Soil organic matter quality influences mineralization and GHG emissions in cryosols: a field-based study of sub- to high Arctic

Abstract

Arctic soils store large amounts of labile soil organic matter (SOM) and several studies have suggested that SOM characteristics may explain variations in SOM cycling rates across Arctic landscapes and Arctic ecosystems. The objective of this study was to investigate the influence of routinely measured soil properties and SOM characteristics on soil gross N mineralization and soil GHG emissions at the landscape scale. This study was carried out in three Canadian Arctic ecosystems: Sub-Arctic (Churchill, MB), Low-Arctic (Daring Lake, NWT), and High-Arctic (Truelove Lowlands, NU). The landscapes were divided into five landform units: (1) upper slope, (2) back slope, (3) lower slope, (4) hummock, and (5) interhummock, which represented a great diversity of Static and Turbic Cryosolic soils including Brunisolic, Gleysolic, and Organic subgroups. Soil gross N mineralization was measured using the (15) N dilution technique, whereas soil GHG emissions (N2 O, CH4 , and CO2 ) were measured using a multicomponent Fourier transform infrared gas analyzer. Soil organic matter characteristics were determined by (1) water-extractable organic matter, (2) density fractionation of SOM, and (3) solid-state CPMAS (13) C nuclear magnetic resonance (NMR) spectroscopy. Results showed that gross N mineralization, N2 O, and CO2 emissions were affected by SOM quantity and SOM characteristics. Soil moisture, soil organic carbon (SOC), light fraction (LF) of SOM, and O-Alkyl-C to Aromatic-C ratio positively influenced gross N mineralization, N2 O and CO2 emissions, whereas the relative proportion of Aromatic-C negatively influenced those N and C cycling processes. Relationships between SOM characteristics and CH4 emissions were not significant throughout all Arctic ecosystems. Furthermore, results showed that lower slope and interhummock areas store relatively more labile C than upper and back slope locations. These results are particularly important because they can be used to produce better models that evaluate SOM stocks and dynamics under several climate scenarios and across Arctic landscapes and ecosystems.