Assessing the relationship between community dispersion and disturbance in a soft‐sediment ecosystem
Bibliographic record
Abstract
Abstract Disturbed ecosystems often exhibit increased community heterogeneity when compared to nondisturbed systems. One way to measure community heterogeneity is statistical dispersion, a measure of how variable individual samples are from the multivariate average of the community condition (species presence/absence and density). In more specific manner, dispersion measures the distance between an individual data point and the centroid, the multivariate average of all data points. Statistical dispersion may be an important parameter to include in environmental assessments, or in studies that attempt to understand the role of disturbances in structuring biological systems. However, disturbances have been observed to increase, decrease, or not impact community dispersion (or community heterogeneity). Therefore, the usefulness of dispersion in studying or identifying disturbances is unclear. We tested if a mechanical disturbance increased community dispersion using the infaunal community of the intertidal mudflats along the north coast of British Columbia, Canada. We observed no statistically significant increase in community dispersion with varying frequency and intensity of a mechanical disturbance. This is likely a result of disturbed and nondisturbed treatments being dominated by the same six taxa, thus minimizing dispersion. Therefore, in ecosystems where differences in community successional stages are subtle (a result of changes in relative abundance rather than species replacement), community dispersion may not be an informative parameter when investigating disturbance. Despite this, we suggest that dispersion can be a useful variable to include in studies attempting to understand or identify disturbances; however, dispersion should only be one parameter amongst many used to understand or identify disturbances.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".