Fish assemblage monitoring in Alberta’s Ells River: Baseline fish and habitat variability prior to major development
Bibliographic record
Abstract
The collection of sentinel fish species for Environmental Effects Monitoring (EEM) has provided ancillary fish assemblage surveys on several tributaries in the Athabasca Oil Sands Region (AOSR) of Alberta, Canada over the last decade. Using available, comparable data we investigated baseline fish assemblage variability along the Ells River, a tributary of the Athabasca River experiencing increasing proximity to natural bitumen deposits and proposed mining development as it approaches confluence with the mainstem Athabasca. Transect-based electrofishing data from four sites surveyed in September 2013, 2014 and 2018 showed significant spatiotemporal variability in assemblages, where spatial variability was greatest in 2013 and temporal variability was observed in assemblages both upstream (2013–2014) and downstream (2014–2018) of proposed development. Habitat assessments in 2018 revealed significant relationships among pH, algae cover and site slope with fish assemblages of the same year. Due to the complementary nature of assemblage surveys, data challenges (changing methodologies, sampling effort, and limited ancillary physiochemical data) have presented limitations to the multivariate approach applied in the study. Moving forward, employing consistent methods for fish collections and fine-scale habitat assessments will improve the ability to correlate assemblage variability with changes in the physical environment. Ultimately, this will aid in developing potential triggers of change that may be attributed to or confound adjacent, expanding Oil Sands activities. These findings will also inform monitoring programs on the use of fish assemblages as indicators of change, potentially providing an alternative to existing biomonitoring approaches in small streams with small fish populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".