Are tissue concentrations of Hylocomium splendens a good predictor of nitrogen deposition?

Abstract

Emissions from fossil fuel combustion and intensive agriculture have led to a significant increase in global atmospheric concentrations of reactive nitrogen (Nr). Traditional monitoring networks do not capture the high spatial variability of Nr emissions sources; as such, pleurocarpous moss species have been used as a biomonitor for Nr deposition. Tissue concentrations of nitrogen (%N) in the moss species Hylocomium splendens at more than 100 locations in Ireland were used in conjunction with environmental variables (e.g., mean temperature (°C), sun hours, rain (mm), distance to coast) to determine predictive models of total nitrogen (N) deposition (kg N ha −1 yr −1 ) and atmospheric ammonia (NH 3 ) concentrations (μg m −3 ). Tissue N concentrations ranged from 0.5 to 1.2%, with an average of 0.8%; concentrations showed a (low−high) gradient from the west (Atlantic) coast to the east coast. Univariate regression analysis showed that %N tissue concentrations and environmental variables had similar spatial trends and associations. Tissue N concentrations had a stronger relationship with total N deposition compared with atmospheric NH 3 (R 2 = 0.2, p < 0.001; R 2 = 0.1, p < 0.001, respectively). The best predictive model (based on backwards stepwise multiple regression, with model selection based on corrected Akaike Information Criterion) for total N deposition included tissue N concentrations; however, the model for atmospheric NH 3 only included environmental variables (Total N deposition: R 2 = 0.61, p < 0.001; atmospheric NH 3 : R 2 = 0.73, p < 0.001). Sampling strategies involving pleurocarpous moss species have the potential to inform traditional monitoring networks towards improved spatial mapping of N deposition. • Hylocomium splendens was evaluated as a biomonitor of reactive nitrogen deposition. • Tissue N concentrations showed a significant positive relationship with total N deposition. • Moss biomonitoring could improve the spatial analysis of N deposition from traditional networks.