A traits-based test of the home-field advantage in mixed-species tree litter decomposition
Bibliographic record
Abstract
BACKGROUND AND AIMS: Litter often decomposes faster in its environment of origin (at 'home') than in a foreign environment ('away'), which has become known as the home-field advantage (HFA). However, many studies have highlighted the conditional nature of the HFA, suggesting that current understanding of this phenomenon is not yet sufficient to generalize across systems. METHODS: The HFA hypothesis was tested for mono-specific and mixed-species litter using a tree-based experiment that manipulated the functional identity and diversity of the host tree community. Litter types of varying quality were transplanted between several host tree communities and decomposition rates were measured using litterbags. Since the decomposer community should respond to traits of the litter input and not their taxonomic identity, a traits-based index of litter-tree similarity was developed. KEY RESULTS: Mono-specific litter exhibited HFA, but when the same litter was decomposed in mixture, this trend was not observed. Mixed-species litter decomposed on average no faster or slower than monoculture litter and exhibited both positive and negative species interactions. These non-additive interactions of decomposition rates in mixture were influenced by the degree of similarity between litter and tree traits. Both synergistic and antagonistic interactions decreased in magnitude with increasing litter-tree similarity such that mixture rates were predictable from monocultures. CONCLUSIONS: The HFA occurred more strongly for mono-specific litter than for the litter types mixed together because interactions between species may have masked this effect. However, when expressed as a function of trait similarity between litters and tree communities, the HFA was not detected.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".