If you build it, will they come? Macroinvertebrate community recovery patterns in ‘successful’ agricultural restoration efforts
Bibliographic record
Abstract
Stream communities are shaped by regional-scale processes, yet the influence of regional conditions on local restoration outcomes remains underexplored. Biophysical stream restoration is a common response to environmental legislation but often reveals a disconnect between scientific understanding and policy objectives. In agricultural watersheds, physical habitat restoration ideally complements other best management practices (BMPs), including chemical and biological interventions. However, these landscapes are often heavily modified, with extensive bank erosion and riparian zone loss. In this study, physical habitat restoration was prioritised due to the absence of water quality guideline exceedances and in anticipation of ongoing BMP implementation. It had two main objectives: (1) to assess long-term trends in regional water quality and benthic macroinvertebrate communities across 14 sites in seven streams spanning a gradient of agricultural disturbance, evaluating the ecological integrity surrounding a previously degraded, restored reach; and (2) to use this regional dataset to evaluate restoration outcomes in Ridge Brook - a heavily impacted watershed restored in 2007 to support endangered and culturally significant fish habitat. Findings highlight the limitations of restoration in the absence of broader regional contexts, offering insights to guide future restoration strategies and policy alignment.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".