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Stoichiometry of hydrological C, N, and P losses across climate and geology: An environmental matrix approach across New Zealand primary forests
Author(s) -
McGroddy M. E.,
Baisden W. T.,
Hedin L. O.
Publication year - 2008
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/2007gb003005
Subject(s) - biogeochemical cycle , environmental science , dissolved organic carbon , nutrient , biogeochemistry , ecosystem , hydrology (agriculture) , temperate climate , ecology , environmental chemistry , chemistry , geology , biology , geotechnical engineering
Hydrologic losses can play a key role in regulating ecosystem nutrient balances, particularly in regions where baseline nutrient cycles are not augmented by industrial deposition. We used first‐order streams to integrate hydrologic losses at the watershed scale across unpolluted old‐growth forests in New Zealand. We employed a matrix approach to resolve how stream water concentrations of dissolved organic carbon (DOC), organic and inorganic nitrogen (DON and DIN), and organic and inorganic phosphorus (DOP and DIP) varied as a function of landscape differences in climate and geology. We found stream water total dissolved nitrogen (TDN) to be dominated by organic forms (medians for DON, 81.3%, nitrate‐N, 12.6%, and ammonium‐N, 3.9%). The median stream water DOC:TDN:TDP molar ratio of 1050:21:1 favored C slightly over N and P when compared to typical temperate forest foliage ratios. Using the full set of variables in a multiple regression approach explained approximately half of the variability in DON, DOC, and TDP concentrations. Building on this approach we combined a simplified set of variables with a simple water balance model in a regression designed to predict DON export at larger spatial scales. Incorporating the effects of climate and geologic variables on nutrient exports will greatly aid the development of integrated Earth‐climate biogeochemical models which are able to take into account multiple element dynamics and complex natural landscapes.

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