Development, Evaluation, and Implementation of a Standardized Fish Community-Based Index of Biotic Integrity for Evaluating the Ecological Health of Boreal Plains Streams and Rivers in Saskatchewan, Canada
Bibliographic record
Abstract
Freshwater ecosystems face increasing threats from anthropogenic influences and multiple stressors, necessitating effective management techniques to assess, conserve, and restore aquatic health. Fish-based Index of Biotic Integrity (IBI) tools play a crucial role in assessing and monitoring the health of freshwater ecosystems. Despite a prosperous, significant fishery and ample aquatic habitats, Saskatchewan (SK), and much of Canada’s boreal region, currently lack a fish-based IBI framework, and the development and evaluation of such a tool could complement existing monitoring programs and provide a novel approach to fisheries and aquatic resource management within SK, and more broadly, northern Canada. This study developed and evaluated a fish-based IBI framework for streams and rivers of the Beaver River watershed in the Boreal Plain ecozone of SK. This watershed exhibits a gradient of human disturbance, ranging from agriculture in the south to relatively unimpacted forest landscapes in the north, making it an ideal location to study the potential effects of human stressors on fish and aquatic ecosystems and evaluate the IBI in a relatively homogenous area with multiple land-use stressors. By assessing various measures of land use and fish habitat, I classified minimally disturbed (or low-stress) conditions, established a gradient of stream and river health throughout the Beaver River watershed at 18 sites, and then determined fish community response to known stressors. A potential limitation of fish-biomonitoring studies is the effect of seasonality and timing of sampling on the interpretation of results, especially in northern regions where temperature extremes likely influence fish reproduction and mobility. Therefore, I revisited five of the sites annually over a three-year period to test the sensitivity of the IBI to interannual variability. I identified nine metrics, selected across the major metric categories, that showed the highest responsiveness to human disturbance. As expected, IBI scores decreased with increasing stress, but a depauperate and tolerant fish community, confounded by high interannual variability in environmental conditions and the fish community, created difficulties in developing the IBI and limited my ability to attribute variations to natural trends through time or anthropogenic influence. My results reinforce the importance of long-term monitoring to decipher trends in natural variation of fish communities from variation created by anthropological stressors and can inform fisheries and aquatic ecosystem health management and decision making in SK as well as other Boreal Plains’ watersheds throughout Canada.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".