Open access publications in the SLU publication database

Abstract

The impacts of global environmental change on productivity in northern latitudes will be contingent on nitrogen (N) availability. In circumpolar boreal ecosystems, nonvascular plants (i.e., bryophytes) and associated N-2-fixing diazotrophs provide one of the largest known N inputs but are rarely accounted for in Earth system models. Instead, most models link N-2-fixation with the functioning of vascular plants. Neglecting nonvascular N-2-fixation may be contributing toward high uncertainty that currently hinders model predictions in northern latitudes, where nonvascular N-2-fixing plants are more common. Adequately accounting for nonvascular N-2-fixation and its drivers could subsequently improve predictions of future N availability and ultimately, productivity, in northern latitudes. Here, we review empirical evidence of boreal nonvascular N-2-fixation responses to global change factors (elevated CO2, N deposition, warming, precipitation, and shading by vascular plants), and compare empirical findings with model predictions of N-2-fixation using nine Earth system models. The majority of empirical studies found positive effects of CO2, warming, precipitation, or light on nonvascular N-2-fixation, but N deposition strongly downregulated N-2-fixation in most empirical studies. Furthermore, we found that the responses of N-2-fixation to elevated CO2 were generally consistent between models and very limited empirical data. In contrast, empirical-model comparisons suggest that all models we assessed, and particularly those that scale N-2-fixation with net primary productivity or evapotranspiration, may be overestimating N-2-fixation under increasing N deposition. Overestimations could generate erroneous predictions of future N stocks in boreal ecosystems unless models adequately account for the drivers of nonvascular N-2-fixation. Based on our comparisons, we recommend that models explicitly treat nonvascular N-2-fixation and that field studies include more targeted measurements to improve model structures and parameterization.