Diversity has stronger top‐down than bottom‐up effects on decomposition
Bibliographic record
Abstract
The flow of energy and nutrients between trophic levels is affected by both the trophic structure of food webs and the diversity of species within trophic levels. However, the combined effects of trophic structure and diversity on trophic transfer remain largely unknown. Here we ask whether changes in consumer diversity have the same effect as changes in resource diversity on rates of resource consumption. We address this question by focusing on consumer-resource dynamics for the ecologically important process of decomposition. This study compares the top-down effect of consumer (detritivore) diversity on the consumption of dead organic matter (decomposition) with the bottom-up effect of resource (detrital) diversity, based on a compilation of 90 observations reported in 28 studies. We did not detect effects of either detrital or consumer diversity on measures of detrital standing stock, and effects on consumer standing stock were equivocal. However, our meta-analysis indicates that reductions in detritivore diversity result in significant reductions in the rate of decomposition. Detrital diversity has both positive and negative effects on decomposition, with no overall trend. This difference between top-down and bottom-up effects of diversity is robust to different effect size metrics and could not be explained by differences in experimental systems or designs between detritivore and detrital manipulations. Our finding that resource diversity has no net effect on consumption in "brown" (detritus-consumer) food webs contrasts with previous findings from "green" (plant-herbivore) food webs and suggests that effects of plant diversity on consumption may fundamentally change after plant death.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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".