Fish zeta diversity responses to human pressures and cumulative effects across a freshwater basin
Bibliographic record
Abstract
Abstract Aim Declining biodiversity across ecosystems and myriad human pressures necessitate high‐level regional assessments for effective management. Evaluation of biodiversity patterns and stressor accumulation through beta diversity and cumulative effect analyses are two key methods for management prioritization. This study links these concepts to develop a novel cumulative effect metric based on beta diversity responses. Location Fraser River basin, British Columbia, Canada. Methods Multi‐Site Generalized Dissimilarity Models were used to evaluate nonlinear relationships between fish species compositional differences (ζn, number of shared species across any number of watersheds compared) and human pressure, environmental, and geospatial differences among all watersheds and within low, mid‐, and high elevation clusters. A cumulative effect metric was calculated as the sum of response values generated by the model for each human pressure variable specific to each watershed, when evaluated for ζ2 (equivalent to pairwise beta diversity). This metric was tested against the local contribution of each watershed to beta diversity to determine whether watersheds with unique communities had low cumulative effects and are, therefore, candidates for conservation and conversely, whether watersheds with non‐distinctive communities had high cumulative effects and warrant restoration. Species contributions to beta diversity were also assessed across the basin. Results Zeta diversity across low elevation watersheds indicated stronger filtering by human pressures than mid‐ and high elevation watersheds, which showed more stochastic community assembly. The relative importance and response to human pressures varied based on the diversity component (i.e., total diversity including compositional nestedness vs. turnover) and order of zeta (number of watersheds compared). Cumulative effects were negatively related to community uniqueness, supporting the use of these metrics for developing management priorities. Main Conclusions This assessment contributes to biodiversity conservation efforts by identifying important watersheds, species, and human pressures to manage as well as providing a cumulative effect metric directly based on biodiversity responses.
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 | 0.000 | 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".