Bidirectional connectivity in rivers and implications for watershed stability and management
Bibliographic record
Abstract
River networks are connected in both upstream and downstream directions on large spatial scales by movement of water, materials, and animals. Here I examine the implications of these linkages for the stability, productivity, and management of watersheds and their migratory fishes. I use simple simulations of watershed alteration to illustrate that degradation can erode the productivity and stability of both upstream and downstream fisheries. Through analysis of an existing global dataset on rivers, I found that larger rivers tend to be more fragmented than smaller rivers. I offer three challenges and opportunities for the future management of watersheds. First, given that human impacts can spread up and down rivers, there is a need to align the scales of impact assessments with the natural scale of river systems. Second, free-flowing rivers naturally dampen variability; thus, the conservation of connectivity, habitat, and biodiversity represents a key opportunity to sustain the processes that confer stability. Third, watersheds represent natural units of social–ecological systems; watershed governance would facilitate reciprocal feedbacks between people and ecosystems and enable more social–ecological resilience.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".