Premium
Nitrogen and phosphorus availabilities interact to modulate leaf trait scaling relationships across six plant functional types in a controlled‐environment study
Author(s) -
Crous Kristine Y.,
O'Sullivan Odhran S.,
ZaragozaCastells Joana,
Bloomfield Keith J.,
Negrini A. Clarissa A.,
Meir Patrick,
Turnbull Matthew H.,
Griffin Kevin L.,
Atkin Owen K.
Publication year - 2017
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/nph.14591
Subject(s) - trait , nutrient , herbaceous plant , biology , phosphorus , photosynthesis , respiration , nitrogen , ecology , botany , darkness , chemistry , organic chemistry , computer science , programming language
Summary Nitrogen (N) and phosphorus (P) have key roles in leaf metabolism, resulting in a strong coupling of chemical composition traits to metabolic rates in field‐based studies. However, in such studies, it is difficult to disentangle the effects of nutrient supply per se on trait–trait relationships. Our study assessed how high and low N (5 mM and 0.4 mM, respectively) and P (1 mM and 2 μM, respectively) supply in 37 species from six plant functional types (PTFs) affected photosynthesis ( A ) and respiration ( R ) (in darkness and light) in a controlled environment. Low P supply increased scaling exponents (slopes) of area‐based log–log A –N or R –N relationships when N supply was not limiting, whereas there was no P effect under low N supply. By contrast, scaling exponents of A –P and R –P relationships were altered by P and N supply. Neither R : A nor light inhibition of leaf R was affected by nutrient supply. Light inhibition was 26% across nutrient treatments; herbaceous species exhibited a lower degree of light inhibition than woody species. Because N and P supply modulates leaf trait−trait relationships, the next generation of terrestrial biosphere models may need to consider how limitations in N and P availability affect trait−trait relationships when predicting carbon exchange.