Migratory marine species: their status, threats and conservation management needs
Bibliographic record
Abstract
ABSTRACT Migratory marine species (MMS) include many of the world's most charismatic organisms such as marine mammals, seabirds, turtles, sharks, and tuna. Many are now among the most threatened due to the diverse range of pressures they encounter during their extensive movements. This paper shows that 21% of MMS are classified as threatened (i.e. categorized as Critically Endangered, Endangered or Vulnerable). Sea turtles are the most threatened group (85%), followed by seabirds (27%), cartilaginous fish (26%), marine mammals (15%) and bony fish (11%). Taken together 48% of MMS are threatened, Near Threatened or Data Deficient. As well as being threatened they share in common being wide‐ranging animals, travelling through the waters of multiple nations as well as in Areas Beyond National Jurisdiction (ABNJ) during different times of the year. This makes their conservation a challenge, requiring coordinated action by many nations, international organizations, Multilateral Environmental Agreements (MEAs) and other stakeholders if their populations are to recover to healthy levels and be safeguarded into the future. Even though they are wide‐ranging, long‐term studies reveal considerable site fidelity and well‐defined habitats for many species and areas. These sites are prime candidates for enhanced management such as via Marine Protect Area (MPA) designations. However, existing management frameworks do not yet contribute sufficiently to MMS conservation, MPA networks need to be expanded to capture key areas, in many cases through the application of new dynamic management techniques such as time area closures. Data on the distribution, abundance, behaviours and threats faced by many MMS are now available. These data should be used to inform the design of effective management regimes, such as MPAs, both within and beyond national jurisdictions. MEAs should ensure a full complement of MMS are included within species listings, and encourage further action to safeguard their populations. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".