Bibliographic record
Abstract
Summary 1. Faced with widespread degradation of riverine ecosystems, stream restoration has greatly increased. Such restoration is rarely planned and executed with inputs from ecological theory. In this paper, we seek to identify principles from ecological theory that have been, or could be, used to guide stream restoration. 2. In attempts to re‐establish populations, knowledge of the species’ life history, habitat template and spatio‐temporal scope is critical. In many cases dispersal will be a critical process in maintaining viable populations at the landscape scale, and special attention should be given to the unique geometry of stream systems 3. One way by which organisms survive natural disturbances is by the use of refugia, many forms of which may have been lost with degradation. Restoring refugia may therefore be critical to survival of target populations, particularly in facilitating resilience to ongoing anthropogenic disturbance regimes. 4. Restoring connectivity, especially longitudinal connectivity, has been a major restoration goal. In restoring lateral connectivity there has been an increasing awareness of the riparian zone as a critical transition zone between streams and their catchments. 5. Increased knowledge of food web structure – bottom‐up versus top‐down control, trophic cascades and subsidies – are yet to be applied to stream restoration efforts. 6. In restoration, species are drawn from the regional species pool. Having overcome dispersal and environmental constraints (filters), species persistence may be governed by local internal dynamics, which are referred to as assembly rules. 7. While restoration projects often define goals and endpoints, the succession pathways and mechanisms (e.g. facilitation) by which these may be achieved are rarely considered. This occurs in spite of a large of body of general theory on which to draw. 8. Stream restoration has neglected ecosystem processes. The concept that increasing biodiversity increases ecosystem functioning is very relevant to stream restoration. Whether biodiversity affects ecosystem processes, such as decomposition, in streams is equivocal. 9. Considering the spatial scale of restoration projects is critical to success. Success is more likely with large‐scale projects, but they will often be infeasible in terms of the available resources and conflicts of interest. Small‐scale restoration may remedy specific problems. In general, restoration should occur at the appropriate spatial scale such that restoration is not reversed by the prevailing disturbance regime. 10. The effectiveness and predictability of stream ecosystem restoration will improve with an increased understanding of the processes by which ecosystems develop and are maintained. Ideas from general ecological theory can clearly be better incorporated into stream restoration projects. This will provide a twofold benefit in providing an opportunity both to improve restoration outcomes and to test ecological theory.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".