Potential drivers of changing ecological conditions in English and Welsh rivers since 1990
Bibliographic record
Abstract
River invertebrate communities across Europe have been changing in response to variations in water quality over recent decades, but the underlying drivers are difficult to identify because of the complex stressors and environmental heterogeneity involved. Here, using data from ∼4000 locations across England and Wales, collected over 29 years, we use three approaches to help resolve the drivers of spatiotemporal variation in the face of this complexity: i) mapping changes in invertebrate richness and community composition; ii) structural equation modelling (SEM) to distinguish land cover, water quality and climatic influences; and iii) geographically weighted regression (GWR) to identify how the apparent relationships between invertebrate communities and abiotic variables change across the area. Mapping confirmed widespread increases in richness and the proportion of pollution-sensitive taxa across much of England and Wales. It also revealed regions where pollution-sensitive taxa or overall richness declined, the former primarily in the uplands. SEMs confirmed strong increases in average biochemical oxygen demand and nutrient concentrations related to urban and agricultural land cover, but only a minority of land cover's effect upon invertebrate communities was explained by average water chemistry, highlighting potential factors such as episodic extremes or emerging contaminants. GWR identified strong geographical variation in estimated relationships between macroinvertebrate communities and environmental variables, with evidence that the estimated negative impacts of nutrients and water temperature were increasing through time. Overall the results are consistent with widespread biological recovery of Britain's rivers from past gross organic pollution, whilst highlighting declines in some of the most diverse and least impacted streams. Modelling points to a complex and changing set of drivers, highlighting the multifaceted impacts of catchment land cover and the evolving role of different stressors, with the relationship to gross organic pollution weakening, whilst estimated nutrient and warming effects strengthened.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".