Incorporating climate change into restoration decisions: perspectives from dam removal practitioners
Bibliographic record
Abstract
Incorporating climate change into conservation and restoration decisions is increasingly important for natural resource managers and restoration practitioners to effectively address the underlying drivers of ecosystem change. Small dam removal is an example of a restoration tool that may offer multiple socioeconomic and ecological benefits in streams, including promoting climate resilience. With the pace of dam removals increasing, practitioners and researchers are well-poised to incorporate climate change into future dam removal decisions. Therefore, we surveyed dam removal practitioners across 14 states in the eastern United States to understand current practices of small dam removals, factors driving restoration decisions, and how climate change knowledge is incorporated into these decisions. We also aimed to identify barriers to and opportunities for knowledge exchange between practitioners and researchers. Of the 100 respondents, most (79%) consider climate change in their dam removal decisions to some extent. Despite this, many reported a lack of clear, relevant, and accessible data linking small dam removal to climate resilience benefits. Dam removal practitioners also indicated that they most often rely on climate change information garnered from conversations with colleagues, rather than from scientific research products. These results suggest that the co-production of relevant, salient research questions and readily accessible and interpretable research products (e.g., technical summaries, open access articles) may encourage practitioners to incorporate climate change science more consistently and efficiently into dam removal decisions. These findings may also translate to other stream restoration efforts to inform knowledge exchange and improve restoration outcomes in a changing climate.
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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 | 0.059 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".