Leaf litter traits drive community structure and functioning in a natural aquatic microcosm
Bibliographic record
Abstract
Abstract Leaf litter fuels secondary production in many aquatic ecosystems. Although the identity and species richness of leaf litter have been shown to influence ecosystem functioning and food‐web composition, it has been challenging to relate such patterns to mechanisms based on litter traits. Here, we investigate how six different leaf litter species, and their mixture, affect litter decomposition, as well as the colonisation and survival of associated aquatic invertebrates in natural microecosystems (tank bromeliads). We then ask whether these effects of litter composition are explained by chemical and structural traits of the litter. Litter composition affected decomposition rates, assembly of aquatic macroinvertebrates in bromeliads and survival of some detritivores (e.g. Chironomidae). In general, most of this effect of litter composition was due to differences between litter species, not between single‐species and six‐species mixtures, and could be explained in terms of two dominant axes in litter traits. Decomposition was fastest in litters with high specific leaf area ( SLA ), N:P ratios and N and P contents, and slowest in litters with high lignin content and C:N ratios. Chironomid survival was also greatest on high N, N:P and SLA litters. Our results highlight the importance of considering leaf litter traits on the structure and functioning of freshwater ecosystems in future studies. More broadly, these results add to a growing consensus that functional traits of resource species, rather than the number of resource species, are essential to predicting resource–consumer interactions in food webs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".