Modeling nature‐based restoration potential across aquatic–terrestrial boundaries
Bibliographic record
Abstract
Today, few watersheds remain untouched by global change processes arising from climate warming, impoundments, channelization, water extraction, pollution, and urbanization. The need for restoration has resulted in a myriad of interventions, generally performed at small scales, which have limited measurable impact in restoring biodiversity and ecosystem functions. We propose bringing nature-based restoration (also referred to as rewilding) principles to rivers and their watersheds to allow freshwater ecosystems to heal themselves and present a case study example for the Wolastoq, a transboundary watershed on North America's east coast. We aimed to identify key areas for the provision of the ecosystem function secondary productivity in the watershed and explored how the existing network of protected lands contributes to its conservation. We first developed species distribution models for 94 aquatic insects and 5 aerial insectivores and then considered human footprint and existing protected areas when employing spatial prioritization to meet 2 area-based targets (17% and 30% [i.e., Aichi Biodiversity Target 11 and Canada's 30×30, respectively]) for conservation or restoration of freshwater secondary production. Current conservation protection in the watershed was predicted to be insufficient to protect either ecosystem function providers or receivers of secondary production. By considering integrated conservation strategies, restoration and conservation actions can be better allocated throughout habitat patches to ensure sustained provision of ecosystem functions across the watershed. Nature-based restoration and conservation can help inform Canada's area-based targets, providing a framework for incorporating ecosystem functions into conservation planning and offering practical insights for policy and restoration efforts aimed at safeguarding biodiversity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".