Interspecific vs intraspecific patterns in leaf nitrogen of forest trees across nitrogen availability gradients. Issue 1 (6th June 2013)

• Record Type: Journal Article
• Authors: Dybzinski, Ray; Farrior, Caroline E.; Ollinger, Scott; Pacala, Stephen W.
• Is Part Of: New phytologist. Volume 200:Issue 1(2013)
• Page Start: 112
• Page End: 121
• Publication Date: 2013-06-06
• Subjects: Evolutionarily Stable Strategy (ESS) -- foliar nitrogen (N) -- forest diversity -- game theory -- light competition -- Perfect Plasticity Approximation (PPA) -- shade tolerance -- White Mountains New Hampshire; Botany -- Periodicals; 580
• DOI: 10.1111/nph.12353
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: Leaf nitrogen content ( δ ) coordinates with total canopy N and leaf area index (LAI) to maximize whole‐crown carbon (C) gain, but the constraints and contributions of within‐species plasticity to this phenomenon are poorly understood. Here, we introduce a game theoretic, physiologically based community model of height‐structured competition between late‐successional tree species. Species are constrained by an increasing, but saturating, relationship between photosynthesis and leaf N per unit leaf area. Higher saturating rates carry higher fixed costs. For a given whole‐crown N content, a C gain‐maximizing compromise exists between δ and LAI. With greater whole‐crown N, both δ and LAI increase within species. However, a shift in community composition caused by reduced understory light at high soil N availability (which competitively favors species with low leaf costs and consequent low optimal δ ) counteracts the within‐species response, such that community‐level δ changes little with soil N availability. These model predictions provide a new explanation for the changes in leaf N per mass observed in data from three dominant broadleaf species in temperate deciduous forests of New England. Attempts to understand large‐scale patterns in vegetation often omit competitive interactions and intraspecific plasticity, but here both are essential to an understanding of ecosystem‐level patterns.