression are being used more widely to study the role of nutrients in ecology. For example, aquatic consumers have recently been found to increase the production of digestive proteases in response to cyanobacterial food sources (Schwarzenberger et al. 2010) and to increase the expression of genes underlying the eicosanoid synthesis pathway in response to higher fatty acid content of their diet (Schlotz et al. 2012). As the tools to measure these response variables become more common, nutrient-specific indicators based on gene expression will be of considerable utility to ecologists.

Translational regulators

Similar to gene expression, translational regulators are a potential source of nutritional indicators. Translational regulators can modify the amount of protein produced per gene transcript and can be quite responsive to organismal nutrition. In particular, several microRNAs (miR) have been found to be robust and nutrient-specific indicators of nutritional stress in plants (reviewed in Scheible et al. 2011). The best described example is miR 399, which has only been found in strongly P-limited plants and shows no cross-reactivity with other nutritional stressors (Scheible et al. 2011). While miRs have also been used to study stress responses in animals, whether they respond to nutrient stress in consumer taxa has yet to be determined. Nutrient-specific miR responses in consumers, if found, would be extremely valuable to ecologists because miR are highly conserved within and among taxa (Sempere et al. 2006; Wheeler et al. 2009). The conserved nature of miRs would allow for the examination of nutrient stress in different animal taxa with little to no susceptibility to contamination by plant food material (Pasquinelli et al. 2000; Sempere et al. 2006).

Protein composition and activity

Proteins represent another potentially rich source of indicators of nutritional stress in animal consumers. Within consumers, poor diet is known to change proteins involved with nutrient uptake, incorporation and storage, and degradation and recycling of cellular structures (Kolkman et al. 2006). These changes are well-documented in many organisms experiencing nutrient stress and have been used to infer their nutritional state under natural conditions. For example, in Pseudomonas putida, unique protein composition was found in cells grown under C-stress compared with those grown under N or P-stress (Givskov et al. 1994). Following the identification of such nutrient-stress proteins, nutritional indicator profiles could be developed for routine use in ecological studies. This approach has been particularly useful in the study of iron-limitation of phytoplankton. For example, under Fe-limitation, diatoms replace the Fe-containing electron transport protein, ferredoxin, with a flavin containing protein, flavodoxin (LaRoche et al. 1996). In Lake Superior, greater flavodoxin levels are present in offshore areas, which indicate more intense Fe-limitation of diatoms in these areas (McKay et al. 2004). Although protein-based indicators require some methodological development, this approach could ultimately be extremely cost effective and involve minimal complexity, which would increase its usefulness for studies of nutritional studies of animal consumers and their ecological interactions.

The activities of enzymes are another potential indicator of consumer nutritional status. Enzyme activity has been routinely used to detect nutrient-stress in phytoplankton (e.g. North et al. 2007) and bacteria (e.g. Sala et al. 2001), and, more recently, has been used to study aquatic consumers (Elser et al. 2010; McCarthy et al. 2010; Wagner & Frost 2012). Elser et al. (2010) used this approach to study the intensity of P-limitation in zooplankton communities grown under different levels of N-loading. They found an increased alkaline phosphatase activity in Daphnia associated with higher N : P ratios in their food. Enzymatic indicators benefit from the existence of many well-developed enzyme assays that could be readily adopted for in situ studies. On the other hand, this approach may be confounded by enzyme cross-reactivity to other substrates and suffer from a lack of nutrient-specificity. For example, Wagner & Frost (2012) found apparent increases in alkaline phosphatase activity in P-, N- and food-limited Daphnia, which plausibly resulted from the cleavage of fluorometrically labelled substrates by other non-P sensitive enzymes. Nevertheless, enzyme activities could be an important complementary component of animal nutritional profiling and its use in ecology.

Metabolite analysis

The composition of metabolites could provide valuable information of the type and intensity of nutrient stress in animal consumers. Metabolite responses to nutrient stress are well-described in plants (Bölling & Fiehn 2005), bacteria (Yuan et al. 2009), and yeast (Boer et al. 2010). For example, a few key metabolites respond strongly to glucose-, N- and P-limitation in yeast with different metabolite responses observed among the different types of nutrient limitation (Boer et al. 2010). Specifically, pyruvate, glutamine and adenosine triphosphate were found to be nutrient-specific growth limiting metabolites in glucose-, N- and P-limited yeast, respectively (Boer et al. 2010), which means they each decreased substantially in cells experiencing each respective type of nutrient limitation. One major advantage of using metabolite composition is that it can be applied to any consumer with little method development (e.g. no requirement to design consumer-specific primers or produce protein-specific antibodies), which increases its immediate applicability to different animal taxa and nutritional stressors. However, the distinct disadvantage of this method is that without consumer gut clearing or the analysis of specific tissues, food material can contribute metabolites to the sample of interest and could confound easy interpretation of resulting data.

Lipid analysis

Lipids are a well-known dietary requirement of animals for growth and development. For example, some animal taxa have a restricted ability to synthesise sterols or ω3 and ω6 polyunsaturated fatty acids (PUFA) de novo (e.g. Stanley-Samuelson et al. 1988). The quantity and relative composition of these structurally and functionally diverse lipids in consumers could provide information on the nature and intensity of nutritional limitation. Although our knowledge of the underlying molecular mechanisms is rather poor, the responses of polar and non-polar lipids, fatty acids (FA) and sterols to different types of nutritional stress in algae have been systematically examined. Under both nutrient-limiting conditions and at high light intensities, non-polar lipids and saturated FA accumulate in algal cells (Guschina & Harwood 2009). In contrast, sterols respond to light-nutrient interactions in algae such that they increase with light intensity under low nutrient supply but decrease with increasing light intensity under high nutrient supply (Piepho et al. 2010). In animal consumers, the ratio of non-polar storage to polar structural lipids in whole bodies or isolated tissues would likely closely match food quantity (Elendt 1989). More specifically, the composition and relative quantities of FA and sterols in animal consumers may illuminate the type and strength of biochemical limitation (i.e. either direct FA or sterol limitation) or mineral limitation.

Biomolecular content

Although recent advancements in molecular biology provide an array of novel and extremely promising nutritional indicators, previously developed and currently available biochemical responses should continue to be considered for use in ecological studies. These responses would include the biomolecular composition (e.g. protein, lipids, DNA and RNA) of tissues and organismal bodies (e.g. Fig. 2). Although responses of biomolecular pools are unlikely to be entirely nutrient-specific, they could nonetheless assist with the diagnosis of specific types of nutrient limitation. For example, low lipid content, high free amino acid content and low body %C would together indicate limitation by food quantity. The application of such approaches, in an absolute sense, would likely require species-specific information on maximum growth rate and biochemical composition during non-limiting conditions. For example, relatively fast growing animal taxa may exhibit relatively large changes in RNA : DNA ratios under P-limitation, whereas this ratio may be less sensitive in slower growing taxa (Elser et al. 1999). Given differences in clones/species or life-history strategies and the potential lack of nutrient-specificity, the usefulness of these biomolecular data alone as indicators of nutrient stress may be limited. However, the ease and low cost of sample analysis nevertheless will continue to make these responses a viable complementary source of nutritional information.

Figure 2.

Putative nutritional profile of an animal consumer responding to acute dietary phosphorus stress. Listed are complementary molecular, biochemical, biomolecular and physiological responses that dictate increase nutrient use efficiency. See Supplementary Information for more detailed information.

Physiological processes

Key physiological traits involved in nutrient metabolism could also be used as complementary indicators of nutrient stress (Fig. 2). Nutrient-poor food is well known to alter metabolic rates, digestive and absorptive efficiencies and rates and ratios of nutrient excretion and egestion (Sterner & Elser 2002; Frost et al. 2005). For example, DeMott et al. (1998) found lower body P content, increased P assimilation, and reduced P excretion in Daphnia grown with P-deficient diets. When appropriate, addition of these types of response variables to ecological studies of nutrition would strengthen conclusions regarding the identity and severity of elemental limitation. Physiological processes involved in nutrient metabolism (e.g. assimilation efficiencies or nutrient release rates) would be particularly useful in determining animal nutritional state. The value of these measurements is that they are already quite commonly used, are of low cost, and are relatively easy to interpret. On the other hand, some of these variables (i.e. growth and respiration) are known to respond to all forms of nutritional limitation, show low or no consumer-specificity, and are of less value when used alone in the absence of other nutritional indicators.

While much insight into the nature and type of nutrient-specific responses described above is from past work examining bacteria, yeast and plants, this information nonetheless can be used to illustrate how metabolic profiles of animal consumers could be altered by nutritional stress and to create a detailed nutritional profile (Fig. 2; Supplementary Information). Together, systematic compilations of these molecular responses, such as described here, will provide greater and more defensible conclusions regarding the nutritional status of an animal consumer whether collected from nature or used in a lab experiment.

Application of Nutritional Indicators to Ecological Studies

There is growing recognition within the field of ecology of the fundamental importance of the relative availability of multiple resources cycling within and among ecosystems (Sterner & Elser 2002). For example, consumer-food mismatches in nutrient content alter individual (e.g. feeding behaviour) and population-wide (e.g. grazing rates) processes (Frost et al. 2005; Hillebrand et al. 2009a). Despite the broad importance of understanding the role of nutrients in ecosystems, methods used to evaluate the degree and type of nutritional limitation experienced by animal consumers remain relatively simple and unrefined. Improved nutritional indicators are thus needed and would add value to many types of ecological studies. Nutritional indicators would be of considerable use in determining how nutrient supply ratios alter ecological dynamics by way of changes in animal physiological condition and performance (Fig. 1).

Occurrence of nutritional limitation

One area that nutritional indicators could immediately be applied is the determination of the type, frequency and strength of nutritional limitation of animals in situ. Previous determinations of nutrient limitation in consumers have relied on indirect inferences based on stoichiometric differences between food and tissue (e.g. Frost & Elser 2002) and/or animal growth responses in bioassays (e.g. Müller-Navarra et al. 2000). Although these approaches have seen wide use, their ability to differentiate among multiple forms of nutritional limitation has been repeated questioned (Hartwich et al. 2012). As animals ingest resource packages (in contrast to plant uptake of single resources), consumers may face frequent co-limitation by multiple nutritional components in their food (Lukas et al. 2011; Sperfeld et al. 2012), especially when these nutrients strongly co-vary in food material (Hartwich et al. 2012). Nutritional indicators would provide a means to study the relative importance of co-varying nutritional components. For example, divergent responses of consumers to P-, specific types of FA-, sterol- or amino acid-limitation could provide a defendable and relatively easy means to separate these types of nutritional limitation (Wacker & Martin-Creuzburg 2012). Indicator profiles could thus quickly illuminate which nutritional components, alone or in combination, are limiting consumer performance. Detecting resource-specific limitation signals would also provide a method to determine the general applicability of multiple resource limitation to consumers as in autotrophs (Harpole et al. 2011). In addition, this approach could be applied to individual animals, allowing for the assessment of variability in nutritional state within populations. Substantial variability among animals would indicate fine-scale heterogeneity in food sources or foraging success and could be included in ecosystem models that often consider only the average nutritional state of animals within populations. Variability in nutritional profiles associated with genotype or species would be of interest in and of itself as it would provide evidence for the evolution of differential sensitivity to poor food quality. If found, this inter- and intraspecific variability in nutrient stress would be a fertile territory for future studies aiming to link local scale selective pressures created by dietary stress to population genetics and the consequences on phenotypic variation among populations. At larger scales, studies could use this approach to better assess the nutritional states of consumers and their populations through time (e.g. within a lake or field across seasons) or across space (e.g. among streams or forest plots across resource gradients).

Descriptive data on the frequency and severity of animal nutritional stress could also be used in studies examining the environmental drivers of animal nutrition. In particular, nutrient supply ratios, plant taxonomic composition, and other important environmental variables (e.g. temperature) all can affect the quantity and quality of food available to consumers, which could be verified using a nutritional profile to assess the nutritional state of consumers. Nutritional indicators could also be used to examine the propagation of nutrient limitation signals across trophic levels. With an experiment on marine pelagic food webs, Malzahn et al. (2007) found that predators responded to the nutrient content of the algae consumed by their herbivorous prey even though the body stoichiometry of the herbivores did not change. Nutritional indicators, in this case applied to herbivores and predators, would provide an improved evaluation of proximate nutritional mechanisms that transfer resource limitation up through the food web (Fig. 1).

Linking nutrition to organism physiology

The development of nutritional indicators would also feed into better and more complex experimental designs aimed at understanding how nutritional limitation affects consumer physiology, life history and behaviour. For example, the study of acclimation to poor nutrition by animal consumers would be improved by nutrient-specific indicators. The measurement of key metabolite fluxes would add great value to time scale experiments and provide a better understanding of how nutrient limitation affects consumers. In addition, nutritional profiling would be useful for studies of consumer feeding rates and food selection, mass conversion, and for determining how fluctuating food quantity and quality affects the growth and reproduction of consumers in natural ecosystems. For example, one could determine if nutrient stress occurs during short periods of low food quality, even if growth rates are not affected (e.g. Hood & Sterner 2010). This decoupling of nutrient stress and growth rates would imply that there are compensatory physiological processes and changes in mass conversion efficiency. The mechanisms linking food quality and optimal foraging could also be studied using nutritional indicators. For example, one could take simultaneous and repeated measurements of food quality, ingestion rates, assimilation efficiencies and nutritional profiles. Such data would show whether changes in the downstream nutritional state of the animal precede or simply follow alterations to feeding behaviour caused by food of declining quality. The use of nutrient-specific indicators would also aid in studies of consumer habitat selection, differential use of resource patches of varying quantity and quality, and selectivity for certain food items.

Indicators of animal nutritional state could also be used to generate an improved mechanistic understanding of nutrition with the metabolic theory of ecology (MTE; Brown et al. 2004). MTE seeks to understand ecological processes through the use of metabolic scaling of individuals and populations primarily as a function of temperature and body size. Plasticity in metabolic scaling appears common (Glazier 2005) and recent work suggests nutritional limitation can change mass-metabolism scaling (see Jeyasingh 2007). Most simply, nutritional indicators could be used to show whether scaling changes systematically with the type and strength of dietary stress in consumers. Alternatively, nutritional profiles of animals in situ could be used to adjust the application of metabolic power laws to derive population-level parameters. Integrated nutritional profiling would provide an avenue to determine the cause of variable metabolic scaling either under laboratory (e.g. Jeyasingh 2007) or field (e.g. McFeeters et al. 2011) conditions.

Ecological interactions

Nutritional indicators could also be used to improve studies of the population dynamics of animals and their interactions in food webs. Although changes in food quantity and quality affect key demographic parameters, the translation of these effects into population growth/decline depends on the interactions with other factors such as mortality from predators and disease (Dodson 1998). Indicators applied to multiple species could help sort out how competitive abilities change with nutritional state and affect interactions among similar species. Specific indicators could thus address the question of whether apparent differences in consumer nutrient requirements actually translate into different competitive abilities and altered fitness at population scales. In this regard, competition experiments among two or more taxa could be improved by better tracking of the nature and severity of nutritional stress in multiple consumer species through time. For example, two common zooplankton species, Daphnia and Bosmina, appear to have different requisite P requirements and sensitivity to low P food (Schulz & Sterner 1999). In competition experiments, one might expect that the high P requirements of daphnids would produce more frequent and intense periods of P-limitation in these animals under conditions of low external P supply and thereby provide a competitive advantage to relatively P-poor Bosmina.

Nutritional indicators could be incorporated into studies that consider how populations or communities respond to variable resource supply ratios. For example, they would be well suited to determine whether coexistence of multiple consumers on single prey items can result from the limitation by separate nutritional components (Loladze et al. 2004). In this example, different species would show limitation by different nutrients even though they are consuming the same prey item. Additional ecological questions of interest that link nutrients and trophic interactions would also be amendable to this approach including: does reduction of consumer density by predation yield decreased limitation by food quantity and greater constraints by poor food quality? Or how does host nutritional quality alter parasite population establishment and growth? For these types of studies, nutritional profiles would provide information about the nature and strength of limitation in changing population and communities as they respond to varying food quantity and quality.

Ecosystem processes

Nutritional indicators and their application to consumers in situ would provide new information on the controls of ecosystem structure and function. The flux and storage of matter, including the nutrient ratios and the rates of nutrient cycling, appear to strongly depend on the traits and the diversity of organisms in ecosystems (Hooper et al. 2005). Nutritional profiling would enable ecologists to more quickly and easily link functional traits affecting consumer nutritional demand and status to their effects on the movement and retention of elements in ecosystems (Fig. 1). Recent evidence from autotrophs suggests that 1) resource availability and resource ratios constrain both biodiversity and biomass production and 2) increased producer biodiversity subsequently enhances the retention and transfer efficiency of available resources into new production (Cardinale et al. 2009; Hillebrand & Lehmpfuhl 2011). Similar work has not been completed thus far for consumers, which precludes assessing whether nutritional complementarity is intimately tied to consumer biodiversity or determines how nutrients affect emergent ecosystem processes. This particularly includes understanding biodiversity effects on nutrient recycling, which have been predicted (McIntyre et al. 2007) and described (Hillebrand et al. 2009b), but remain poorly linked mechanistically to the nutrient status of consumers. One could also determine how the strength and nature of nutritional limitation varies between herbivores and detritivores, who often encounter distinctly different types of food (Olff et al. 2009). Such work would help explain variable rates of organic matter processing and storage in ecosystems. Ultimately, nutritional indicators could be used to determine how food quality constraints on individual consumers or their populations affect the movement of multiple elements through the environment.


The use of nutritional indicators will advance the study of ecology by providing more explicit and greater evidence of a consumer's nutritional state. There are strong and specific effects of poor nutrition on cellular and molecular mechanisms underlying animal metabolism, which will become easier to identify and study using rapidly advancing information-rich fields of study in biochemistry and molecular biology. Nutritional ecology is poised to benefit from greater and more ambitious use of these approaches by integrating and adopting this information to devise novel and precise methods of studying consumer nutritional state. The greater ability to study relationships between food quantity and quality, consumer nutrition and performance, and ecological processes promises to revolutionise the study of ecology and its nutritional bases.


This study was supported by funds provided to PCF by the Natural Sciences and Engineering Research Council of Canada and the Alexander von Humboldt Foundation. AW thanks the German Research Foundation (DFG) for funding of his research. Thanks to Steven Rafferty and Clay Prater for critically reviewing this manuscript and to Colleen Middleton for the line drawings in Fig. 2.


All authors contributed to the writing and editing of the manuscript.