Considering multiple anthropogenic threats in the context of natural variability: Ecological processes in a regulated riverine ecosystem
Bibliographic record
Abstract
Rivers are among the most altered environments globally, but identifying which threats are responsible for observed biotic and abiotic changes is complicated by natural drivers of variation. The Bow River, Canada provides an ideal model to resolve these influences and explore spatial relationships. It originates from pristine Rocky Mountain headwaters and is subsequently impacted by typical human alterations: damming, municipal channelization and effluent release, and agricultural impacts (nutrient enrichment and water withdrawal for irrigation). By coordinating studies of the Bow River's biota, we demonstrate how threat–driver interactions depend on season and the abiotic factor and biotic community or species of interest. We conclude that impact severity and riverine recovery depend on the threat magnitude, its longitudinal position and proximity to other threats and natural drivers. We found that river regulation, water extraction and bank armouring interact to limit geomorphic processes resulting in depleted riparian woodlands and numbers of fish species, though a large, undammed tributary nearby allows quick recovery downstream. We highlight the implications of the longitudinal position of the threats because cold-water fish species are disproportionately impacted through the area where the human impacts on the Bow River overlap. We illustrate how the interactions between flow, nutrients and temperature lead to macrophyte- or algae-dominated communities and associated shifts in fish composition and biomass. Finally, we applied our increased understanding of ecological riverine processes to conclude that management techniques such as flushing flows or functional environmental flows are likely to have only minimal or conditional success in the Bow River.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".