Meiofauna affect the macrobenthic biodiversity–ecosystem functioning relationship
Bibliographic record
Abstract
The biodiversity–ecosystem functioning (B–EF) relationship has become of main interest in the last few decades, mostly because of the worldwide increase in species extinctions. However, most of these studies only consider species within single size‐class or trophic level, thereby most likely underestimating ecosystem complexity. To reach more realistic scenarios, we assessed the role of meiofauna (lower size‐class level) on the relationship between macrofaunal biodiversity and multiple benthic ecosystem properties. Experiments took place under controlled conditions using three macrofaunal species ( Alitta (formerly Nereis ) virens, Macoma balthica and Mya arenaria ). A total of eight combinations of zero to three functionally different macrofaunal species were maintained in microcosms for 34 days in either the presence or absence of a different size‐class grouping (a meiofaunal mixture). The organic matter content and bacterial abundance in sediments and the oxygen and nutrient (NH 4 + , NO x − , PO 4 3− ) fluxes across the sediment‐water interface were measured and used as proxies of ecosystem properties. Overall, macrofaunal species richness did not modify any of the measured properties; however, we observed changes associated to the presence of A. virens and M. balthica . This study also revealed a strong impact of the presence of meiofauna on ecosystem properties. They changed interactions between macrofaunal species, which led to modifications in the ecosystem properties. Thus, even if this size‐class group has been poorly considered in previous studies, this experiment suggests that future studies should consider the meiofauna with greater attention, particularly in the context of B–EF.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".