Industrial road crossings drive changes in community structure and instream habitat for freshwater fishes in the boreal forest
Bibliographic record
Abstract
Summary Stream crossing structures are an increasingly prevalent anthropogenic feature on North American riverscapes, particularly in watersheds affected by industrial resource development in sensitive boreal environments. If improperly managed, stream crossings have the potential to alter fish habitat and impede fish movement. This study assessed instream habitat characteristics and fish communities from 33 culverted, bridged and reference streams in an industrialising region of the boreal forest in west‐central Alberta. Mixed‐effects modelling and multivariate analysis were used to determine impacts of stream crossings at three scales: whole‐stream scale, within‐stream scale and the interaction of scales. Instream habitat characteristics such as mean depth, water velocity, percent fines, turbidity, water temperature and dissolved oxygen showed significant between‐stream as well as within‐stream differences among stream crossings. The majority of fish species exhibited significantly lower densities (n m−2) in upstream habitats as compared to downstream habitats, including a significant reduction in Slimy Sculpin densities in culverted streams. Multivariate tests corroborated these results, showing that fish assemblages differ as a function of stream type. This study suggests industrial stream crossings influence abiotic habitat characteristics in freshwater ecosystems, restrict biotic connectivity and impact fish community structure at the whole‐stream and within‐stream scales. Alterations to stream ecosystems associated with stream crossings may be driving large‐scale changes in stream fish communities in the boreal forest. With expanded development expected in much of North America's boreal region, mitigation measures which limit impacts from stream crossings are needed to ensure proper ecosystem function in freshwater systems.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".