On the "strict homeostasis" assumption in ecological stoichiometry

Hao Wang, Robert W. Sterner, James J. Elser

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


Multiple elements including carbon (C), nitrogen (N) and phosphorus (P) are required for organismal growth, reproduction, and maintenance. Newly emerging mathematical models linking population dynamics with stoichiometric relationships among these key elements improve historic trophic interaction models and resolve some existing paradoxes. The degree to which organisms maintain a constant chemical composition in the face of variations in the chemical composition and availability of their environmental resources is referred to as "stoichiometric homeostasis" Most of these models so far have assumed constant nutrient contents in heterotrophs, called "strict homeostasis", and varied nutrient contents in autotrophs, called "non-homeostasis", due to the fact that the stoichiometric variability of heterotrophs is often much less than that of autotrophs. Our study suggests that the "strict homeostasis" assumption is reasonable when the stoichiometric variability of herbivores is less than a threshold. This threshold is independent of algal stoichiometric variability, thus the above historic reasoning for strict homeostasis in heterotrophs is not convincing. We find that the "strict homeostasis" assumption seems valid for many herbivores except for herbivores with small mortality rates. The results are nearly same in both one-nutrient and two-nutrient models, and robust to perturbation of parameter values and environmental nutrient status. Finally, the two-nutrient model shows that herbivore's survival needs higher variation in the more potentially limiting of the two elements.

Original languageEnglish
Pages (from-to)81-88
Number of pages8
JournalEcological Modelling
StatePublished - Sep 24 2012


  • Carbon
  • Hard dynamical threshold
  • Herbivore
  • Nitrogen
  • Phosphorus
  • Phytoplankton
  • Stoichiometry
  • StrictHomeostasis
  • Turnover rates


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