Abstract
The theories developed in ecological stoichiometry (ES) are fundamentally based on traits. Traits directly linked to cell/body stoichiometry, such as nutrient uptake and storage, as well as the associated trade-offs, have the potential to shape ecological interactions such as competition and predation within ecosystems. Further, traits that indirectly influence and are influenced by nutritional requirements, such as cell/body size and growth rate, are tightly linked to organismal stoichiometry. Despite their physiological and ecological relevance, traits are rarely explicitly integrated in the framework of ES and, currently, the major challenge is to more closely inter-connect ES with trait-based ecology (TBE). Here, we highlight four interconnected nutrient trait groups, i.e., acquisition, body stoichiometry, storage, and excretion, which alter interspecific competition in autotrophs and heterotrophs. We also identify key differences between producer-consumer interactions in aquatic and terrestrial ecosystems. For instance, our synthesis shows that, in contrast to aquatic ecosystems, traits directly influencing herbivore stoichiometry in forested ecosystems should play only a minor role in the cycling of nutrients. We furthermore describe how linking ES and TBE can help predict the ecosystem consequences of global change. The concepts we highlight here allow us to predict that increasing N:P ratios in ecosystems should shift trait dominances in communities toward species with higher optimal N:P ratios and higher P uptake affinity, while decreasing N retention and increasing P storage.
Introduction
Ecological stoichiometry (ES) is a framework that links an organism's metabolic demands with the relative supply of elements in the environment (Sterner and Elser, ; Hessen et al., ). It postulates a crucial relationship between the balance of elements, typically but not limited to carbon (C), nitrogen (N), and phosphorus (P), and their role in determining growth and reproduction of organisms as well as in ecological interactions. The recognition of the importance of stoichiometric constraints between consumer needs and prey nutrient content has substantially increased our understanding of trophic interactions. For example, high C:P food is often of low-quality for a variety of organisms including molluscs (Stelzer and Lamberti, ; Fink and Elert, ), crustaceans (Boersma and Kreutzer, ; Meunier et al., , ), insects (Perkins et al., ), fish (Borlongan and Satoh, ; Vrede et al., ), and birds (Grone et al., ). ES has therefore proven to be a highly suitable framework in community ecology, explaining consumer responses to prey food quality (food intake, growth, as well as competition between consumer species, and consumer effects on prey nutrient composition (Sterner, ; Sterner et al., ; Sterner and Hessen, ).
Trait-based ecology (TBE) focuses on functional traits expressed by an organism allowing its growth and survival under distinct environmental conditions (McGill et al., ). Functional traits are the morphological, physiological, phenological, and behavioral characteristics of an organism that influence its performance or fitness. As such, TBE couples biological function to the success of species in a food-web (Litchman and Klausmeier, ; Litchman et al., ; Kremer et al., ). More specifically, TBE focuses on traits rather than taxa, providing a functional approach to understanding ecological interactions. For example, it has been shown that trait diversity can provide a better predictor of primary production as compared to taxonomic diversity (Vogt et al., ). Numerous studies indicate that abiotic parameters, such as temperature, precipitation, and nutrient concentrations, can directly influence spatial and temporal trait patterns both at the population and at the community levels (Brun et al., ). TBE therefore constitutes a powerful tool linking species functional characteristics to their distributions along environmental gradients, as well as to community interactions and ecosystem function.
The framework of ES is essentially, although not explicitly, based on traits such as homeostasis, growth rate, and nutrient uptake. For instance, the relationship between organism size and stoichiometry has been well studied (see below for more detail) but connecting these ES traits with more traditional TBE traits still remains a major challenge. Combining the framework of ES with TBE furthers the coupling of elements to functional traits from subcellular processes to species interactions and ultimately ecosystem dynamics. For example, linking ES with TBE could help studying how variation in traits related to elemental body composition influences organismal fitness (Leal et al., ). The focus of this paper is to explore and synthesize existing insights, and to develop novel connections between ES and TBE. We first review key traits and their elemental requirements, highlighting differences between trophic levels as well as ecosystems. Next, we describe trade-offs between traits that directly or indirectly affect the elemental composition or requirements of organisms. We also develop hypotheses on how different traits could be linked to life history trade-offs, and we outline community and ecosystem consequences of variation in ES traits and identify limitations and opportunities for future research further connecting TBE and ES. This framework will foster collaboration between scientists of different disciplines (freshwater, marine, and terrestrial ecologists) and enhance our understanding of fundamental ecological issues.
Traits and elemental balances
Four interconnected major trait groups affect the balance of energy and elements in organisms: acquisition, cell/body stoichiometry, storage, and excretion. These traits define how organisms interact with their environment as well as with one another, and are, therefore, among the key determinants of ecological niches. This elemental trait framework has already been successfully used to identify how resource imbalances affect basic physiological processes (Frost et al., ). In the following sections, we describe different strategies utilized by autotrophs and heterotrophs to acquire and use C and nutrients, and we explain how these traits directly influence organismal stoichiometry. While traditional stoichiometric approaches are implicitly trait-based, we here aim to fully place life history trade-offs in a stoichiometric context. This conceptual framework should enhance our ability to predict how communities will respond to changes in nutrient conditions in the environment.
Autotrophs
Autotrophs obtain energy (mostly sunlight) and material from different, uncoupled sources within their environment. Therefore, plants needed to develop strategies to store these resources, as the availability of one resource does not guarantee another one. Nutrient uptake strategies are often linked to storage capacity and therefore to plasticity in cellular stoichiometry. When C or nutrients are taken up and fixed by autotrophs, they may be used immediately for growth or accumulate as storage pools for later use (Chapin, ; Reynolds, ). Due to their need and hence capacity to store nutrients, autotroph C:N:P ratios greatly vary with available nutrient ratios, indicating a high flexibility in chemical composition and a lack of homeostasis (Sterner and Elser, ; Meunier et al., ). This can result in different competitive strategies between various autotroph species. For instance, for phytoplankton, three major nutrient acquisition strategies have been proposed (Sommer, ): (1) velocity-adapted species, or r-strategists, with high maximum nutrient uptake rates and high maximum growth rates that are able to directly utilize nutrient pulses for growth, (2) storage-adapted species, with high rates of nutrient uptake but lower maximum growth rates that have the ability to store excess nutrients, and (3) affinity-adapted species, or K-strategists, with the ability to effectively take up and assimilate growth-limiting nutrients even at low concentrations, a strategy that is advantageous in oligotrophic environments. Comparable nutrient-acquisition strategies exist in terrestrial systems, and nutrient availability strongly influences interspecific plant interactions and community composition (Zemunik et al., ). For instance, tall-growing species with high growth rates are generally favored by high nutrient inputs at the expense of species with conservative growth strategies (Diekmann and Falkengren-Grerup, ). Physiological traits such as nutrient requirements also alter interspecific competition, giving a competitive advantage to species with high-nutrient affinity (Price and Morgan, ).
Such different strategies may affect the stoichiometry of autotrophs. For instance, high growth rates may lead to higher demands for P (see below), storage will increase cellular quota of distinct elements, while high affinities will reduce the minimum nutrient requirements (Figure 1). Stoichiometric plasticity will furthermore be determined by physiological limits, as was shown for phytoplankton N:P ratios that increased non-linearly with increasing N:P supply ratios (Rhee, ; Persson et al., ). In other words, the upper limit of nutrient quota is determined by storage capabilities, while the lower limits are determined by minimal structural and functional requirements, as well as the affinity for a nutrient. These boundaries within which the organism's stoichiometry can fluctuate represent the “homeostatic capacity” parameter defined by Meunier et al. (). This parameter corresponds to the boundaries within which the organism's body stoichiometry can fluctuate and therefore characterizes storage capacity. Consequently, storage- and affinity-adapted species will be generally characterized by variable stoichiometric composition while velocity-adapted species with high P demands will have low cellular N:P ratios (Hillebrand et al., ).
Figure 1
Variation in N:P ratios between organisms often reflect functional differences, such as the ones that have been described for optimal N:P ratios (Güsewell, ; Hillebrand et al., ), i.e., the N:P ratio at which growth is maximized. In aquatic environment for example, a recent meta-analysis identified a scaling relationship between maximum growth rate and phytoplankton nutrient demand (Hillebrand et al., ). Phytoplankton N:P ratios decrease with increasing growth rates and simultaneously display decreasing variance, particularly driven by P limitation, which follows predictions of the Growth Rate Hypothesis (Sterner, ; Elser et al., ; Sterner and Elser, ). This suggests that fast growing phytoplankton species, or within a species under less severe limiting conditions, phytoplankton are relatively more P-rich. Using a trait-based eco-evolutionary model, Klausmeier et al. () showed that different environmental conditions select for species with different N:P ratios: P-rich conditions select for fast growers with low N:P, while P-limited conditions select for better P competitors with higher N:P ratios (due to their investment in resource acquisition proteins, Figure 1). Not only the N:P ratio itself but the form of N and P available to phytoplankton matters. While we may expect higher biomass of small phytoplankton at lower N:P supply due to their generally fast growth (and thus high P requirements), higher picophytoplankton densities are only observed under high N:P conditions (Figure 1; Glibert, ) which is likely caused by changes in N redox and relative proportions of reduced relative to oxidized N (Glibert et al., ). A review of interspecific differences in N:P ratios of terrestrial plants concluded that N:P ratios correlate negatively with maximum relative growth rate in herbaceous and woody plants (Güsewell, ). Species with inherently low N:P ratios are predicted to dominate N-limited communities and should be favored during P fertilization (Figure 2; Tilman, ). Therefore, nutrient supply and composition often shape terrestrial and aquatic autotroph community composition by species sorting as well as by dynamic shifts in species' C:N:P stoichiometry (Sterner and Elser, ; Persson et al., ; Meunier et al., ).
Figure 2
Heterotrophs
Autotrophs' stoichiometry also reflects their quality as food for herbivores. Since autotrophs are usually more C-rich than animals, herbivores typically ingest a diet rich in C but deficient in nutrients (Sterner and Elser,
Nutrient storage capacity is a much more confined trait in heterotrophs than in autotrophs resulting in more constrained body composition (Persson et al.,
Stoichiometric homeostasis also results from adjustments in the elemental ratios of recycled material. The stoichiometry of excreted material is influenced by both the consumer's stoichiometry, e.g., low N:P consumers will have high N:P excretion, as well as by the resource stoichiometry, e.g., the excreted N:P ratio will increase with increasing resource N:P ratio (Vanni,
Interspecific variations in recycling traits will also have consequences at the ecosystem scale. For instance, experiments in lakes provided evidence that the replacement of the high body N:P copepods with low N:P Daphnia caused a transition from N to P limitation for primary producers (Sterner,
Trait connections
Correlative relationships
Several studies have linked elemental stoichiometry to distinct species traits in order to explain ecosystem structure (for an overview see Table 1). As we previously mentioned, the tight relationship between the body N:P ratio and organismal growth rate is formulated in the Growth Rate Hypothesis (Table 1), a central hypothesis in ES. It postulates that species with high growth rates have more ribosomal RNA content for rapid protein synthesis than slow-growing species (Sterner and Elser,
Table 1
| ES characteristics | Physiological trait | Description | Citations |
|---|---|---|---|
| AUTOTROPHS | |||
| Organismal C:P and N:P contents | Growth rate | Autotrophs of lower C:P and N:P contents exhibit greater maximum growth rates | Güsewell, |
| Structure (wood investment) | Investment in woody structures reduces plant tissue N and P contents | Han et al., | |
| Homeostasis | Temporal stability | Homeostatic plant species show reduced temporal variation in grassland communities | Yu et al., |
| Maximum growth rate | Homeostatic phytoplankton exhibit faster growth rates | Hillebrand et al., | |
| Nutrient uptake | Cell size | Phytoplankton nutrient uptake affinities decline with increased cell size | Irwin et al., |
| Growth rate | Higher phytoplankton maximum growth rates with greater nutrient uptake affinity | Edwards et al., | |
| Resistance to grazing | Phytoplankton resistant to grazing may be poor competitors for limiting nutrients | Litchman and Klausmeier, | |
| Symbioses (mycorrhizae) | Mycorrhizal symbionts increase host plant nutrient acquisition | Brundrett, | |
| HETEROTROPHS | |||
| Organismal C:P and N:P contents | Growth rate | Heterotrophs of lower C:P and N:P exhibit greater maximum growth rates | Elser et al., |
| Defense (bone investment) | Investment in bone decreases vertebrate body C:P contents | El-Sabaawi et al., | |
| Ontogeny | Vertebrate C:P and N:P decline during development, whereas invertebrate C:P and N:P increase during development | Pilati and Vanni, | |
| Sex | Divergent P contents between males and females | Back and King, | |
| Homeostasis | Maximum growth rate | Species of flexible homeostasis tend to have high maximum growth rates | Hood and Sterner, |
| Sex | Divergent flexibility in body P contents between males and females | Goos et al., | |
| Generalism vs. specialism? | Homeostatic consumers may exhibit greater nutritional specialism | Sperfeld et al., | |
| Nutrient assimilation | Gut residence time or gut length | Increased gut length confers increased nutrient assimilation efficiencies | Liess et al., |
| Consumer-resource elemental imbalance | Trophic level | Higher trophic levels exhibit reduced consumer-resource elemental imbalances | Lemoine et al., |
| Omnivory | Selective feeding may reduce imbalances between consumers and resources | Snyder et al., | |
| Nutrient recycling | Growth rate | Faster-growing animals recycle less P | Elser et al., |
| Body size | Higher mass-specific nutrient recycling by organisms of smaller body size | Allgeier et al., | |
| Phylogeny | Taxonomic identity affects size scaling and rates of N and P excretion | Allgeier et al., | |
Relationships between elemental stoichiometry characteristics and physiological traits among autotrophs and heterotrophs, with abbreviated description of the linkage and associated citations.
Interestingly, organisms' size and growth rate are usually negatively correlated (Table 1), which implies that, based on the growth rate hypothesis, smaller heterotrophic organisms might generally have lower N:P ratios (Figure 2). These explicit connections between body size, ontogeny, and N:P stoichiometry have been well documented (Elser et al.,
Figure 3

Influence of variability in resource supply on traits. Increasing variability is expected to influence the trade-off between fast growth and investment in storage. Higher variability should therefore yield communities with larger organisms with greater storage and homeostatic capacities, but slower maximal growth rates. Moreover, the intermediate disturbance hypothesis suggests that local species diversity should be maximized when ecological disturbance is neither too rare nor too frequent (Wilkinson,
Changes in environmental conditions can also lead to evolutionary changes in traits. The mutual interactions between the evolved functional traits and environment characteristics therefore have gained increasing interest over the past two decades. In particular, understanding how genome and proteome adaptations are shaped by selection on growth-related traits and the parameters determining the extent of stoichiometrically relevant variation in genomes across taxa are increasingly studied (Kay et al.,
Trade-offs
Natural selection balances traits associated with the three main missions of any organism, i.e., to eat, survive, and reproduce in order to maximize fitness. However, it is generally not possible to maximize all traits simultaneously, particularly as resources are often limiting. Trade-offs are therefore inevitable and different organisms specialize in various aspects of their life history. Such trade-offs are typically enabled by trait specialization and plasticity. For example, consumers with lower P requirements and higher body N:P ratios should be abundant in low P environments despite having reduced maximum growth rates (Sterner,
Similarly, low nutrient availability in the environment forces plants to adopt different strategies. For example, they can invest in storage traits and preferentially accumulate N rather than P (Chapin et al.,
In their review of phytoplankton traits and trade-offs, Litchman and Klausmeier (
Implications
Understanding the link between nutrient stoichiometry and organismal traits can help predict how human-induced changes in biogeochemical cycles will alter the interaction between producers' stoichiometry and consumer elemental requirements. Human activities have altered the C, N, and P biogeochemical cycles on a global scale (Peňuelas et al.,
Conclusion
Coupling functional traits to the stoichiometry of organisms allows a more general understanding of ecological interactions. Specifically, optimal body N:P ratios, nutrient uptake and storage traits, as well as their associated trade-offs, have the potential to drive species competition and thereby influence food web interactions and ecosystem dynamics. Quantifying the contribution of these traits to ecosystem services (i.e., C sequestration, water quality) represents a promising avenue for research into changes in biogeochemical cycling associated with global environmental change. At the same time, traits indirectly coupled to elemental demands, such as cell/body size and growth rate have a strong influence on, and are affected by, organismal stoichiometry. Therefore, combining observations and ideas from ES and TBE offers a unified framework that enables answering a wide array of complex ecological questions, for instance how biological communities will perform under changing environmental conditions. Linking and applying multiple ecological frameworks allows crosstalk between the various scientific disciplines, fostering the exchange of comparable efforts in understanding the complexity of ecosystem structure and functioning.
Statements
Author contributions
The manuscript was written by CM. Table 1 was created by HH. Figure 1 was created by DW. Figures 2, 3 were created by CM. All co-authors helped to evaluate and edit the manuscript.
Acknowledgments
This article summarizes the work done in the “Workshop on Biological Stoichiometry and Trait-Based Ecology” that took place during the Conference On Biological Stoichiometry (2015, Trent, Canada). This conference was supported by the David Schindler Professorship of Aquatic Sciences at Trent University and by Trent University. The workshop was supported by the Canadian Institute of Ecology and Evolution. We thank all workshop participants for their input into the discussions: Claudia Acquisti, Thomas Anderson, Jeff Back, Stephen Baines, Esteban Balseiro, Gergely Boros, Krista Capps, Sarah Collins, Richard Connon, James Cotner, Michael Danger, Sanatan Das Gupta, Steven Declerck, Lenore Doumas, Dan Durston, Jonathan Ebel, James Elser, Michelle Evans-White, Carolyn Faithfull, Isabel Fernandes, Laura Fidalgo, Michał Filipiak, Stephanie Fong, Paul Frost, Andrea Gall, Pat Glibert, Casey Godwin, Angelica L. Gonzalez, Jared Goos, Jin-Sheng He, Puni Jeyasingh, Susan Kilham, Ryan King, Jude Kong, James Larson, Cecilia Laspoumaderes, Kimberley Lemmen, Shawn Leroux, Patrick Lind, Paloma Lopes, Keeley MacNeill, Adam Martiny, Peter B. McIntyre, Katie Miller, Eric Moody, David Ott, Rachel Paseka, Angela Peace, Amber Rock, Anna Rożen, Amanda Rugenski, Andrew Sanders, Jennifer Schmitt, Kimberly Schulz, Ryan Sherman, Robert Sterner, Maren Striebel, Caroline Turner, Jotaro Urabe, Michael Vanni, Stoycho Velkovsky, Mandy Velthuis, Nicole Wagner, Wei Wang, Tanner Williamson, and Donald Yee.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1
AllgeierJ. E.WengerS. J.RosemondA. D.SchindlerD. E.LaymanC. A. (2015). Metabolic theory and taxonomic identity predict nutrient recycling in a diverse food web. Proc. Natl. Acad. Sci. U.S.A.112, E2640–E2647. 10.1073/pnas.1420819112
2
BackJ. A.KingR. S. (2013). Sex and size matter: ontogenetic patterns of nutrient content of aquatic insects. Freshw. Sci.32, 837–848. 10.1899/12-181.1
3
BanseK. (1976). Rates of growth, respiration and photosynthesis of unicellular algae as related to cell size—a review. J. Phycol. 12, 135–140.
4
BobbinkR.HicksK.GallowayJ.SprangerT.AlkemadeR.AshmoreM.et al. (2010). Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis. Ecol. Appl.20, 30–59. 10.1890/08-1140.1
5
BoersmaM.KreutzerC. (2002). Life at the edge: is food quality really of minor importance at low quantities?Ecology83, 2552–2561. 10.1890/0012-9658(2002)083[2552:LATEIF]2.0.CO;2
6
BorlonganI. G.SatohS. (2001). Dietary phosphorus requirement of juvenile milkfish, Chanos chanos (Forsskal). Aquac. Res.32, 26–32. 10.1046/j.1355-557x.2001.00003.x
7
BrunP.PayneM. R.KiørboeT. (2016). Trait biogeography of marine copepods–an analysis across scales. Ecol. Lett.19, 1403–1413. 10.1111/ele.12688
8
BrundrettM. (2009). Mycorrhizal associations and other means of nutrition of vascular plants: understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant Soil320, 37–77. 10.1007/s11104-008-9877-9
9
CalbetA. (2008). The trophic roles of microzooplankton in marine systems. ICES J. Mar. Sci.65, 325–331. 10.1093/icesjms/fsn013
10
CarrilloP.Villar-ArgaizM.Medina-SánchezJ. M. (2001). Relationship between N:P ratio and growth rate during the life cycle of calanoid copepods: an in situ measurement. J. Plankton Res.23, 537–547. 10.1093/plankt/23.5.537
11
CebrianJ. (1999). Patterns in the fate of production in plant communities. Am. Nat.154, 449–468. 10.1086/303244
12
ChapinF. S. (1980). The mineral nutrition of wild plants. Ann. Rev. Ecol. Syst.11, 233–260. 10.1146/annurev.es.11.110180.001313
13
ChapinI. E.SchulzeA.MooneyH. A. (1990). The ecology and economics of storage in plants. Annu. Rev. Ecol. Syst.21, 423–447. 10.1146/annurev.es.21.110190.002231
14
CherifM.LoreauM. (2009). When microbes and consumers determine the limiting nutrient of autotrophs: a theoretical analysis. Proc. R. Soc. Lond. B Biol. Sci.276, 487–497. 10.1098/rspb.2008.0560
15
CherifM.LoreauM. (2013). Plant–herbivore–decomposer stoichiometric mismatches and nutrient cycling in ecosystems. Proc. R. Soc. B Biol. Sci.280:20122453. 10.1098/rspb.2012.2453
16
CotnerJ. B.HallE. K.ScottT.HeldalM. (2010). Freshwater bacteria are stoichiometrically flexible with a nutrient composition similar to seston. Front. Microbiol.1:132. 10.3389/fmicb.2010.00132
17
CrossW. F.BensteadJ. P.FrostP. C.ThomasS. A. (2005). Ecological stoichiometry in freshwater benthic systems: recent progress and perspectives. Freshw. Biol.50, 1895–1912. 10.1111/j.1365-2427.2005.01458.x
18
DeclerckS. A.MaloA. R.DiehlS.WaasdorpD.LemmenK. D.ProiosK.et al. (2015). Rapid adaptation of herbivore consumers to nutrient limitation: eco-evolutionary feedbacks to population demography and resource control. Ecol. Lett.18, 553–562. 10.1111/ele.12436
19
DemottW. R.GulatiR. D.SiewertsenK. (1998). Effects of phosphorus-deficient diets on the carbon and phosphorus balance of Daphnia magna. Limnol. Oceanogr.43, 1147–1161. 10.4319/lo.1998.43.6.1147
20
DiekmannM.Falkengren-GrerupU. (2002). Prediction of species response to atmospheric nitrogen deposition by means of ecological measures and life history traits. J. Ecol.90, 108–120. 10.1046/j.0022-0477.2001.00639.x
21
DoddsW. K.CollinsS.HamiltonS.TankJ.JohnsonS.WebsterJ.et al. (2014). You are not always what we think you eat: selective assimilation across multiple whole-stream isotopic tracer studies. Ecology95, 2757–2767. 10.1890/13-2276.1
22
EdwardsK. F.KlausmeierC. A.LitchmanE. (2011). Evidence for a three-way tradeoff between nitrogen and phosphorus competitive abilities and cell size in phytoplankton. Ecology92, 2085–2095. 10.1890/11-0395.1
23
EdwardsK. F.ThomasM. K.KlausmeierC. A.LitchmanE. (2012). Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57, 554–566. 10.4319/lo.2012.57.2.0554
24
El-SabaawiR. W.WarbanskiM. L.RudmanS. M.HovelR.MatthewsB. (2016). Investment in boney defensive traits alters organismal stoichiometry and excretion in fish. Oecologia181, 1209–1220. 10.1007/s00442-016-3599-0
25
ElserJ. J.AcharyaK.KyleM.CotnerJ.MakinoW.MarkowT.et al. (2003). Growth rate–stoichiometry couplings in diverse biota. Ecol. Lett.6, 936–943. 10.1046/j.1461-0248.2003.00518.x
26
ElserJ. J.DobberfuhlD. R.MackayN. A.SchampelJ. H. (1996). Organism size, life history, and N:P stoichiometry. Bioscience46, 674–684. 10.2307/1312897
27
ElserJ. J.ElserM. M.MackayN. A.CarpenterS. R. (1988). Zooplankton-mediated transitions between N- and P-limited algal growth. Limnol. Oceanogr.33, 1–14. 10.4319/lo.1988.33.1.0001
28
ElserJ. J.FaganW. F.DennoR. F.DobberfuhlD. R.FolarinA.HubertyA.et al. (2000b). Nutritional constraints in terrestrial and freshwater foodwebs. Nature408, 578–580. 10.1038/35046058
29
ElserJ. J.SternerR. W.GorokhovaE.FaganW. F.MarkowT. A.CotnerJ. B.et al. (2000c). Biological Stoichiometry from genes to ecosystem. Ecol. Lett.3, 540–550. 10.1046/j.1461-0248.2000.00185.x
30
ElserJ. J.UrabeJ. (1999). The Stoichiometry of consumer-driven nutrient recycling: theory, observations, and consequences. Ecology80, 735–751. 10.1890/0012-9658(1999)080[0735:TSOCDN]2.0.CO;2
31
ElserJ. J.O'brienW.DobberfuhlD.DowlingT. (2000a). The evolution of ecosystem processes: growth rate and elemental stoichiometry of a key herbivore in temperate and arctic habitats. J. Evol. Biol.13, 845–853. 10.1046/j.1420-9101.2000.00215.x
32
FanesiA.RavenJ. A.GiordanoM. (2014). Growth rate affects the responses of the green alga Tetraselmis suecica to external perturbations. Plant Cell Environ.37, 512–519. 10.1111/pce.12176
33
FinkP.ElertE. V. (2006). Physiological responses to stoichiometric constraints: nutrient limitation and compensatory feeding in a freshwater snail. Oikos115, 484–494. 10.1111/j.2006.0030-1299.14951.x
34
FoxJ. W. (2013). The intermediate disturbance hypothesis should be abandoned. Trends Ecol. Evol.28, 86–92. 10.1016/j.tree.2012.08.014
35
FrischD.MortonP. K.ChowdhuryP. R.CulverB. W.ColbourneJ. K.WeiderL. J.et al. (2014). A millennial-scale chronicle of evolutionary responses to cultural eutrophication in Daphnia. Ecol. Lett.17, 360–368. 10.1111/ele.12237
36
FrostP. C.Evans-WhiteM. A.FinkelZ. V.JensenT. C.MatzekV. (2005). Are you what you eat? Physiological constraints on organismal stoichiometry in an elementally imbalanced world. Oikos109, 18–28. 10.1111/j.0030-1299.2005.14049.x
37
GilloolyJ. F.AllenA. P.BrownJ. H.ElserJ. J.Del RioC. M.SavageV. M.et al. (2005). The metabolic basis of whole-organism RNA and phosphorus content. Proc. Natl. Acad. Sci. U.S.A.102, 11923–11927. 10.1073/pnas.0504756102
38
GiordanoM. (2013). Homeostasis: an underestimated focal point of ecology and evolution. Plant Sci.211, 92–101. 10.1016/j.plantsci.2013.07.008
39
GlibertP. M. (2016). Margalef revisited: a new phytoplankton mandala incorporating twelve dimensions, including nutritional physiology. Harmful Algae55, 25–30. 10.1016/j.hal.2016.01.008
40
GlibertP. M.WilkersonF. P.DugdaleR. C.RavenJ. A.DupontC. L.LeavittP. R.et al. (2016). Pluses and minuses of ammonium and nitrate uptake and assimilation by phytoplankton and implications for productivity and community composition, with emphasis on nitrogen-enriched conditions. Limnol. Oceanogr.61, 165–197. 10.1002/lno.10203
41
GonzálezA. L.FariñaJ. M.KayA. D.PintoR.MarquetP. A. (2011). Exploring patterns and mechanisms of interspecific and intraspecific variation in body elemental composition of desert consumers. Oikos120, 1247–1255. 10.1111/j.1600-0706.2010.19151.x
42
GoosJ. M.FrenchB. J.RelyeaR. A.CothranR. D.JeyasinghP. D. (2014). Sex-specific plasticity in body phosphorus content of Hyalella amphipods. Hydrobiologia722, 93–102. 10.1007/s10750-013-1682-7
43
GrizzettiB.BouraouiF.AloeA. (2012). Changes of nitrogen and phosphorus loads to European seas. Glob. Chang. Biol.18, 769–782. 10.1111/j.1365-2486.2011.02576.x
44
GroneA.SwayneD. E.NagodeL. A. (1995). Hypophosphatemic rickets in rheas (Rhea americana). Vet. Pathol.32, 324–327. 10.1177/030098589503200318
45
GroverJ. P. (1991). Resource competition in a variable environment: phytoplankton growing according to the variable-internal-stores model. Am. Nat.138, 811–835. 10.1086/285254
46
GüsewellS. (2004). N: P ratios in terrestrial plants: variation and functional significance. New Phytol.164, 243–266. 10.1111/j.1469-8137.2004.01192.x
47
HallS. R.LeiboldM. A.LytleD. A.SmithV. H. (2004). Stoichiometry and planktonic grazer composition over gradients of light, nutrients, and predation risk. Ecology85, 2291–2301. 10.1890/03-0471
48
HanW.FangJ.GuoD.ZhangY. (2005). Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytol.168, 377–385. 10.1111/j.1469-8137.2005.01530.x
49
HessenD. O.ElserJ. J.SternerR. W.UrabeJ. (2013). Ecological stoichiometry: an elementary approach using basic principles. Limnol. Oceanogr.58, 2219–2236. 10.4319/lo.2013.58.6.2219
50
HillebrandH.SteinertG.BoersmaM.MalzahnA. M.MeunierC. L.PlumC.et al. (2013). Goldman revisited: faster growing phytoplankton has lower N:P and lower stoichiometric flexibility. Limnol. Oceanogr.58, 2076–2088. 10.4319/lo.2013.58.6.2076
51
HoodJ. M.SternerR. W. (2014). Carbon and phosphorus linkages in Daphnia growth are determined by growth rate, not species or diet. Funct. Ecol.28, 1156–1165. 10.1111/1365-2435.12243
52
IrwinA. J.FinkelZ. V.SchofieldO. M.FalkowskiP. G. (2006). Scaling-up from nutrient physiology to the size-structure of phytoplankton communities. J. Plankton Res.28, 459–471. 10.1093/plankt/fbi148
53
JeyasinghP. D.CothranR. D.ToblerM. (2014). Testing the ecological consequences of evolutionary change using elements. Ecol. Evol.4, 528–538. 10.1002/ece3.950
54
JeyasinghP. D.WeiderL. J. (2007). Fundamental links between genes and elements: evolutionary implications of ecological stoichiometry. Mol. Ecol.16, 4649–4661. 10.1111/j.1365-294X.2007.03558.x
55
KagataH.OhgushiT. (2011). Ecosystem consequences of selective feeding of an insect herbivore: palatability–decomposability relationship revisited. Ecol. Entomol.36, 768–775. 10.1111/j.1365-2311.2011.01327.x
56
KayA. D.AshtonI. W.GorokhovaE.KerkhoffA. J.LiessA.LitchmanE. (2005). Toward a stoichiometric framework for evolutionary biology. Oikos109, 6–17. 10.1111/j.0030-1299.2005.14048.x
57
KlausmeierC. A.LitchmanE.DaufresneT.LevinS. A. (2004). Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton. Nature429, 171–174. 10.1038/nature02454
58
KneitelJ. M.ChaseJ. M. (2004). Trade-offs in community ecology: linking spatial scales and species coexistence. Ecol. Lett. 7, 69–80. 10.1046/j.1461-0248.2003.00551.x
59
KnollL. B.McIntyreP. B.VanniM. J.FleckerA. S. (2009). Feedbacks of consumer nutrient recycling on producer biomass and stoichiometry: separating direct and indirect effects. Oikos118, 1732–1742. 10.1111/j.1600-0706.2009.17367.x
60
KremerC. T.WilliamsA. K.FiniguerraM.FongA. A.KellermanA.PaverS. F.et al. (2016). Realizing the potential of trait-based aquatic ecology: new tools and collaborative approaches. Limnol. Oceanogr.62, 253–271. 10.1002/lno.10392
61
LealM. C.SeehausenO.MatthewsB. (2016). The ecology and evolution of stoichiometric phenotypes. Trends Ecol. Evol.32, 108–117. 10.1016/j.tree.2016.11.006
62
LeiboldM. A. (1996). A graphical model of keystone predators in food webs: trophic regulation of abundance, incidence, and diversity patterns in communities. Am. Natural. 147, 784–812. 10.1086/285879
63
LemoineN. P.GieryS. T.BurkepileD. E. (2014). Differing nutritional constraints of consumers across ecosystems. Oecologia174, 1367–1376. 10.1007/s00442-013-2860-z
64
LiW.StevensM. H. H. (2012). Fluctuating resource availability increases invasibility in microbial microcosms. Oikos121, 435–441. 10.1111/j.1600-0706.2011.19762.x
65
LiessA.GuoJ.LindM. I.RoweO. (2015). Cool tadpoles from Arctic environments waste fewer nutrients–high gross growth efficiencies lead to low consumer-mediated nutrient recycling in the North. J. Anim. Ecol.84, 1744–1756. 10.1111/1365-2656.12426
66
LitchmanE.KlausmeierC. A. (2008). Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. Syst.39, 615–639. 10.1146/annurev.ecolsys.39.110707.173549
67
LitchmanE.KlausmeierC. A.YoshiyamaK. (2009). Contrasting size evolution in marine and freshwater diatoms. Proc. Natl. Acad. Sci. U.S.A.106, 2665–2670. 10.1073/pnas.0810891106
68
LitchmanE.OhmanM. D.KiørboeT. (2013). Trait-based approaches to zooplankton communities. J. Plankton Res.35, 473–484. 10.1093/plankt/fbt019
69
LöderM. G. J.MeunierC.WiltshireK. H.BoersmaM.AberleN. (2011). The role of ciliates, heterotrophic dinoflagellates and copepods in structuring spring plankton communities at Helgoland Roads, North Sea. Mar. Biol.158, 1551–1580. 10.1007/s00227-011-1670-2
70
McGillB. J.EnquistB. J.WeiherE.WestobyM. (2006). Rebuilding community ecology from functional traits. Trends Ecol. Evol.21, 178–185. 10.1016/j.tree.2006.02.002
71
MéndezM.KarlssonP. S. (2005). Nutrient stoichiometry in Pinguicula vulgaris nutrient availability, plant size, and reproductive status. Ecology86, 982–991. 10.1890/04-0354
72
MeunierC. L.BoersmaM.WiltshireK. H.MalzahnA. M. (2016a). Zooplankton eat what they need: copepod selective feeding and potential consequences for marine systems Oikos125, 50–58. 10.1111/oik.02072
73
MeunierC. L.GundaleM. J.SánchezI. S.LiessA. (2016b). Impact of nitrogen deposition on forest and lake food webs in nitrogen limited environments. Glob. Chang. Biol.22, 164–179. 10.1111/gcb.12967
74
MeunierC. L.HantzscheF. M.Cunha-DupontA. Ö.HaafkeJ.OppermannB.MalzahnA. M.et al. (2012). Intraspecific selectivity, compensatory feeding, and flexible homeostasis in the phagotrophic flagellate Oxyrrhis marina: three ways to handle food quality fluctuations. Hydrobiologia680, 53–62. 10.1007/s10750-011-0900-4
75
MeunierC. L.MalzahnA. M.BoersmaM. (2014). A new approach to homeostatic regulation: towards a unified view of physiological and ecological concepts. PLoS ONE9:e107737. 10.1371/journal.pone.0107737
76
MontechiaroF.GiordanoM. (2010). Compositional homeostasis of the dinoflagellate Protoceratium reticulatum grown at three different pCO2. J. Plant Physiol.167, 110–113. 10.1016/j.jplph.2009.07.013
77
PeňuelasJ.SardansJ.Rivas-UbachA.JanssensI. A. (2012). The human-induced imbalance between C, N and P in Earth's life system. Glob. Chang. Biol.18, 3–6. 10.1111/j.1365-2486.2011.02568.x
78
PerkinsM. C.WoodsH. A.HarrisonJ. F.ElserJ. J. (2004). Dietary phosphorus affects the growth of larval Manduca sexta. Arch. Insect Biochem. Physiol. Behav.55, 153–168. 10.1002/arch.10133
79
PerssonJ.FinkP.GotoA.HoodJ. M.JonasJ.KatoS. (2010). To be or not to be what you eat: regulation of stoichiometric homeostasis among autotrophs and heterotrophs. Oikos119, 741–751. 10.1111/j.1600-0706.2009.18545.x
80
PilatiA.VanniM. J. (2007). Ontogeny, diet shifts, and nutrient stoichiometry in fish. Oikos116, 1663–1674. 10.1111/j.0030-1299.2007.15970.x
81
PlougH.StolteW.EppingE. H.JørgensenB. B. (1999). Diffusive boundary layers, photosynthesis, and respiration of the colony-forming plankton alga. Phaeocystis sp. Limnol. Oceanogr. 44, 1949–1958. 10.4319/lo.1999.44.8.1949
82
PriceJ. N.MorganJ. W. (2007). Vegetation dynamics following resource manipulations in herb-rich woodland. Plant Ecol.188, 29–37. 10.1007/s11258-006-9145-0
83
ReichwaldtE. S. (2008). Food quality influences habitat selection in Daphnia. Freshw. Biol.53, 872–883. 10.1111/j.1365-2427.2007.01945.x
84
ReynoldsC. (1988). Functional Morphology and the Adaptive Strategies of Freshwater Phytoplankton. Growth and Reproductive Strategies of Freshwater Phytoplankton. Cambridge: Cambridge University Press.
85
ReynoldsC. S. (1984). The Ecology of Freshwater Phytoplankton. Cambridge: Cambridge University Press
86
ReynoldsC. S. (2006). The Ecology of Phytoplankton.Cambridge; New York, NY: Cambridge University Press.
87
RheeG.-Y. (1978). Effects of N:P atomic ratios and nitrate limitation on algal growth, cell composition, and nitrate uptake. Limnol. Oceanogr.23, 10–25. 10.4319/lo.1978.23.1.0010
88
SardansJ.Rivas-UbachA.PeñuelasJ. (2012). The elemental stoichiometry of aquatic and terrestrial ecosystems and its relationships with organismic lifestyle and ecosystem structure and function: a review and perspectives. Biogeochemistry111, 1–39. 10.1007/s10533-011-9640-9
89
SherwoodT. K.PigfordR. L.WilkeC. R. (1975). Mass Transfer. McGraw-Hill.
90
ShimizuY.UrabeJ. (2008). Regulation of phosphorus stoichiometry and growth rate of consumers: theoretical and experimental analyses with Daphnia. Oecologia155, 21–31. 10.1007/s00442-007-0896-7
91
SmaydaT. J. (1997). Harmful algal blooms: their ecophysiology and general relevance to phytoplankton blooms in the sea. Limnol. Oceanogr.42, 1137–1153. 10.4319/lo.1997.42.5_part_2.1137
92
SnyderM. N.SmallG. E.PringleC. M. (2015). Diet-switching by omnivorous freshwater shrimp diminishes differences in nutrient recycling rates and body stoichiometry across a food quality gradient. Freshw. Biol.60, 526–536. 10.1111/fwb.12481
93
SommerU. (1984). The paradox of the plankton: fluctuations of phosphorus availability maintain diversity of phytoplankton in flow-through cultures. Limnol. Oceanogr.29, 633–636. 10.4319/lo.1984.29.3.0633
94
SommerU.GliwiczZ. M.LampertW.DuncanA. (1986). The PEG-model of seasonal succession of planktonic events in fresh waters. Arch. Hydrobiol. 106, 433–471.
95
SperfeldE.WagnerN. D.HalvorsonH. M.MalishevM.RaubenheimerD. (2017). Bridging Ecological Stoichiometry and Nutritional Geometry with homeostasis concepts and integrative models of organism nutrition. Funct. Ecol.31, 286–296. 10.1111/1365-2435.12707
96
StelzerR. S.LambertiG. A. (2002). Ecological stoichiometry in running waters: periphyton chemical composition and snail growth. Ecology83, 1039–1051. 10.1890/0012-9658(2002)083[1039:ESIRWP]2.0.CO;2
97
SternerR. W. (1990). The ratio of nitrogen to phosphorus resupplied by herbivores: zooplankton and the algal competitive arena. Am. Nat.136, 209–229. 10.1086/285092
98
SternerR. W. (1995a). Elemental stoichiometry of species in ecosystems, in Linking Species and Ecosystems, eds JonesC. G.LawtonJ. H. (New York, NY: Chapman and Hall), 240–252.
99
SternerR. W. (1995b). Linking Species and Ecosystems. New York, NY: Chapman & Hall.
100
SternerR. W.ElserJ. J. (2002). Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere. Princeton; Oxford: Princeton University Press.
101
SternerR. W.ElserJ. J.HessenD. O. (1992). Stoichiometric relationships among producers, consumers and nutrient cycling in pelagic ecosystems. Biogeochemistry17, 49–67. 10.1007/BF00002759
102
SternerR. W.HessenD. O. (1994). Algal nutrient limitation and the nutrition of aquatic herbivores. Annu. Rev. Ecol. Syst.25, 1–29. 10.1146/annurev.es.25.110194.000245
103
ThingstadT. F.ØvreåsL.EggeJ. K.LøvdalT.HeldalM. (2005). Use of non-limiting substrates to increase size; a generic strategy to simultaneously optimize uptake and minimize predation in pelagic osmotrophs?Ecol. Lett. 8, 675–682. 10.1111/j.1461-0248.2005.00768.x
104
TiegsS. D.BervenK. A.CarmackD. J.CappsK. A. (2016). Stoichiometric implications of a biphasic life cycle. Oecologia180, 853–863. 10.1007/s00442-015-3504-2
105
TilmanD. (1982). Resource Competition and Community Structure. Princeton, NJ: Princeton University Press.
106
TilmanD. (1997). 8: Mechanisms of Plant Competition. Oxford: Blackwell Science.
107
Van De WaalD. B.VerschoorA. M.VerspagenJ. M.Van DonkE.HuismanJ. (2010). Climate-driven changes in the ecological stoichiometry of aquatic ecosystems. Front. Ecol. Environ.8, 145–152. 10.1890/080178
108
VanniM. J. (2002). Nutrient cycling by animals in freshwater ecosystems. Annu. Rev. Ecol. Syst.33, 341–370. 10.1146/annurev.ecolsys.33.010802.150519
109
VanniM. J.FleckerA. S.HoodJ. M.HeadworthJ. L. (2002). Stoichiometry of nutrient recycling by vertebrates in a tropical stream: linking species identity and ecosystem processes. Ecol. Lett.5, 285–293. 10.1046/j.1461-0248.2002.00314.x
110
VanniM. J.McIntyreP. B. (2016). Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis. Ecology97, 3460–3471. 10.1002/ecy.1582
111
VogtR. J.BeisnerB. E.PrairieY. T. (2010). Functional diversity is positively associated with biomass for lake diatoms. Freshw. Biol.55, 1636–1646. 10.1111/j.1365-2427.2010.02397.x
112
VredeT.DrakareS.EklövP.HeinA.LiessA.OlssonJ.et al. (2011). Ecological stoichiometry of Eurasian perch – intraspecific variation due to size, habitat and diet. Oikos120, 886–896. 10.1111/j.1600-0706.2010.18939.x
113
WilkinsonD. M. (1999). The disturbing history of intermediate disturbance. Oikos84, 145–147. 10.2307/3546874
114
WinderM.SpaakP.MooijW. M. (2004). Trade-off in Daphnia habitat selection. Ecology85, 2027–2036. 10.1890/03-3108
115
WirtzK. (2013). Mechanistic origins of variability in phytoplankton dynamics: Part I: niche formation revealed by a size-based model. Mar. Biol.160, 2319–2335. 10.1007/s00227-012-2163-7
116
WoodsH. A.WilsonJ. K. (2013). An information hypothesis for the evolution of homeostasis. Trends Ecol. Evol.28, 283–289. 10.1016/j.tree.2012.10.021
117
YuQ.WilcoxK.PierreK. L.KnappA. K.HanX.SmithM. D. (2015). Stoichiometric homeostasis predicts plant species dominance, temporal stability, and responses to global change. Ecology96, 2328–2335. 10.1890/14-1897.1
118
ZemunikG.TurnerB. L.LambersH.LalibertéE. (2015). Diversity of plant nutrient-acquisition strategies increases during long-term ecosystem development. Nature Plants1:15050. 10.1038/nplants.2015.50
Summary
Keywords
food web, biological stoichiometry, functional trait, fitness, trade-off, resource
Citation
Meunier CL, Boersma M, El-Sabaawi R, Halvorson HM, Herstoff EM, Van de Waal DB, Vogt RJ and Litchman E (2017) From Elements to Function: Toward Unifying Ecological Stoichiometry and Trait-Based Ecology. Front. Environ. Sci. 5:18. doi: 10.3389/fenvs.2017.00018
Received
26 January 2017
Accepted
19 April 2017
Published
08 May 2017
Volume
5 - 2017
Edited by
Michael M. Douglas, University of Western Australia, Australia
Reviewed by
Wyatt F. Cross, Montana State University, USA; Joshua Hamilton, University of Wisconsin-Madison, USA
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© 2017 Meunier, Boersma, El-Sabaawi, Halvorson, Herstoff, Van de Waal, Vogt and Litchman.
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*Correspondence: Cédric L. Meunier cedric.meunier@awi.de
This article was submitted to Freshwater Science, a section of the journal Frontiers in Environmental Science
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