Fungal richness does not buffer the effects of streams salinization on litter decomposition
Bibliographic record
Abstract
Freshwater salinization is a world-wide phenomenon threatening stream communities and ecosystem functioning. In these systems, litter decomposition is a main ecosystem-level process where fungi (aquatic hyphomycetes) play a central role linking basal resource and higher levels of food-web. The current study evaluated the impact of aquatic hyphomycete richness on leaf litter decomposition when subjected to salinization. In a microcosm study, we analysed leaf mass loss, fungal biomass, respiration and sporulation rate by fungal assemblages at three levels of species richness (1, 4, 8 species) and three levels of salinity (0, 8, 16 g NaCl L‑1). Mass loss and sporulation rate were depressed at 8 and 16 g NaCl L‑1, while fungal biomass and respiration were only negatively affected at 16 g L‑1. A richness effect was only observed on sporulation rates, with the maximum values found in assemblages of 4 species. In all cases, the negative effects of high levels of salinization on the four tested variables superimposed the potential buffer capacity of fungal richness. The study suggests functional redundancy among the fungal species even at elevated salt stress conditions which may guarantee stream functioning at extreme levels of salinity. Nonetheless, it also points to the possible importance of salt induced changes on fungal diversity and identity in salinized streams able to induce bottom-up effects in the food webs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".