Biomonitoring under changing climate conditions: assessing seasonal variability of benthic macroinvertebrate communities and stream characteristics in two ecozones in Northern Ontario, Canada.
Bibliographic record
Abstract
The vast “Ring of Fire” region, at the interface of the Boreal Shield and Hudson Plains ecozones in the far north of Ontario, Canada, is considered to be one of the most promising mineral exploration areas in Ontario in almost a century. The region is undeveloped and remote, accessible only by air, water or winter road. Multiple stresses to the natural ecosystems are anticipated with mining development, in a time of climate change which is predicted to significantly impact hydrological and temperature regimes (intensified spring freshet, more high discharge events, warmer temperatures for longer). Preserving and protecting the aquatic ecosystems in this pristine region will require effective baseline environmental monitoring in advance of development. Quantifying seasonal variation of habitat characteristics and benthic macroinvertebrate (BMI) communities, and assessing the seasonal difference between ecozones are considered key challenges in designing effective monitoring programs. In this study, I examined seasonal variability in habitat characteristics and BMI communities by sampling 43 stream sites distributed across the two ecozones. In 2015 each site was sampled three times during the open water season: post-freshet, mid-summer and fall. BMI communities varied among sampling seasons, but less variation was observed between the post-freshet and summer sampling periods than either of these compared to the fall. Ordination analyses of BMI communities identified to the family level indicated that sites from the same sampling season were more likely to group together. Water temperature, stream velocity and canopy coverage were the key factors associated with seasonal differences in benthic macroinvertebrates. Differences in location of the sampling sites across the two ecozones did not significantly impact seasonal variability, but differences may have been constrained by the common habitat features used because of the sampling protocol. Conducting sampling during the post-freshet or summer seasons is recommended for future stream bioassessments to reduce the influence of seasonal variability and thus ensure comparability over time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".