Aridity threshold for alpine soil nitrogen isotope signature and ecosystem nitrogen cycling

Abstract

AbstractDetermination of tipping points in nitrogen (N) isotope (δ15N) natural abundance, especially soil δ15N, with increasing aridity, is critical for estimating N‐cycling dynamics and N limitation in terrestrial ecosystems. However, whether there are linear or nonlinear responses of soil δ15N to increases in aridity and if these responses correspond well with soil N cycling remains largely unknown. In this study, we investigated soil δ15N and soil N‐cycling characteristics in both topsoil and subsoil layers along a drought gradient across a 3000‐km transect of drylands on the Qinghai–Tibetan Plateau. We found that the effect of increasing aridity on soil δ15N values shifted from negative to positive with thresholds at aridity index (AI) = 0.27 and 0.29 for the topsoil and subsoil, respectively, although soil N pools and N transformation rates linearly decreased with increasing aridity in both soil layers. Furthermore, we identified markedly different correlations between soil δ15N and soil N‐cycling traits above and below the AI thresholds (0.27 and 0.29 for topsoil and subsoil, respectively). Specifically, in wetter regions, soil δ15N positively correlated with most soil N‐cycling traits, suggesting that high soil δ15N may result from the “openness” of soil N cycling. Conversely, in drier regions, soil δ15N showed insignificant relationships with soil N‐cycling traits and correlated well with factors, such as soil‐available phosphorus and foliage δ15N, demonstrating that pathways other than typical soil N cycling may dominate soil δ15N under drier conditions. Overall, these results highlight that different ecosystem N‐cycling processes may drive soil δ15N along the aridity gradient, broadening our understanding of N cycling as indicated by soil δ15N under changing drought regimes. The aridity threshold of soil δ15N should be considered in terrestrial N‐cycling models when incorporating 15N isotope signals to predict N cycling and availability under climatic dryness.