Comparative analysis of different survey methods for monitoring fish assemblages in coastal habitats
Bibliographic record
Abstract
Coastal ecosystems are among the most productive yet increasingly threatened marine ecosystems worldwide. Particularly vegetated habitats, such as eelgrass (Zostera marina) beds, play important roles in providing key spawning, nursery and foraging habitats for a wide range of fauna. To properly assess changes in coastal ecosystems and manage these critical habitats, it is essential to develop sound monitoring programs for foundation species and associated assemblages. Several survey methods exist, thus understanding how different methods perform is important for survey selection. We compared two common methods for surveying macrofaunal assemblages: beach seine netting and underwater visual census (UVC). We also tested whether assemblages in shallow nearshore habitats commonly sampled by beach seines are similar to those of nearby eelgrass beds often sampled by UVC. Among five estuaries along the Southern Gulf of St. Lawrence, Canada, our results suggest that the two survey methods yield comparable results for species richness, diversity and evenness, yet beach seines yield significantly higher abundance and different species composition. However, sampling nearshore assemblages does not represent those in eelgrass beds despite considerable overlap and close proximity. These results have important implications for how and where macrofaunal assemblages are monitored in coastal ecosystems. Ideally, multiple survey methods and locations should be combined to complement each other in assessing the entire assemblage and full range of changes in coastal ecosystems, thereby better informing coastal zone management.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".