Resistance to drying: The role of seedbanks and hyporheic refuges in invertebrate communities
Bibliographic record
Abstract
Abstract Aquatic refuges are essential for invertebrate communities to cope with flow intermittence in intermittent rivers and ephemeral streams (IRES), but their contribution to recovery after drying remains unclear, although they may be essential to safeguard IRES biodiversity. Here, we explored the role of hyporheic zones (HZs) and dry sediments (seedbanks) as aquatic refuges in six Mediterranean intermittent streams during drying and their contribution to community recovery at the local scale. We analysed the taxonomic and functional composition and diversity in the refuges and benthos under connected flow conditions by combining field and laboratory conditions. We explored the relationship between these metrics and the drying duration, expecting a reduction in diversity and an increase in resistance trait abundances in the refuges. The seedbank and HZ contributed 16 and 40% of the benthic taxa, respectively, and up to 60% of the functional richness found in the benthos. Conversely, we only found a negative relationship of drying duration with the relative abundance of resilience traits, not with resistance traits as initially predicted. The expected increase in drought severity due to climate change will compromise the water needed to maintain the HZ and the moisture in streambed sediments. Therefore, the protection of the integrity of biodiversity recovery mechanisms in IRES is a priority.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".