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Combined climate factors alleviate changes in gross soil nitrogen dynamics in heathlands

Journal article
Authors Anna-Karin Björsne
Tobias Rütting
P. Ambus
Published in Biogeochemistry
Volume 120
Issue 1-3
Pages 191-201
ISSN 0168-2563
Publication year 2014
Published at Department of Earth Sciences
Pages 191-201
Language en
Links dx.doi.org/10.1007/s10533-014-9990-...
Keywords N-15 tracing, Climate change, Elevated CO2, Warming, Summer drought, Nitrogen cycle, ATMOSPHERIC CARBON-DIOXIDE, ELEVATED CO2, SUMMER DROUGHT, TERRESTRIAL, ECOSYSTEMS, TEMPERATE HEATHLAND, PROCESS RESPONSES, GRASSLAND SOIL, N, DYNAMICS, N-15, MODELS, Environmental Sciences, Geosciences, Multidisciplinary
Subject categories Earth and Related Environmental Sciences

Abstract

The ongoing climate change affects biogeochemical cycling in terrestrial ecosystems, but the magnitude and direction of this impact is yet unclear. To shed further light on the climate change impact, we investigated alterations in the soil nitrogen (N) cycling in a Danish heathland after 5 years of exposure to three climate change factors, i.e. warming, elevated CO2 (eCO(2)) and summer drought, applied both in isolation and in combination. By conducting laboratory N-15 tracing experiments we show that warming increased both gross N mineralization and nitrification rates. In contrast, gross nitrification was decreased by eCO(2), an effect that was more pronounced when eCO(2) was combined with warming and drought. Moreover, there was an interactive effect between the warming and CO2 treatment, especially for N mineralization: rates increased at warming alone but decreased at warming combined with eCO(2). In the full treatment combination, simulating the predicted climate for the year 2075, gross N transformations were only moderately affected compared to control, suggesting a minor alteration of the N cycle due to climate change. Overall, our study confirms the importance of multifactorial field experiments for a better understanding of N cycling in a changing climate, which is a prerequisite for more reliable model predictions of ecosystems responses to climate change.

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