Extreme stresses, niches, and positive species interactions along stress gradients. Issue 6 (1st June 2014)

• Record Type: Journal Article
• Authors: He, Qiang; Bertness, Mark D.
• Is Part Of: Ecology. Volume 95:Issue 6(2014)
• Page Start: 1437
• Page End: 1443
• Publication Date: 2014-06-01
• Subjects: community ecology -- environmental gradients -- extreme environment -- facilitation -- general rule -- plant interactions -- standardized approach; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1890/13-2226.1
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract : Since proposed two decades ago, the stress‐gradient hypothesis (SGH), suggesting that species interactions shift from competition to facilitation with stress, has been widely examined. Despite broad support across species and ecosystems, ecologists debate whether the SGH applies to extreme environments, arguing that species interactions switch to competition or collapse under extreme stress. We show that facilitation often expands distributions on species borders. SGH exceptions occur when weak stress gradients or stresses outside of species' niches are examined, multiple stresses co‐occur canceling out their effects, temporally dependent effects are involved, or results are improperly analyzed. We suggest that ecologists resolve debates by standardizing key SGH terms, such as fundamental and realized niche, stress gradients vs. environmental gradients, by quantitatively defining extreme stress, and by critically evaluating the functionality of stress gradients. We also suggest that new research examine the breadth and relevance of the SGH. More rigor needs to be applied to SGH tests to identify actual exceptions rather than those due to failures to meet its underlying assumptions, so that the general principles of the SGH and its exceptions can be incorporated into ecological theory, conservation strategies, and environmental change predictions.