Integrating stream bioassessment and landscape ecology as a tool for land use planning
Bibliographic record
Abstract
Summary 1. Bioassessment has evolved significantly from a method of deciding whether an ecosystem exposed to stressors should ‘pass’ or ‘fail’ (or how badly it fails). Society wants some notion of what has caused any observed degradation of ecosystems, and what management strategies might improve degraded ecosystems. Managers also want to predict what negative or positive effects different land use strategies will have on the component ecosystems of a landscape, including lakes and streams. 2. Here we illustrate an approach to providing these tools to managers with data from a bioassessment study of streams in the Fraser River Basin of British Columbia, Canada. 3. Landscape scale descriptors of both the natural (e.g. catchment size, surficial geology) and stressor (e.g. hard rock mines, forest harvest) environment of each site were used to define the natural and stressor environments of each of the 242 stream sites. 4. We classified 206 reference (relatively unexposed to human activity) sites using their benthic macroinvertebrate community composition, and then discriminated among the faunally defined groups with landscape scale descriptors of the natural environment of the sites. 5. This discriminant function model allowed us to predict which group each of the test sites would be in if it were in reference condition, and then measure the relationship between the amount of human activity and the biota in each of these groups. 6. These relationships were turned into projections of what will happen to a stream ecosystem's biota if the stressor environment is either improved or degraded. These projection models form the basis of evidence‐based land use planning that takes into account the health of freshwater ecosystems.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".