Epifaunal diversity patterns within and among seagrass meadows suggest landscape‐scale biodiversity processes
Bibliographic record
Abstract
Abstract Coastal seascapes can support high animal diversity and secondary productivity that attracts conservation interest and provides ecosystem services. Though the importance of spatial structure in marine habitats is well known, determining the dominant spatial scale for biodiversity patterns is an often‐overlooked dimension of the ecological and conservation value of seagrass meadows. We estimated biodiversity patterns at fine (0.28 m 2 ), meadow and seascape scales to explore whether seagrass‐associated biodiversity patterns are consistent with spatial processes such as abiotic habitat filtering or metacommunity dynamics in a northeast Pacific seascape. In Barkley Sound, British Columbia, we quantified epifaunal biodiversity on eelgrass ( Zostera marina ) to test three hypotheses: Taxonomic diversity and composition (1) vary randomly within meadows but (2) vary systematically among meadows reflecting meadow location or environmental conditions, and (3) spatial patterns are stable over time. We sampled epifaunal invertebrates in a systematic spatial grid within nine eelgrass meadows. We found that epifaunal community composition varied as much over a few meters within the same meadow as among meadows separated by kilometers and of different sizes and wave exposures. In each meadow, we observed less than three‐quarters of the species in the regional species pool, and we observed non‐random spatial aggregation within many species. Even with spatial turnover, assemblages were more similar than predicted by null models based on random species distributions, suggesting that some species tend to co‐occur in high abundance. These spatial biodiversity patterns were not clearly explained by meadow location, area, or abiotic conditions, except that there were differences in clusters of meadows distinguished by their salinity (more marine vs. more fresh). Our results indicate that effective conservation and understanding of how seagrass can support high biodiversity and ecosystem function may require consideration of spatial connections among meadows, and not just the condition of the meadows themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".