High environmental stress and productivity increase functional diversity along a deep‐sea hydrothermal vent gradient
Bibliographic record
Abstract
Productivity and environmental stress are major drivers of multiple biodiversity facets and faunal community structure. Little is known on their interacting effects on early community assembly processes in the deep sea (>200 m), the largest environment on Earth. However, at hydrothermal vents productivity correlates, at least partially, with environmental stress. Here, we studied the colonization of rock substrata deployed along a deep-sea hydrothermal vent gradient at four sites with and without direct influence of vent fluids at 1,700-m depth in the Lucky Strike vent field (Mid-Atlantic Ridge [MAR]). We examined in detail the composition of faunal communities (>20 μm) established after 2 yr and evaluated species and functional patterns. We expected the stressful hydrothermal activity to (1) limit functional diversity and (2) filter for traits clustering functionally similar species. However, our observations did not support our hypotheses. On the contrary, our results show that hydrothermal activity enhanced functional diversity. Moreover, despite high species diversity, environmental conditions at surrounding sites appear to filter for specific traits, thereby reducing functional richness. In fact, diversity in ecological functions may relax the effect of competition, allowing several species to coexist in high densities in the reduced space of the highly productive vent habitats under direct fluid emissions. We suggest that the high productivity at fluid-influenced sites supports higher functional diversity and traits that are more energetically expensive. The presence of exclusive species and functional entities led to a high turnover between surrounding sites. As a result, some of these sites contributed more than expected to the total species and functional β diversities. The observed faunal overlap and energy links (exported productivity) suggest that rather than operating as separate entities, habitats with and without influence of hydrothermal fluids may be considered as interconnected entities. Low functional richness and environmental filtering suggest that surrounding areas, with their very heterogeneous species and functional assemblages, may be especially vulnerable to environmental changes related to natural and anthropogenic impacts, including deep-sea mining.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".