Abstract
In recent years, the arctic tundra has been subject to more frequent stochastic biotic or extreme weather events (causing plant dieback) and warmer summer air temperatures. However, the combined effects of these perturbations on the tundra ecosystem remain uninvestigated. We experimentally simulated plant dieback by cutting vegetation and increased summer air temperatures (ca. +2°C) by using open-top chambers (OTCs) in an arctic heath tundra, West Greenland. We quantified surface greenhouse gas fluxes, measured soil gross N transformation rates, and investigated all ecosystem compartments (plants, soils, microbial biomass) to utilize or retain nitrogen (N) upon application of stable N-15 isotope tracer. Measurements from three growing seasons showed an immediate increase in surface CH4 and N2O uptake after the plant dieback. With time, surface N2O fluxes alternated between emission and uptake, and rates in both directions were occasionally affected, which was primarily driven by soil temperatures and soil moisture conditions. Four years after plant dieback, deciduous shrubs recovered their biomass but retained significantly lower amounts of 15N, suggesting the reduced capacity of deciduous shrubs to utilize and retain N. Among four plant functional groups, summer warming only increased the biomass of deciduous shrubs and their 15N retention, while following plant dieback deciduous shrubs showed no response to warming. This suggests that deciduous shrubs may not always benefit from climate warming over other functional groups when considering plant dieback events. Soil gross N mineralization (~ -50%) and nitrification rates (~ -70%) significantly decreased under both ambient and warmed conditions, while only under warmed conditions immobilization of NO3 - significantly increased (~ +1900%). This explains that plant dieback enhanced N retention in microbial biomass and thus bulk soils under warmed conditions. This study underscores the need to consider plant dieback events alongside summer warming to better predict future ecosystem-climate feedback.
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