The stoichiometry of consumer-driven nutrient recycling: theory, observations, and consequences

James J. Elser


Ecological stoichiometry is an approach that analyzes the constraints and consequences of mass balance of multiple chemical elements in ecological interactions (Sterner 1995, Elser et al. 1996, Hessen 1997). Reiners (1986) was among the first to articulate stoichiometry as a complementary model of ecosystem function, potentially supplementing and extending insights from ecological energetics, which has been the dominant mode of biophysical analysis in ecology since the days of Lindeman (1942). Central to the application of stoichiometric thinking in ecology is the realization that biological entities (such as molecules, organelles, cells, and organisms) can differ considerably in terms of their elemental composition and that these differences are fundamentally linked to important aspects of ecological function (Elser et al. 1996). Thus, the ways that organisms interact with each other and with their abiotic environment can be strongly and reciprocally influenced by the elemental requirements of the organisms involved and the balance of chemical elements presented to them in their environment. These interactions have a rich history in pelagic ecology owing the work of A. C. Redfield and co-workers (Redfield 1958, Redfield et al. 1963). These researchers showed that the characteristic elemental ratios of marine phytoplankton production leave a biogeochemical fingerprint on the cycling of major elements (O, C, N, P) at the scale of the world’s oceans and, indeed, at the scale of the entire biosphere. However, despite the renown of Redfield’s ratio, until recently pelagic ecologists have not considered the stoichiometry of processes occurring elsewhere in the food web.

The differential elemental requirements of organisms form the basis of one of the most highly developed aspects of ecological stoichiometry, resource ratio competition theory (Tilman 1982). However, ecological stoichiometry has recently been extended to a variety of other settings, including trophic dynamics (Sterner and Hessen 1994, Andersen 1997, Elser et al. 1998), microbial nutrition (Tezuka 1990, Chrzanowski et al. 1997), host-pathogen interactions (Smith 1993a), symbiosis (Smith 1993b), and comparative ecosystem analysis (Elser and Hassett 1994, Downing 1997). Besides these, one of the main arenas of application of the stoichiometric perspective has been in the study of consumer-driven nutrient recycling (CNR) in pelagic ecosystems (Sterner 1990, Urabe et al. 1995). Here, we will focus on CNR and how stoichiometric analysis helps to understand the complex interactions among grazers, algae, and nutrient cycling in pelagic ecosystems. Two sets of elemental ratios are critical in this sort of analysis: those of the algae being grazed and those of the grazer. We will show that the balance of these ratios between predator and prey is critical in determining the rates and ratios of limiting nutrients recycled by zooplankton. Our examples will be drawn largely from freshwater systems, although marine environments are considered when relevant. We will emphasize the most important aspects of general interest and, in doing so, hope to show how such a perspective may help ecologists to understand the role of the food web in regulating nutrient recycling in other habitats as well. Ecologists are increasingly interested in understanding the reciprocal relationships between the biological features of individual species and the ecosystem context in which they are imbedded (Jones and Lawton 1995). Nutrients, algae, and grazers are very closely coupled (Sterner 1989). Thus, progress in understanding CNR, how CNR is affected by various zooplankton species, and how CNR affects phytoplankton growth and community structure will represent considerable progress in understanding fundamental species-ecosystem relationships.

Limnologists and oceanographers have long known that herbivores of various kinds and sizes are critically involved in nutrient cycling in pelagic ecosystems (Ketchum 1962, Lehman 1980a, Sterner 1986). However, much past work on CNR by zooplankton has tended to be one-dimensional. Marine zooplankton ecologists, working under the main paradigm that N is the primary limiting nutrient in the oceans (an assumption now under scrutiny; Downing 1997, but see Falkowski 1997), generally quantified N processing by zooplankton. However, freshwater ecologists, working under the paradigm that P was the primary limiting nutrient (an assumption also under scrutiny; Elser et al. 1990), focused on P metabolism. Only rarely did pelagic scientists consider regeneration by zooplankton of both N and P and its implications, aside from the sustenance of primary production.

In the middle to late 1980s, the situation began to change. The first sign of change came in the form of a paper by Lehman (1984), who pointed out the possibility that zooplankton might not recycle N and P with the same efficiency and that this might have effects on phytoplankton communities by altering N:P ratio (N:P) of the nutrient supply. Soon after, Olsen et al. (1986) showed that the rate of P release by the cladoceran Daphnia was strongly dependent on the P content (in terms of P:C ratio [P:C]) of its food; P release decreased strongly when algal P:C was low. Two additional pieces of the puzzle came soon after. Elser et al. (1988) showed that changes in zooplankton community composition from Daphnia dominance to calanoid-copepod dominance, due to changes in fish predation, were associated with qualitative shifts in the identity of the nutrient limiting phytoplankton growth. Under conditions of Daphnia dominance, phytoplankton growth was P-limited while under conditions of copepod dominance the phytoplankton were N-limited. Small-scale enclosure experiments also showed that alterations in zooplankton biomass or size structure qualitatively altered algal N vs. P limitation. Elser et al. (1988) reported no definitive mechanism for this effect, but new data from Andersen and Hessen (1991) soon clarified the situation (see Plate 1). These data showed that calanoid copepods have slightly elevated N content (in terms of %N by dry weight) but greatly reduced P content (%P) relative to Daphnia. Thus, Daphnia has low body N:P ([approximately]14:1 by atoms) relative to most calanoid copepods ([approximately]30-50:1), leading Sterner et al. (1992) to conclude that the phenomena reported by Elser et al. (1988) resulted from an altered stoichiometry of CNR. Zooplankton assemblages dominated by Daphnia differentially retained P in biomass and recycled N at a relatively high rate, thus shifting phytoplankton growth towards P limitation. When the zooplankton assemblage was dominated by calanoid copepods, the zooplankton community retained N and differentially recycled P; thus, phytoplankton growth was N limited.

We present an updated view of the application of ecological stoichiometry to CNR in pelagic ecosystems in order to show the promise of this concept both for continued studies in pelagic systems, but especially for application in other systems. First, we review the structure and predictions of three primary theoretical constructs that have mathematically formalized the stoichiometry of CNR (Olsen and Ostgaard 1985, Sterner 1990, Hessen and Andersen 1992) and provide an overview of the key findings of the first fully stoichiometric dynamic model of trophic interactions (Andersen 1997). Second, we review recent studies that have simultaneously considered recycling of both N and P by zooplankton and evaluate the extent to which these studies support the role of stoichiometry in CNR. Third, by synthesizing the results of prior studies we will consider the implications of the stoichiometry of CNR for pelagic ecosystems, both in terms of phytoplankton community structure and pelagic ecosystem function. Our emphasis in these three areas will be on stoichiometric aspects of nutrient recycling by zooplankton. In recent years an increasing number of studies have applied stoichiometric perspectives to analysis of nutrient recycling by other types of invertebrates and especially by fish (e.g., Vanni and Findlay 1990, Schindler 1992, Arnott and Vanni 1996, Vanni 1996, Schindler and Eby 1997, Vanni et al. 1997). Thus, consumers higher in the food web may also be important in nutrient recycling in pelagic systems via a variety of means, including the stoichiometry of CNR by fish (Vanni 1996, Vanni et al. 1997). However, we do not emphasize these interesting studies in this paper for two reasons. First, much more is currently known about the stoichiometry of zooplankton-phytoplankton interactions. Second, the reciprocal interactions between zooplankton herbivores and algae, via nutrient transfers, are likely to be more tightly coupled than those driven by higher level consumers that are separated by additional trophic levels from the direct effects of their nutrient recycling. Finally, we will suggest a number of unanswered questions that may help in further understanding the stoichiometry of CNR in pelagic ecosystems and raise the possibility that a better understanding of a variety of other systems, such as streams, soils, and aboveground food webs, may also result from the application of this set of ideas.


The strict constraints imposed on the natural world by the first law of thermodynamics provide a powerful tool for development of mathematical formulations for the fate of multiple elements during trophic interactions. This has led to the development of a set of models of trophic interactions and nutrient recycling by zooplankton that have greatly illuminated the nature of these processes. Because these models formally acknowledge the constraints imposed by the mass balance of multiple elements during ecological interactions, we refer to them as “stoichiometrically explicit.” We choose this term because it is analogous to the widespread concept of “spatially explicit” ecological models that recognize the important constraints placed on ecological dynamics by space and rates of movement through space. Other models of zooplankton nutrient recycling have employed less satisfying descriptions of zooplankton nutrient release, relying, for example, on fixed allometric expressions (e.g., Carpenter and Kitchell 1984). Andersen (1997) points out that some of these approaches have the unfortunate property that under certain conditions the animals are seen to synthesize new nutrient atoms in their bodies! A model property such as this is clearly unacceptable. Instead, we require formulations that acknowledge that animal biomass is constructed of multiple elements; that the proportions of those elements in that biomass must be accounted for; that animals can independently adjust the “efficiencies” with which they retain various elements; and thus that the rates and ratios of elements released by the animal will change as a function of the elemental composition of the food being ingested, the elemental composition of the animal biomass being formed, and the ability of the animal to retain matter relative to the rate of consumption.

We will describe two stoichiometrically explicit theoretical approaches. The first (Sterner 1990) predicts the N:P of recycled nutrients as a function of food N:P, consumer N:P, and the ability of the consumer to sequester a growth-limiting nutrient. This model has been particularly important in stimulating thinking about the effects of consumers on the identity of the element that limits phytoplankton growth (N vs. P); the N:P balance in grazers determines rates and ratios of N and P release that, in turn, influence the nutrient regime experienced by phytoplankton. The second approach involves C and P simultaneously, rather than N and P, and involves a family of studies investigating how P recycling is affected by the relationship between grazer and food P: C ratios. These studies start with Olsen and Ostgaard (1985) and Olsen et al. (1986), are modified by Hessen and Andersen (1992), and finally undergo a comprehensive analysis by Andersen (1997) that involved examination of the dynamical consequences of the stoichiometry of nutrient recycling within a carbon and phosphorus framework.

The models to be discussed were developed by limnologists for application to pelagic food webs. However, we emphasize that there is nothing inherently specific to limnology in these formulations. For example, Sterner’s model focuses on zooplankton N:P and algal N:P to predict recycling N:P in the water column. There are no differences in animal physiology sufficiently large between consumers in freshwater and those in soil (for example) to preclude direct application of his equations in soil systems, provided that data on food and consumer elemental composition are available.

A model based on N and P

Sterner (1990) developed an approach in which zooplankton, phytoplankton, and dissolved pools of both N and P were modeled. Fluxes between pools took the form of ingestion (flux from phytoplankton to zooplankton), uptake by algae (flux from dissolved to phytoplankton), and recycling by zooplankton (flux from zooplankton to dissolved). Since the focus of the model was zooplankton CNR, only fluxes into and out of zooplankton were considered. Equations took the following general form:

d[Z.sup.N,P]/dt = [F.sup.N, P] – [E.sup.N, P] – M (1)

where Z is the zooplankton body mass in terms N or P, F is the feeding rate for the element in question, E is the loss rate via excretion and egestion, and M is the mortality. Sterner showed that the expressions for N and for P can be usefully integrated under a key physiological assumption that grazer N:P (b) is homeostatically regulated by the animal (that is, db/dt = 0) by adjustment of assimilation efficiencies for N and for P, according to somatic N:P and food N:P. The reader can examine the derivations presented in Sterner (1990), but the assumption of elemental homeostasis permits solution of the expressions to predict the N:P of nutrients released by the grazer (s) as a function of grazer N:P (b) and food N:P (f) as follows:

s =f/(1 – L) – bL/(1 – L) when f [greater than] b (2)


s =f(1 – L)/(1 – Lf/b) when f [less than or equal to] b (3)

where L is the maximum accumulation efficiency (fraction of ingested element retained in biomass) for the element in shortest supply. Note that in Eq. 2, s is linear with f, while in Eq. 3, s is curvilinear with f These expressions formally describe the simultaneous dependence of recycling N:P both on the relationship between food and grazer N:P ratios (f and b respectively) and on the ability of the grazer to sequester limiting resource in its biomass (L).

The predictions of Eqs. 2 and 3 are shown in Fig. 1 [ILLUSTRATION FOR FIGURE 3 OMITTED]. By considering a vertical slice of the curves in Fig. 1, one can see that, for any given f and L, recycling N:P (s) is inversely related to grazer somatic N:P. Thus, when the zooplankton community is dominated by low-N:P grazers (such as Daphnia), recycling N:P should be high, but, when grazers have high body N:P, recycling N:P should be low. Recycling N:P is also clearly a function of food N:P (f) for a given grazer (holding b constant), but in a nonlinear way, as s increases curvilinearly with f when f is lower than the animal N:P (b). Grazer accumulation efficiency (L) strongly accentuates the nonlinearity of the relationship between s and f (not shown). At low L, the relationship between s and f is relatively straight; indeed, for a grazer that accumulates none of its ingested nutrients (L = 0), recycled N:P is identical to ingested N:P regardless of grazer elemental composition. Such a grazer is a “garbage disposal” and merely converts its prey into inorganic chemical elements. However, if grazers can efficiently accumulate a nutrient element in short supply (high L) they can dramatically “bend” the s vs. f relationship.

This has important implications for feedbacks on the interaction, as it means that the grazers generate nutrient supply ratios substantially different than those of the algae they ingest. As pointed out by Sterner (1990), this can accentuate the elemental imbalance between algae and grazer. For example, grazers feeding on P-limited algae (f [greater than] b) will recycle at high N:P, potentially accentuating algal P limitation and resulting in increased algal N:P (Rhee 1978), which feeds back again via further increases in recycling N:P. Sterner et al. (1992) suggested that such feedbacks likely generated the food web induced shifts in N and P-limited algal growth reported by Elser et al. (1988).

Sterner’s model was one of the first multiple currency theories of nutrient recycling by consumers and has generated predictions that hold up very well to experimental tests. However, its predictions are static and do not consider the complex feedbacks among algal elemental composition, zooplankton nutritional requirements, and nutrient cycling. For example, if low-N:P grazer populations tend to accentuate P limitation in algae, thus raising the C:P and N:P of their own food, the grazers themselves may eventually become strongly P limited and collapse, releasing the P stored in grazer biomass and returning it to the algae. Does the stoichiometry of nutrient recycling generate an indefinite sequence of worsening nutrient limitation in algae, or do feedbacks arise that damp the system? We will present theoretical insights on this question below (see A dynamic model of CNR).

Models based on carbon and phosphorus

A series of important theoretical advances have emerged from Scandinavia, where Olsen, Hessen, and Andersen (and co-workers) have developed a series of expressions for the rate of P release (per unit biomass ingested) based on mass balance that incorporates the animal’s demands for both C (for respiration and biomass production) and P. The initial steps came from Olsen and colleagues, who were concerned by the fact that measurements of P release by zooplankton were extremely variable and sought to address the problem by use of a computational model that took advantage of the mass balance of P, both in incubation vessels and inside the grazer’s body (Olsen and Ostgaard 1985, Olsen et al. 1986). They expressed the rate of P release per unit zooplankton biomass (R) simply as the rate of P ingestion minus the rate of body growth in terms of P:

R = IQ- g[Theta] (4a)

where I is the ingestion rate of food carbon per unit zooplankton biomass, Q is food P:C, g is the specific growth rate of the grazer, and 0 is the P:C ratio of grazer biomass. Eq. 4 makes it clear that release of P by the grazer will decline as food P:C declines and/or as grazer P demand (as indexed by [Theta]) increases. Indeed, this equation implies that, below a threshold food elemental composition, no P release by the animal should occur. That value is found by setting Eq. 4 equal to zero and solving for the threshold food P:C ratio ([Q.sub.T]):

[Q.sub.T] = (g/I)[Theta]. (4b)

In fact, Olsen et al. (1986) obtained experimental data for the rate of P released per unit of food carbon ingested (R/I) that they interpreted as a linear function of food P:C with an x-intercept of [approximately]7 [[micro]gram] P/mg C (atomic C:P [approximately] 370:1). In other words, they concluded that when the food C:P [greater than] 370, Daphnia no longer recycled P back to the inorganic form.

Olsen et al. (1986) cautiously suggested that their formulation implies the potential for P limitation of grazer growth under natural conditions, one of the earliest suggestions of such a possibility. Olsen et al. (1986) first believed that P limitation of herbivore growth would be relatively rare in nature because a small proportion of bacteria or P-sufficient algae (low C:P) in the food might enough to meet grazer P demand. However, they were not aware of Daphnia’s unusually high somatic P demand. Later, however, Hessen (1992) and Urabe and Watanabe (1992) estimated [Q.sub.T] by using Eq. 4, with real carbon and phosphorus balance data for cladocerans. They showed that although [Q.sub.T] changes with food abundance and differs among cladoceran species with different somatic P:C, [Q.sub.T] for Daphnia is generally somewhat higher than the original value estimated by Olsen et al. (1986). Since P:C ratios of particulate matter (the potential food of zooplankton) in many lakes are lower than the [Q.sub.T] estimated by Hessen (1992) and Urabe and Watanabe (1992), they concluded that P limitation of Daphnia growth is likely in nature. Since P limitation of animal growth has major implications for various ecological processes including CNR, these studies sparked controversy regarding the possibility of animal P limitation (Brett 1993, Hessen 1993, Urabe and Watanabe 1993). This controversy has stimulated a number of studies examining effect of food quality on animal growth rate (see papers in Gulati and DeMott 1997). Since discussion of these studies is beyond the scope of this paper, we only briefly mention that there is now direct evidence showing that P limitation of animal growth is real (Urabe et al. 1997, DeMott 1998).

While there is some question about the general validity of Eq. 4 at low food P content, the work of Olsen and colleagues was one of the most important initial advances in understanding the stoichiometry of CNR by zooplankton and in appreciating the potential for mineral limitation of zooplankton production. Understanding the conditions that produce herbivore food quality limitation has now become a major emphasis in plankton ecology (Elser and Hassett 1994, Sterner and Hessen 1994, Urabe and Sterner 1996, Gulati and DeMott 1997, Sterner 1997, Sterner et al. 1997).

Hessen and Andersen (1992) modified the formulation of Olsen et al. (1986), primarily addressing the issue of whether animals completely halted release of P when consuming P-deficient food. Key to their contribution is the idea that when food P:C ratio decreases below that of the animal’s body, the rate of animal growth decreases. The most recent treatment of C/P-based equations for P release is that of Andersen (1997), who noted that the model of Hessen and Andersen (1992) is one case in a continuum of assumptions regarding how growth and the P economy of animals respond to P-deficient food. After considering the full range of potential animal responses to P-deficient food (from “sloppy” animals that release a certain fraction of their body P even under P-limited growth to animals that are 100% efficient in reclaiming P from materials respired by the animal), Andersen chose the following expressions to describe the rate of P release per unit zooplankton biomass (p, in his expressions) as a function of animal and food P:C ratios:

R = IQ – [g.sup.*][Theta] when Q [greater than] [Theta] (5)


R = IQ – [g.sup.*](Q/[Theta])[Theta]

= IQ – [g.sup.*]Q when Q [less than] [Theta] (6)

where [g.sup.*] is grazer specific growth rate under good food conditions. Eq. 5 is the same expression as used by Olsen (i.e., Eq. 4a, under good food conditions). However, Eq. 6 reflects the assumption that, when food P content is deficient relative to grazer P content, the rate of growth of the grazer decreases by a factor directly proportional to the imbalance in the elemental composition of the food and grazer (Q/[Theta]).

The predictions of these equations are shown in Fig. 2 (P release rate is normalized to food ingested for easier comparison with laboratory and field measurements). Grazer elemental composition affects the rate of P release, as P-rich grazers have lower rates of P release for a given food P content (when food P:C is high). The figure also shows that the gross growth efficiency ([g.sup.*]/I, in terms of carbon) of the grazer also affects predicted P release and, as for the effect of L in the model of Sterner (1990), accentuates the effect of grazer elemental composition on P release. Note from the figure and equations that, under these assumptions, grazers always release at least some P, provided that food P content is nonzero. This can be seen by noting that Eq. 6 can only equal zero if food P:C is zero (trivial case) or if [g.sup.*] = I (i.e., growth efficiency is 100%, a thermodynamic impossibility). Eq. 6 also implies that at very low food P:C ratios (ratios lower than the P:C values of all potential grazers), grazer P release is no longer a function of grazer elemental composition. This means that species-specific differences in grazer body elemental composition directly determine when the switch from Eq. 5 to Eq. 6 occurs and directly affect P release rates when algal P:C is comparatively high (Eq. 5). However, differences in grazer body P content counter-intuitively do not influence P release rate at very low food P:C values. Distinguishing which of the two sets of assumptions, Olsen’s or Andersen’s, is correct, or indeed whether either set is satisfactory, may be an important unsolved issue in grazer ecology, as it is likely that algal P:C ratios in many limnetic systems will be lower than P:C values for most grazer species (Elser and Hassett 1994).

A dynamic model of CNR

The approaches just described, and that of Sterner (1990), are static formulations. That is, they assess nutrient recycling for fixed values of food elemental composition and grazer elemental composition; the approaches do not incorporate responses of grazer populations to changes in the quantity and quality of their food. However, CNR, itself, is likely to affect the physiological status of the algae, especially if it involves strongly differential recycling of limiting nutrients. For example, the model of Sterner (1990) predicts that a grazer feeding on algae with N:P greater than its body N:P will recycle at an N:P even higher than that of its food. This may accentuate P limitation of the algae and further increase food N:P, leading to additional increases in recycling N:P in a continual round of worsening P limitation of algal growth. The model of Hessen and Andersen (1992) implies a similar dynamic for zooplankton P recycling and algal C:P. But does CNR continually alter recycling ratios and food nutritional status, or do other factors prevent such a indefinite spiral? To answer this question, and a variety of others, Andersen (1997) built a set of fully dynamic expressions for the stoichiometry of zooplankton-phytoplankton interaction. In addition to the stoichiometric expression of P release, the other key components of the model were formulations for variable algal C:P as a function of P-limited algal growth rate and for a homeostatic grazer that must match both its energy (C) and nutrient (P) requirements to grow maximally (i.e., food quality, as indexed by algae C:P, has an influence). Thus, this model is fully stoichiometrically explicit and contains the most important reciprocal feedbacks between algae and grazer.

The dynamical consequences of the nutritional stoichiometry of grazers, algae, and recycling in Andersen’s model are profound. The isoclines for algae and grazer predicted by Andersen’s model are shown in Fig. 3. In contrast to classical Lotka-Volterra theory, both isoclines are hump-shaped. The rising portion of the algal isocline reflects saturation of zooplankton feeding response (once individual grazer functional response is saturated, it takes more zooplankton biomass to match a given rate of algal growth), and its intersection with the x-axis reflects the constraint of total P in the system (algal P:C has a minimal value due to physiological constraints; thus, a given amount of P can produce [algal biomass] [less than or equal to] [total P]/[minimal algal P:C]). Perhaps more interesting than the algal isocline is the zooplankton isocline, which is also hump-shaped due to the consequences of the stoichiometry of nutrient recycling and food quality. As algal biomass increases along the x-axis, zooplankton feeding and, thus, nutrient recycling eventually saturate. Thus, the rate of P recycling by zooplankton relative to algal demand (algal biomass) begins to decline along the x-axis. Such an effect necessarily results in stronger P limitation of algal growth, higher algal C:P, and decreased grazer growth due to poor quality food. The situation worsens at even higher levels of algal biomass until the food quality is sufficiently bad that the grazer can not grow at all [ILLUSTRATION FOR FIGURE 3A OMITTED].

The dynamics of the grazer-algae system are directly determined by the shape and relationship of the isoclines shown in Fig. 3. Two scenarios are possible. In one [ILLUSTRATION FOR FIGURE 3A OMITTED], if the system starts anywhere in the positive phase space (i.e., both algal and grazer biomasses are nonzero) the components will cycle indefinitely in a limit cycle or (if the system starts in the shaded area) be drawn to the equilibrium point A1. In any case, the zooplankton-phytoplankton interaction is stable (i.e., the grazer extinction point A2 in Fig. 3A is unstable). In the second situation [ILLUSTRATION FOR FIGURE 3B OMITTED], there are two positive intersections and, consequently, dynamic conditions differ. While one positive intersection remains the center of a domain of attraction (point B1), the equilibrium point with high algal biomass and no grazers (point B3) is now locally stable. That is, depending on initial conditions, grazer extinction is now a possibility. Andersen shows that transitions between situations such as those in Fig. 3 occur as a function of the parameter [g.sup.”], which is defined as follows:

[g.sup.”] = [g.sup.*]([Q.sub.a]/[Theta]) (7)

where [Q.sub.a] is the minimal P:C of the algae (i.e., algal P:C when algal growth rate is zero). High values of [g.sup.”] make it more likely that the system will be appear as in Fig. 3A, where the zooplankton-phytoplankton interaction is stable. Such a situation will occur when [Theta] is low (as such animals are less sensitive to the food quality effects that bend the grazer isocline) and [Q.sub.a] is high (a high [Q.sub.a] means that the algal isocline will intersect the x-axis at a lower value). On the other hand, algal species that are more efficient in P use at low growth rate (lower [Q.sub.a]), or grazers with higher P contents (higher [Theta]), reduce the magnitude of [g.sup.”], making the situation more likely to resemble Fig. 3B where grazer extinction is a possibility.

These analyses show that the dynamic stability of the zooplankton-algae interaction hinges directly on two stoichiometric parameters (autotroph minimal P:C and grazer P:C), but nutrient recycling appears to have disappeared from the formulation. However, if one appreciates that nutrient recycling is simply the outcome of ingestion and assimilation processes (Eqs. 5 and 6) that are strongly dependent on the elemental composition of food and grazer, one can appreciate that the stoichiometry of nutrient recycling is already imbedded in the trophic dynamics of the plant-animal interface. Indeed, Andersen’s (1997) analysis makes clear that trophic interactions and nutrient cycling, traditionally distinct components of ecosystem study (Hessen 1997), are one and the same thing, as all of the “ecological players” are in fact “made of the same stuff” (sensu Lotka 1925). In this way, we see that, because of the constraints of matter, stoichiometric nutrient recycling by the grazer does not cause an indefinite spiral of worsening food quality and slower nutrient recycling. Instead, the system may achieve an internal equilibrium, stable cycles, or even grazer extinction (depending on parameter values) with the specific outcome strongly dependent on initial conditions.

The interactions between growth, nutrient release, and the composition of food and grazer modeled by Andersen (1997) are a simplification of trophic dynamics, considering only dissolved nutrients, algae, and grazers. Of course, natural ecosystems are more complex. One complexity that may prove important is the reality that suspended particulate matter in pelagic ecosystems is comprised of more than just phytoplanktonic algae, containing considerable quantities of both bacteria and detritus. To the extent that the grazer can select for or against such particles, bacteria and detritus have divergent effects on the nutritional regime of a given consumer, as bacteria are generally more P rich than algae (Hessen and Andersen 1990, Elser et al. 1995a), while detritus is likely to be strongly depleted in nutrient elements (Olsen et al. 1986). Indeed, Andersen (1997) extended his analysis beyond the three-component system described, to include bacteria and detritus. Adding these components introduced a strongly chaotic flavor to the dynamics, in which increased nutrient loading during eutrophication generated a period-doubling cascade in the plankton time series. We refer the reader to that analysis for more detail. In any case, it seems sufficient to point out that characterizing the actual distribution of key limiting elements among suspended particles, and the extent to which grazers are able to discriminate among those particles in feeding, should be a high priority in stoichiometric analysis of trophic dynamics under natural conditions.

Thus, Andersen (1997) has elegantly shown that trophic biogeochemistry generates alternative stable states, limit cycles, and deterministic chaos. The equations of Andersen (1997) are easily generalizable and may shed considerable light on processes regulating the apparently high degree of variability in trophic dynamics across ecosystem types. For example, Polis and Strong (1996) suggest that differences in the nature and strength of the trophic cascade between different habitat types (terrestrial, aquatic) might reflect differences in the edibility, nutritional value, and/or vulnerability of food at the base of the food web. Such differences are well-gauged by elemental composition and, thus, the approaches described here, including models of the stoichiometry of CNR, provide a formal means of evaluating the sources of differences and similarities in trophic dynamics and nutrient cycling in aquatic and terrestrial ecosystems.


Evaluating the validity of the models of CNR just described first requires a test of their assumptions, the most important of which is the assumption that a consumer’s body elemental composition is stable within a consumer species, regardless of environmental conditions. In support of this assumption, within-species elemental composition was similar for individuals of several species collected from oligotrophic to highly eutrophic lakes (Andersen and Hessen 1991, Hessen et al. 1992), changing little in response to manipulations of food quantity and quality (Andersen and Hessen 1991). Homeostasis in elemental composition of zooplankton species has also been convincingly demonstrated by field studies showing that the N:P of net zooplankton production is close to that of zooplankton body tissue [ILLUSTRATION FOR FIGURE 4A OMITTED]. In this figure, variation of zooplankton N:P is due entirely to differences in species composition. This evidence indicates that zooplankton species keep their elemental composition within a limited range even if they ingest food with an elemental composition much different from their body tissues.

The validity of the proposed recycling models can be directly assessed by comparing N and P release rates of grazers under various environmental conditions. A number of factors, such as food abundance, food quality, and body size, have been shown to affect nutrient release rate by zooplankton species (Peters and Rigler 1973, Lehman 1980b, Seavia and Gardner 1982, Ejsmont-Karabin 1984, Olsen et al. 1986, Urabe 1993, Urabe et al. 1995). To test stoichiometric models of differential nutrient recycling, we compiled data from studies that measured simultaneously both the N and P release rates of zooplankton with their food N:P. Although sources of these data are limited, the studies cover various environmental conditions including a small eutrophic pond (Urabe 1993), a mesotrophic lake (Lake Biwa, Japan; Urabe et al. 1995) and the tropical Atlantic Ocean (LeBorgne 1982). Although Sterner (Sterner 1990) originally used marine data to evaluate his proposed model, use of data from marine systems is less powerful for evaluating effects of food N:P on the N:P release ratio, because algal elemental ratios in marine systems fall within a limited range around Redfield proportions, but vary across a wide range in freshwater systems (Elser and Hassett 1994). Thus, data sets that include values from freshwater in addition to marine environments can evaluate more effectively the effect of food quality on the N:P of nutrient release. As predicted by Sterner’s model, the N:P of nutrient release by zooplankton grazers is not constant, but is closely related to food elemental composition: with increasing food N:P, P release rate relative to N release rate decreases [ILLUSTRATION FOR FIGURE 4B OMITTED].

To examine the validity of the P:C-based models of Olsen et al. (1986) and Andersen (1997), P released per unit of food carbon ingested was plotted against food P:C [ILLUSTRATION FOR FIGURE 4C OMITTED]. In this figure, we included the original data of Olsen et al. (1986) instead of LeBorgne’s (1982) data, because C ingestion rate data are not available in the latter study. Again, in accord with the models, the P release rate, relative to C ingestion rate, decreased with decreasing food P content (increasing C:P). However, variance in P release, relative to C ingestion, also tended to increase with increasing food P:C. One possible cause of this variance is that P release rate may also be affected by the contents of other elements in the food, such as nitrogen (Urabe 1993). For example, the model of Sterner (1990) predicts that, even if food C:P is the same, P release rate is expected to be higher when N is the scarcest element in the food, relative to demand of zooplankton, than when P is the scarcest. This argument implies that the models of Olsen and Andersen are valid only when P or C is the scarcest element in the food, relative to demand of consumers (Andersen 1997). In other words, g in Eq. 4 and [g.sup.*] in Eqs. 5 and 6 may not be determined by relative C and P contents in the food alone. Similarly, Sterner’s model is valid only when N or P (and not C) is the element in the lowest supply, relative to the demand of consumers.

Of considerable interest in this analysis is the nature of the relationship between P release and food P:C, at high-C:P values. The formulation of Olsen et al. (1986: Eq. 4) implies that P release rate is zero below some threshold food P:C (Eq. 4b). However, the expressions of Hessen and Andersen (1992) and Andersen (1997) imply reduced grazer growth rate when P is deficient, thus, permitting some P release at low food P:C. While the data are extremely scarce and no information is available for extremely P-deficient food (P:C [less than] 0.002), the values provided by the study of Lake Biwa by Urabe et al. (1995) indicate detectable P release even for food P:C values considerably lower than grazer P:C [ILLUSTRATION FOR FIGURE 4C OMITTED]. This suggests that the formulations of Hessen and Andersen (1992) and Andersen (1997) more realistically represent the P release situation under conditions of P-deficient food. However, even the studies of Urabe et al. (1995) may not represent a good test, as lakes with C:P values considerably higher than in Lake Biwa may be relatively common (Elser and Hassett 1994). Further theoretical studies are needed to determine whether the dynamic nature of the interaction is sensitive to whether P release is simply very low (Eq. 6), or whether it is actually zero (Eq. 4), when food P:C is very low. If theory indicates that this distinction is important, then the extremely difficult measurements of small rates of P release at low food P:C that will be required to settle the issue will be justified. Furthermore, the ability of the animal to extract a high percentage of P from a high-C:P diet may depend on the chemical form of P in the food (nucleic acids, phosphate or polyphosphate, phospholipids), and new studies are needed of assimilation efficiency of various forms of P in poor quality food. However, it seems reasonable to hold that it is unlikely that nutrient assimilation from food can ever be 100%, thus Eq. 6 may be the most reasonable formulation for the time being.

In addition to effects of food elemental composition, these three models predict that the elemental ratio of the consumer will also affect nutrient release ratios. However, in the data compiled, no direct relationship was found between the N:P of nutrient release and consumer N:P [ILLUSTRATION FOR FIGURE 4D OMITTED]. Since the N:P of potential food varied widely among the study sites involved, the effect of consumer elemental composition on the nutrient release ratio may be obscured by strong variation in food elemental ratio. To evaluate this possibility, we estimated residuals of the regression between N:P of nutrient release and food N:P [ILLUSTRATION FOR FIGURE 4B OMITTED] and plotted them [TABULAR DATA FOR TABLE 1 OMITTED] against consumer N:P [ILLUSTRATION FOR FIGURE 4E OMITTED]. Although the correlation coefficient was low, there was a significant negative relationship between the residuals and consumer N:P, indicating that, in accord with predictions of stoichiometric theory, zooplankton species with relatively high body P content release less P, compared to species with low P content. This suggests that the primary determinant of the N:P of CNR is food N:P and that grazer body N:P is secondary, a reasonable conclusion given the likelihood that cellular elemental composition is considerably more variable (both intra- and interspecifically) for autotrophs than for metazoans (Sterner and Hessen 1994).

The effect of consumer elemental ratio on the N and P recycling regime can also be examined by comparing the accumulation of inorganic nutrients in containers with different zooplankton species that are exposed to similar food. In general, we might expect inorganic nutrient pools at the end of grazing experiments to have higher N:P when a low-N:P animal, such as Daphnia, is the grazer, relative to treatments with higher N:P species, such as calanoid copepods. Several field and lab studies have reported the results of such experiments (Table 1). It should be noted that concentrations of nutrients accumulated in containers with food reflect the balance between nutrient release by zooplankton and nutrient uptake by phytoplankton. However, since the N:P of nutrient uptake rates by phytoplankton should occur to a large extent independent of zooplankton species, differences between treatments in the N:P of accumulated nutrients in such experiments likely reflect differences in release N:P for the different zooplankton species involved.

Five out of the six experiments yielded results consistent with expectations derived from stoichiometric theory, in which the N:P of inorganic nutrient concentrations at the end of grazing experiments was significantly higher in treatments containing Daphnia alone, relative to treatments with other grazers. The exceptions were the experiments of Moegenberg and Vanni (1991) in Lake Mendota, Wisconsin, USA, which showed a similar ratio of inorganic N and P accumulated in Daphnia and non-Daphnia treatments. However, this result is not necessarily at odds with stoichiometric expectations, as they used an ambient zooplankton assemblage that included Daphnia for comparison with Daphnia-only treatments, while the four other studies used copepods or cladoceran species with high body N:P, for comparison with Daphnia.

Thus, from the data produced so far via direct measurements of N and P release and experimental tests of different species on nutrient concentrations, we conclude that nutrient release ratios are strongly affected by deviation of food elemental ratios from that of consumers. It should be noted, however, that the actual value of the nutrient release ratio for any given food-consumer interaction may be not the same as that predicted by theoretical models, because, in nature, not all elements released by consumers are in immediately available forms (Urabe 1993). Indeed, zooplankton feces and algae injured by feeding may need considerable time to return to dissolved form and these “consumer-derived particles,” which are lost from the water column during sinking, may not have the same elemental ratio as the food. However, such possible sources of error might be avoided by focusing on the rate or elemental ratio of particle elimination (elimination is equal to ingestion corrected for release products remaining in particulate form), instead of focusing on rates and ratios of particle ingestion (Urabe 1993). This discussion calls attention to the possibility that stoichiometric analysis may be useful in understanding zooplankton-driven alterations in nutrient sedimentation (Sarnelle 1992, Elser et al. 1995b, Elser and Foster 1998).


Data reviewed in the previous section clearly support the view that the stoichiometry of zooplankton-phytoplankton interactions can result in strongly differential recycling of N and P in pelagic ecosystems. What evidence, beyond that of Elser et al. (1988), exists that this differential nutrient recycling affects phytoplankton nutritional status or community structure in natural environments? As such effects lie one step further than the changes in relative N and P release that have been reviewed, data on effects of CNR on the nature of algal nutrient limitation and community dynamics are correspondingly rare. However, we will show in the following that sufficient data exist to support the contention that differential recycling of N and P due to the stoichiometry of CNR is a potentially important mechanism affecting phytoplankton communities and pelagic ecosystem function.

Some evidence comes in the form of physiological data and enrichment experiments that gauge the relative severity of N and P limitation of phytoplankton growth. Carpenter et al. (1993) analyzed extended time series for zooplankton dynamics and physiological indicators of N and P limitation of phytoplankton growth in the same lakes studied by Elser et al. (1988). They found consistent correlations between changes in N vs. P limitation of algal growth and changes in zooplankton community structure (mean body size, Daphnia biomass) that were driven by changes in food web structure. Thus, the qualitative effects of zooplankton community structure on algal nutrient limitation initially reported by Elser et al. (1988) appear to be reliable phenomena in these lakes. In a study of N and P limitation of phytoplankton growth in subalpine Castle Lake, California, USA, Elser et al. (1995c) showed that in two of three study years the severity of N relative to P limitation, as gauged by nutrient bioassays, was significantly correlated with estimated macrozooplankton community N:P. N-limited algal growth prevailed when zooplankton N:P was high; P-limited growth prevailed when zooplankton N:P was low. However, this relationship was not observed in their third study year, suggesting interannual variability in the importance of macrozooplankton nutrient release for phytoplankton nutrient supply in the system. In the laboratory study of Rothhaupt (1997), long-term (63 d) exposure of mixed algal cultures to Daphnia or Eudiaptomus resulted in changes consistent with the stoichiometry of CNR: bioassays revealed pronounced P limitation of algal growth in the Daphnia treatment, but N-limited algal growth in the presence of Eudiaptomus as the grazer. In contrast, Urabe (1993) showed that increased Daphnia biomass lead to higher relative P content in phytoplankton in a eutrophic pond, where phytoplankton growth is P-limited, but P loading is expected to be high. Detailed analysis indicated that the increase in phytoplankton P content was due to increased per capita availability of P for phytoplankton, through grazing reduction of algal biomass, rather than from increased P release rate. Thus, the response of phytoplankton to zooplankton in this small eutrophic pond does not contradict the stoichiometry of CNR if high P loading is considered. Finally, Moegenberg and Vanni (1991) also examined effects of zooplankton on N and P limitation of phytoplankton, with investigations in Lake Mendora. They found that experimentally increasing zooplankton biomass generally reduced both N and P limitation of phytoplankton by roughly the same extent. Their data indicated that the strength of nutrient limitation may not have been particularly strong in their system, thus strong changes in nutrient limitation status would be difficult to detect. We also note that total nitrogen (TN):total phosphorus (TP) ratios in Lake Mendota were high ([greater than]60:1 by atoms), thus making it less likely that changes in zooplankton species composition would be able to shift algae back and forth between P and N limitation.

Consequently, it appears that support from bioassay and physiological data for qualitative effects of the stoichiometry of CNR on phytoplankton nutrient limitation varies among years and among lakes. Andersen (1997) considered this issue and provides guidelines for where such qualitative effects should be expected. He extended his model to include the following features: (1) N and P as limiting nutrients with trade-offs among algal species in competitive abilities for N and P, (2) grazer release of N and P under an assumption of homeostatic regulation of body N:P, and (3) consideration of the nutrient-loading conditions (with respect to N and P), under which changes in zooplankton community structure between low-N:P grazers (such as Daphnia) and high-N:P grazers (such as copepods) would be expected to shift phytoplankton between N and P limitation. His analysis showed that the zone of nutrient-loading conditions (loading N:P), under which qualitative effects of zooplankton community structure potentially occur, is essentially equivalent to the interspecific range of zooplankton N:P! Since copepods (N: P [approximately equal to] 40:1) and Daphnia (N:P [approximately equal to] 14:1) likely represent the “end-points” of the range of zooplankton body N: P, lakes where qualitative effects of zooplankton community structure on phytoplankton P vs. N limitation might occur would be lakes where the N:P of external loading is 14-40:1.

Using TN and TP as rough indices of nutrient loading, Andersen (1997) noted that only [approximately]6% of Norwegian lakes had TN:TP [less than] 40:1. However, this data set represented lakes experiencing a narrow range of geochemical and climatic conditions. More extensive data sets indicate that a much higher percentage of lakes is in the 14-40:1 range of TN:TP. For example, in the data for TN and TP in lakes around the world compiled by Downing and MacCauley (1992), [approximately]25% of the lakes included had TN:TP [less than] 40:1 (J. Downing, personal communication). Hassett et al. (1997) report data for N and P concentrations and ratios for lakes of north-central Wisconsin and northwestern Ontario, Canada. Mean TN:TP [approximately equal to] 65:1 in the Wisconsin lakes but [greater than]130:1 in the Ontario lakes, indicating strong regional variability in the potential for zooplankton to regulate the identity of the growth-limiting nutrient. Interestingly, further analyses of the data of Hassett et al. (1997) indicate that there was also a significant statistical association between a lake’s food web structure (presence or absence of piscivorous fish) and whether or not its algae showed signs of N or P limitation (J. J. Elser, unpublished data). This association is primarily determined by the tendency in Wisconsin lakes for lakes without piscivores to have N-limited algae, while those with piscivores tend to have P-limited algae, consistent with the effects of stoichiometric CNR.

Our analysis emphasizes the influence of differential nutrient recycling on phytoplankton physiological status (i.e., degree of growth limitation by N or by P). Is there any evidence that such effects are manifested at the level of phytoplankton community composition? While information at this point is scanty, results from studies examining effects of food web structure on blue-green algal dominance tentatively suggest that such effects can be dramatic. The classic study by Smith (1983) of factors affecting blue-green algal dominance in lakes provides an intriguing hint of the role of food web stoichiometry in regulating phytoplankton community structure. Smith noted that blue-green algae are insignificant components of phytoplankton communities when TN:TP [greater than or equal to] 55:1 (by atoms); at ratios below this value blue-greens can dominate the community, but with enormous variability in the extent of that dominance. Examining specific data for Lake Trummen, Sweden in detail, Smith pointed out that blue-green algal dominance closely followed the general pattern of increasing importance with decreasing TN:TP, with the exception of a summer following a winter fish kill. In that summer, blue-green dominance was low despite low TN:TP. Smith (1983) attributed this outlier to changes in underwater light field due to increased Daphnia grazing, following the fish kill. However, an alternative explanation might be that differential recycling of N relative to P by low-N:P Daphnia shifted the competitive advantage away from cyanobacteria and toward algae that are better competitors for phosphorus.

Recent data from the Experimental Lakes Area, Ontario, Canada, support such a mechanism. MacKay and Elser (1998) subjected cyanobacteria-dominated phytoplankton from experimentally eutrophied Lake 227 (L227) to enhanced grazing by large Daphnia in a mesocosm experiment. Relative to controls containing calanoid copepods or lacking zooplankton, enhancement of Daphnia increased dissolved ammonia concentrations but not inorganic phosphate concentrations (see Table 1), raised particulate C:P (indicating more severe P limitation of phytoplankton), and strongly decreased rates of N fixation. Thus it appears that differential nutrient recycling by Daphnia can “disarm” N-fixing cyanobacteria and, in doing so, influence the rate of an important ecosystem-level process, i.e., N fixation. These effects can also occur at the whole-lake scale. In 1993 and 1994, L227 was subjected to a whole-lake food web manipulation in which large numbers of piscivorous pike were introduced (J. J. Elser et al., unpublished data). By 1995 minnow populations were decimated, and in 1996 dense populations of large Daphnia appeared. Associated with increased Daphnia abundance were unprecedented concentrations of dissolved ammonia in the water column (but no increases in dissolved phosphorus), greatly reduced algal biomass, and complete elimination of cyanobacteria, including previously dominant N-fixing taxa. Thus, it appears that food web structure can regulate the rate of N fixation at the ecosystem scale and that an essential mechanism of that regulation is differential recycling of N and P, driven by the stoichiometry of CNR.

It is interesting to note that the importance of N fixation in terrestrial ecosystems is also tremendously variable and that at least some of that variation is associated with differences in the relative availability of nitrogen and phosphorus (Smith 1992). Since terrestrial trophic interactions are not exempt from the first law of thermodynamics, and terrestrial consumers probably also differ in body N:P (Elser et al. 1996), we raise the possibility here that food web processes also influence N fixation in terrestrial ecosystems. To the best of our knowledge, this possibility has not yet been examined.


Many lake ecologists are working to unravel various aspects of the stoichiometry of CNR. Important issues that remain include the following:

1) What abiotic and/or biotic factors regulate the elemental composition of algae in various lakes and, in turn, affect CNR? For example, Urabe and Sterner (1996) showed that balance of light and nutrients affects herbivore growth rate through changes in elemental ratios of algae. It is not yet clear how physical conditions, like temperature and light intensity, alter CNR through interplay with biological (species composition) and chemical (nutrient) conditions associated with the C:N:P stoichiometry of algal production (Sterner et al. 1997).

2) What are the evolutionary, cellular, and biochemical determinants of body C:N:P in zooplankton taxa? Preliminary evidence points to a major contribution of P-rich RNA in establishing body C:N:P composition of zooplankton, thus suggesting a connection between selection on body growth rate and body elemental composition, via the cellular and biochemical machinery required to achieve a given growth rate (Elser et al. 1996).

3) What are the C:N:P ratios of species representative of the full suite of freshwater zooplankton? For example, we know very little about noncrustacean zooplankton (e.g., rotifers). Are there species-specific differences in C:N:P among protozoans, and how tightly are those ratios regulated?

4) How do consumer species differ in their nutrient assimilation abilities? In other words, what is the biology of L in Sterner’s model?

5) Does the stoichiometry of nutrient release via excretion and egestion differ? If so, does this have implications for nutrient cycling due to the fact that egested materials are likely to be lost from the water column due to rapid sinking (Sarnelle 1992, Elser et al. 1995b, Elser and Foster 1998)?

While various pelagic ecologists pursue these and other questions, we suggest that scientists working in other ecosystem types, such as streams, grasslands, soil systems, or forests might profitably apply stoichiometric approaches to food web dynamics and nutrient cycling in their systems. The stoichiometric perspective is generalizable across ecosystem types, as some of the main hypotheses regarding causes and consequences of the stoichiometry of CNR developed from study of pelagic systems can be applied directly elsewhere. For example, as we have described, the primary explanation for species-specific differences among zooplankton taxa in body N:P is that body elemental composition is linked to body growth rate (the “growth rate hypothesis”; Elser et al. 1996, Main et al. 1997). Since the mechanisms responsible operate at the level of cell biology, the stoichiometry-growth rate connection should also apply to animals other than zooplankton. For example, it would be interesting to know if herbivore species that function as “keystone herbivores” in other habitats, propagating trophic cascades to the level of autotrophs, are similar to low-N:P Daphnia in lakes. That is, are other keystone herbivores also P rich, high growth rate opportunists that differentially recycle N relative to P and are sensitive to poor food quality?

Autotroph elemental composition appears to be regulated by similar physiological processes in algae and terrestrial plants (Agren 1988). Thus, the factors that regulate autotroph elemental composition and influence nutrient cycling may be understood in similar stoichiometric terms in lakes as well as terrestrial ecosystems. For example, Sterner et al. (1997) have developed a “light: nutrient hypothesis” to explain variation in autotroph elemental composition in lakes (lakes with high algal C:P appear to be those with water columns that are well-illuminated, relative to their P supply) that appears to be readily applicable to autotrophs in terrestrial systems. Finally, Andersen’s (1997) model shows the critical consequences for pelagic systems of expressing trophic interactions and nutrient recycling in stoichiometric terms. New insights into food web dynamics and nutrient cycling in other ecosystem types may result once trophic interactions are more properly expressed in biogeochemical terms.

Thus, we suggest that focus on the stoichiometry of CNR may be an important next step in the study of terrestrial ecosystems. While the importance of elemental ratios is well recognized in certain areas of terrestrial ecosystem science, as in consideration of the influence of nutrient supply on plant mineral nutrition and tissue elemental composition (Chapin 1980, Agren 1988) and of the effects of elemental composition on litter dynamics (Vitousek 1982), extensions into the food web have been limited (but see studies summarized by White 1993 and see De Ruiter et al. 1996). Much past emphasis has been on C:N ratios with little attention to E Given the overall importance of N:P ratio in influencing processes such as N fixation and the strong link between animal P content and growth rate, we suggest that more attention to P, in conjunction with C and N, may be rewarding in analysis of terrestrial food web dynamics. Perhaps the most exciting place where stoichiometry may be applied in terrestrial systems is in soil systems where food webs are becoming increasingly recognized as important regulators of nutrient cycling (Coleman 1996, De Ruiter et al. 1996, Strong et al. 1996). We suggest the following questions as places to start:

1) How does body N:P vary among important terrestrial herbivore and detritivore species? For example, if the growth rate hypothesis of Elser et al. (1996) is correct, small, rapidly growing herbivores such as aphids should be high-P, low-N:P animals, which might explain their preference for feeding on mineral-rich phloem (Blackman and Eastop 1994). Interestingly, Stadler and Muller (1996) have recently shown that production of organic carbon-rich honeydew by aphids is a function of the mineral nutritional status of the plant, and that changes in production of honeydew by aphids may influence biogeochemical cycling in forests.

2) Does differential recycling of N and P via the stoichiometry of CNR affect plant growth status? In soils, proliferation of rapid growth rate, low-N:P detritivores or root predators during the growing season might result in differential recycling of N relative to P, thus accentuating P limitation of plant growth. Such effects might also reduce soil N fixation or enhance nitrification, due to enhanced release of N[H.sub.4].

3) Do terrestrial herbivores/detritivores sequester significant amounts of limiting nutrient, especially P, in their biomass? In the lakes studied by Hassett et al. (1997), [approximately]20% of water column phosphorus on average was sequestered in zooplankton biomass, with some lakes having considerably higher percentages. Similar findings were reported by Hessen et al. (1992). Can this happen in terrestrial systems? It might: recent calculations suggest the amount of P in a rack of moose antlers that are dropped in winter represents 10% of the amount of P that cycles in the surrounding 1 ha of boreal forest (Moen et al, 1998)!

Use of stoichiometric perspectives in stream ecology appears almost completely absent, as this field, to date, has progressed primarily via application of other conceptual frameworks. Perhaps additional progress can be made in understanding lotic ecosystems by bringing the tools of stoichiometry to bear. Questions such as the following might be of interest to pursue in streams.

1) What is the range of variation in elemental composition of periphyton in streams? What environmental factors influence that variation?

2) What differences exist in the elemental composition of important stream consumers, including not only herbivores (“scrapers”), but also detritivores (“collectors”)? Does consumer biomass represent a significant pool of nutrient elements (N, P) in streams?

3) Do stream consumers differentially recycle N and P? If so, does differential nutrient recycling via the stoichiometry of CNR affect periphyton community structure?

4) How does the very high C:nutrient ratio of terrestrial detritus affect nutrient cycling in streams?

5) Does the stoichiometry of CNR differentially affect spiraling length (sensu Newbold et al. 1983) of N and P in streams?

6) Does the food web, via alteration in the stoichiometry of CNR, affect nitrogen fixation or other N transformation processes in streams?

Chemical stoichiometry implies that imbalances in reactants in solution can greatly reduce the rate of a chemical reaction. Ecological stoichiometry implies similar links between balances, imbalances, and rates of “ecological reactions” (predator-prey dynamics, competition, energy flow, nutrient cycling). It has been argued that the field of ecology has experienced an imbalance in emphasis during its historical development (Elser et al. 1996, Reiners 1986, Hessen 1997), with a dominant focus on energy flow and the principles of biological energetics, with little attention to matter outside of the realm of biogeochemical ecosystem study, Furthermore, even in biogeochemical studies, the cycling of matter is generally analyzed one element at a time (e.g., the C cycle, the N cycle, the P cycle; but see Wollast et al. 1993). Imbalance in favor of single-currency approaches in ecology may be slowing the rate of development of the discipline. Increased study of multiple elements, their interactions, and the constraints that biological coupling of those elements place on ecological interactions, may bring our science in closer match to biophysical reality, thus accelerating progress in understanding. Perhaps this paper can serve to catalyze this readjustment in the study of the reciprocal interactions between food web dynamics and nutrient cycling.


J. J. Elser is grateful for the generous support and productive working atmosphere of the Center for Ecological Research, Kyoto University, during the preparation of this paper. Partial support for J. J. Elser during this period was provided by NSF INT-9602466; J. J. Elser also acknowledges support from NSF DEB-9527322. J. Urabe acknowledges support from a Japanese Ministry of Education, Science and Culture, Grant-in-Aid for Creative Basic Research (09NP1501). We are grateful to D. Dobberfuhl, M. Vanni, Y. Olsen, and V. Smith, and members of the graduate stoichiometry seminar at ASU for helpful comments on early versions of this paper.


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