Leaf litter decomposition rates in freshwaters differ by ecosystem
Bibliographic record
Abstract
Terrestrial leaf litter is a large contributor to the metabolism and secondary production of freshwaters. Decomposition rates of leaf litter in freshwaters are often used as a proxy for ecosystem function; however, there are many sources of variation in decomposition rates within and between freshwater ecosystems that still have not been tested. In particular, the variation in decomposition rates between different freshwater ecosystems has rarely been addressed. Here we compared decomposition rates of red alder (Alnus rubra) leaf litter in streams, ponds and lakes within a single forest, while controlling for water temperatures. Using coarse-mesh and fine-mesh bags we found that when accounting for degree days, decomposition rates were higher in streams than ponds, and twice as high in streams than lakes, for either mesh size. The overall densities of invertebrates per leaf pack or per gram of leaf litter were very similar between the three ecosystems. However, detritivores were six-fold or more abundant in leaf packs from streams than those from lakes or ponds. There were fewer specialized macroinvertebrate consumers of leaf litter in the lentic environments. Specialized shredders such as Plecoptera were absent from lentic sites, and their absence in terms of decomposition rates was not compensated for by litter decomposition by generalist taxa. While we did not test the specific mechanisms responsible, differences may be associated with the relative temporal and spatial variation in the abundance of this resource and lack of specialist consumers in lakes and ponds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".