Stronger effects of litter origin on the processing of conifer than broadleaf leaves: A test of home‐field advantage of stream litter breakdown
Bibliographic record
Abstract
Abstract Ecological shifts that enhance the efficiency of resource acquisition by consumers can affect the fate of resource subsidies in recipient ecosystems. To date, findings have been mixed about whether plant litter breakdown by stream decomposers is faster in locations where the litter originates from (i.e. home region) compared with other locations ( away region). This phenomenon, known as home‐field advantage ( HFA ), may be influenced by litter quality and particularly decomposer groups (shredders versus microbes), rather than being an inherent consequence of the breakdown of locally native litter. We studied the effects of HFA on litter breakdown and litter‐associated communities in streams within mid‐ to late‐successional forests. Two pairs of riparian broadleaf and one pair of conifer litter species with contrasting chemical quality were reciprocally incubated across two temperate regions (˜3,000 km apart). Species in each pair are congeneric and allopatric. The HFA was assessed using within‐pair litter comparisons in home and away regions. Coarse‐ and fine‐mesh litterbags were used to separate the contributions to litter breakdown by microbial decomposition and shredder feeding. Conifer litter species used in this study were more recalcitrant than broadleaf litter species. Consequently, there was a significant positive HFA for the microbial decomposition of conifer litter. In contrast, there was no HFA for either microbial or shredder‐mediated breakdown of broadleaf litter, which might have been overridden by catchment‐scale differences in biophysical variables affecting breakdown. The effects of HFA on shredder colonisation and feeding on broadleaf litter were more variable among streams. Numerically abundant shredder taxa tended to have higher rates of colonisation and consumption on higher‐quality litter than on low‐quality litter, irrespective of litter origin. Our results suggest that prior (evolutionary and/or contemporary) litter exposure could strongly influence the litter‐processing capacity of microbial decomposers, but not shredders, at the community level. In particular, microbial decomposers specialised in degrading conifer litter probably had lower resource‐use plasticity than those processing broadleaf litter. Inter‐stream differences in riparian vegetation and habitat conditions could influence the HFA through altering the litter exposure history of decomposer communities. Our findings highlight the importance of prior litter–microbe interactions in determining the rates of microbial decomposition of native and exotic litter species.
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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 | 0.000 |
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 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".