Toward a new framework for restoring lost wildlife migrations
Bibliographic record
Abstract
Abstract Global declines in wildlife migrations have prompted new initiatives to conserve remaining migratory behaviors. However, many migrations have already been lost. Important attempts have been made to recover extirpated migrations, and our understanding of restoration remains narrowly confined to these particular species and landscapes. Here, we examine diverse restoration efforts through the unifying lens of behavioral ecology to draw broader inferences regarding the feasibility and effectiveness of restoring lost migrations. First, we synthesize recent research advances that illuminate key roles of exploration, learning, and adaptation in migratory behavior. Then, we review case studies to identify common themes of restoration success across four major vertebrate groups: fish, birds, mammals, and herpetofauna. We describe three broad strategies to effectively restore lost migrations: reestablishing migratory populations, recovering migratory habitats, and reviving migratory behavior itself. To guide conservation and research efforts, we link these strategies with specific management techniques, and we explore the biological mechanisms underpinning the success of each. Our work reveals a previously underappreciated potential for restoring lost migrations in terrestrial and freshwater vertebrates, and it provides guidance on whether and how conservation practitioners, researchers, and policymakers can work to restore the valuable migrations we have lost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".