Animal migration in the Anthropocene: threats and mitigation options
Bibliographic record
Abstract
Animal migration has fascinated scientists and the public alike for centuries, yet migratory animals are facing diverse threats that could lead to their demise. The Anthropocene is characterised by the reality that humans are the dominant force on Earth, having manifold negative effects on biodiversity and ecosystem function. Considerable research focus has been given to assessing anthropogenic impacts on the numerical abundance of species/populations, whereas relatively less attention has been devoted to animal migration. However, there are clear linkages, for example, where human-driven impacts on migration behaviour can lead to population/species declines or even extinction. Here, we explore anthropogenic threats to migratory animals (in all domains - aquatic, terrestrial, and aerial) using International Union for the Conservation of Nature (IUCN) Threat Taxonomy classifications. We reveal the diverse threats (e.g. human development, disease, invasive species, climate change, exploitation, pollution) that impact migratory wildlife in varied ways spanning taxa, life stages and type of impact (e.g. from direct mortality to changes in behaviour, health, and physiology). Notably, these threats often interact in complex and unpredictable ways to the detriment of wildlife, further complicating management. Fortunately, we are beginning to identify strategies for conserving and managing migratory animals in the Anthropocene. We provide a set of strategies that, if embraced, have the potential to ensure that migratory animals, and the important ecological functions sustained by migration, persist.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".