Conservation genetics of South American aquatic mammals: an overview of gene diversity, population structure, phylogeography, non‐invasive methods and forensics
Bibliographic record
Abstract
ABSTRACT Most aquatic mammals have high dispersal potential, and there are often severe conservation concerns related to their legal or illegal harvesting. Therefore, economic, social and forensic factors often arise in decisions relating to their population management. Molecular markers are essential tools in modern conservation genetics, revealing previously unknown aspects of aquatic mammal behaviour, natural history, population structure and demography. Molecular markers also have been used to define management units, to recognize taxonomic units, to conduct forensic analyses and to control illegal wildlife trade, providing valuable information for decision‐making in wildlife conservation and management. We review studies published in peer‐reviewed journals between 1993 and 2010, in which genetic approaches have been applied to conservation‐related issues involving natural populations of 25 species of aquatic mammals in South America. These studies cover just 34% of the 70 aquatic mammal species recorded in South America. Most of the studies are related to population structure, phylogeography, gene flow and dispersal movements. In addition, recent findings relate to evolutionarily significant units, management units, forensics and conservation policy. Finally, we look to the future and, based on numbers of studies and conservation concerns, suggest which species, geographic areas and genetic studies should be prioritized. Moreover, we discuss constraints on research and suggest collaborative works that would provide critical information towards the effective conservation and management of aquatic mammals in South America.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".