Cyanobacteria associated with mosses represent a main nitrogen (N) source in pristine, high-latitude and -altitude ecosystems due to their ability to fix N₂. However, despite progress made regarding moss–cyanobacteria associations, the factors driving the large interspecific variation in N₂ fixation activity between moss species remain elusive. The aim of the study was to identify the traits of mosses that determine cyanobacterial colonization and thus N₂ fixation activity. Four moss species varying in N₂ fixation activity were used to assess cyanobacterial abundance and activity to correlate it with moss traits (morphological, chemical, water-balance traits) for each species. Moss hydration rate was one of the pivotal traits, explaining 56 and 38 % of the variation in N₂ fixation and cyanobacterial colonization, respectively, and was linked to morphological traits of the moss species. Higher abundance of cyanobacteria was found on shoots with smaller leaves, and with a high frequency of leaves. High phenol concentration inhibited N₂ fixation but not colonization. These traits driving interspecific variation in cyanobacterial colonization, however, are also affected by the environment, and lead to intraspecific variation. Approximately 24 % of paraphyllia, filamentous appendages on Hylocomium splendens stems, were colonized by cyanobacteria. Our findings show that interspecific variations in moss traits drive differences in cyanobacterial colonization and thus, N₂ fixation activity among moss species. The key traits identified here that control moss-associated N₂ fixation and cyanobacterial colonization could lead to improved predictions of N₂ fixation in different moss species as a function of their morphology.

Nutrient resorption from senescing tissue is a key mechanism for plants to conserve nutrients, and can affect the nutrient dynamics of ecosystems. Yet, our limited knowledge of nitrogen (N) resorption and release from mosses hampers our understanding of the role of mosses as N sources and, thereby, N cycling in moss-dominated ecosystems. The aims of this study were to estimate N resorption efficiency (NRE) of two moss species, identify the pathways of N release from the mosses and to provide a better understanding of N cycling and budgeting strategies of mosses. The dynamics of N allocation along annual moss segments of two dominant moss species (Actinothuidium hookeri and Hylocomium splendens) were assessed in old-growth fir forests using an in situ¹⁵N tracer experiment. The NRE of A. hookeri and H. splendens was 61 and 52 %, respectively. While the mosses lost 23 and 33 % N from live tissues via leaching, 15 and 14 % of N remained in senesced tissues (>3 years old) in A. hookeri and H. splendens, respectively. Both mosses resorbed the majority of their tissue N, but a considerable amount of N was lost from live segments. Our results highlight the crucial role mosses play as N sinks in ecosystems, since N retention (resorbed and sequestered in senescent tissue) outweighed N loss via leaching. However, the sink strength depends on temperature and precipitation, which will change in a future climate. The values for NRE, leaching, etc. estimated here can help improve biogeochemical models aiming to complete N budgets for moss-abundant ecosystems.

Salinity dominates seawater density and directly affects physical and biochemical processes. Having a reliable retrieval model is essential to providing frequent and accurate sea surface salinity (SSS) data for marine research. Remote-sensing techniques provide alternatives for SSS data retrieval with its advantages of wide area surveys and real-time monitoring. In the present study, inverse relationship between SSS and coloured dissolved organic matter (CDOM) concentration in the Chinese Bohai Sea was verified. Thus, four simple band ratios of the original remote-sensing reflectance (R_rs) used to retrieve the CDOM concentration were compared and tested during SSS retrieval. R_rs (531)/R_rs (551) performed best among the four given band ratios. The model employed here can be applied to derive SSS with a root mean square error (RMSE) of 0.26 practical salinity units (psu) (R² = 0.76). A calibration model was verified using a discrete dataset of the measured SSS and was tested further during mapping of SSS in the Chinese Bohai Sea during 2010–2014. The yielded spatial patterns of SSS were satisfactory and an inverse relationship between SSS and the Yellow River discharge was confirmed.