Evaluating a benthic index of biotic integrity (B-IBI) to measure ecological integrity in Pacific Rim National Park Reserve of Canada
Bibliographic record
Abstract
The ease of application and effectiveness of a multimetric benthic index of biotic integrity (B-IBI) was evaluated for use as an ecological integrity monitoring tool in coastal National Parks of Canada.The B-IBI was developed in three phases: 1) analysis of land use impacts to select study watersheds along a gradient of disturbance; 2) collection and identification of benthic macroinvertebrates from study streams; 3) multimetric and multivariate analysis of benthic invertebrate data to evaluate the performance of the B-IBI approach for monitoring the ecological integrity of streams in Pacific Rim National Park Reserve of Canada, on the west coast of Vancouver Island.Land use impacts were analysed from Landsat-7 ETM+ satellite images.Potentially appropriate metrics were chosen through a literature review and evaluated by comparing metric scores with watershed impact scores for each study watershed.The ten best metrics for the study region were combined into a benthic index of biotic integrity for Pacific Rim NPR, and each study stream was given a biotic integrity score derived with this index.Additional analysis of the macroinvertebrate assemblages was conducted using multivariate ordination, in order to compare multimetric and multivariate approaches.The multimetric B-IBI approach produced relatively high overall biotic integrity index scores for most study creeks, but the scores did not correlate well with impact scores determined from satellite image analysis.Multivariate analysis of the macroinvertebrate data separated sites according to environmental variables, but did not separate sites based on impact scores from satellite image analysis.This may be attributable to a lack of impacts in the macroinvertebrate communities, but it is argued in this report that the results are better explained as a failure of the B-IBI method to detect impacts.The B-IBI approach could be improved by increasing sampling effort enough to determine reference conditions for macroinvertebrate assemblages in Pacific Rim NPR.Future studies will need to include streams further outside the boundaries of the Park, to sufficiently increase the number of samples for reliable measurement of the ecological integrity of Park watersheds.I enjoyed the assistance of many people, at all stages of this project.I thank Dr.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".