Habitat heterogeneity in eelgrass fish assemblage diversity and turnover
Bibliographic record
Abstract
ABSTRACT Maintaining habitat diversity and heterogeneity are key ecological elements of marine spatial planning. It is often assumed that patches of the same habitat harbour similar biological diversity. However, if habitat heterogeneity is high then the efficacy of habitats as surrogates of species diversity is weakened. Beta diversity variation in fish assemblages in eelgrass meadows along the Pacific coast of Canada was analysed using permutational multivariate analysis of variance and tests for dispersion of homogeneity. Variations in species composition were examined at an inter‐regional scale (100 s of km apart) and an intra‐regional scale (10s of km apart) over 7 years. Further, similarity percentage analysis and biological‐environmental modelling were used to identify factors that differentiated among fish assemblages. Beta diversity turnover was also considered by examining for the decay in fish assemblage similarity across gradients in sea surface temperature, salinity, and physical distance between pairs‐of‐meadows using linear regression. Patches of eelgrass meadows exhibited high fish assemblage dissimilarity at both the intra‐regional and inter‐regional scales; spatial factors accounted for substantially more variation in fish composition than temporal factors. A large number of fish species (20–30) and different suites of environmental factors accounted for the observed high beta diversity variation. Fish composition similarity did not decay consistently within each region with physical distance between meadows or with a change of 1°C in temperature, but Jaccard similarity did decay significantly within each region by 2–4% per part per thousand change in salinity. It is recommended that marine protected area planners consider the influence of freshwater flow into the coastal ocean and its subsequent impact on environmental gradients, which drives fish assemblage heterogeneity among eelgrass habitat patches. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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 | 0.000 | 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".