Seasonal effects of edge and habitat complexity on eelgrass epifaunal assemblages
Bibliographic record
Abstract
Habitat degradation and fragmentation reduce habitat structural complexity (e.g. amount of physical features) and increase habitat edges. While many studies have focused on the effects of habitat edges or complexity on biodiversity, relatively few have disentangled them or investigated their effects over time. We investigated how proximity to the edge of eelgrassZosterasubg.Zostera marinaLinnaeus, 1753 habitat, shoot density and their interactions across seasons can influence the diversity pattern of epifaunal assemblages in meadows situated in a Mediterranean lagoon (France). We used a combination of field sampling andin situmanipulations with artificial seagrass units (ASUs) mimicking low and high shoot densities. During autumn and spring, we found that shoot density, Z. marina biomass and leaf area index (LAI) were higher inside the meadows than at the edge, while epiphyte load was the highest in spring at the edges. Epifaunal abundance and diversity were higher at the edge than inside the meadow for both natural shoots—regardless of the epiphyte load—and ASUs in spring. In autumn, epifaunal abundance varied positively with ASU density, regardless of the position in the meadow. Our results also showed that edges and habitat complexity affect the epifaunal structure differently across seasons. Therefore, we suggest that recruitment of macrofauna is the main mechanism explaining a positive edge effect during spring. This work highlights the need to consider seasonal dynamics in the assessment of habitat fragmentation and degradation.
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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 | 0.000 |
| 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".