Back

Leaf litter decomposition, microbial respiration and community succession under variable dissolved organic carbon availability in boreal streams

Jimel, M.; Kuglerova, L.; Capo, E.; Sarneel, J. M.; Jonsson, M.

2026-09-28 ecology
10.64898/2026.09.25.754356 bioRxiv
Show abstract

Boreal forested streams receive substantial inputs of terrestrial carbon (C) from riparian vegetation in the form of leaf litter, which is processed by litter-associated microorganisms. In addition to these leaf-litter C inputs, boreal streams also receive significant concentrations of dissolved organic carbon (DOC), which may represent an alternative major resource pool for litter-associated microorganisms. As leaf litter quality decreases over the process of decomposition, DOC may increasingly become a more suitable C source for the microbial communities. We investigated how DOC availability, litter type, and microbial succession influenced respiration and decomposition. Alder, birch, and spruce litter were incubated for 65 days in 12 outdoor experimental channels supplied with natural boreal stream water. Higher time-adjusted DOC concentrations were associated with higher respiration and lower relative mass remaining, whereas DOC flux received less support. Respiration and mass loss followed litter-specific trajectories, with birch showing a respiration peak that was not matched by proportional mass loss. Prokaryotic and eukaryotic communities differed among litter types and changed through time but explained process variation only for specific litter types and responses. Our results suggest that DOC can sustain litter-associated microbial activity and partly decouple respiration from litter decomposition, thereby affecting boreal stream C processing.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.