Spatial ecology and conservation of sharks, rays, and chimaeras
Bibliographic record
Abstract
Patterns of biodiversity provide foundational information that can be used to inform conservation prioritization and action. For example, those places harbouring relatively greater numbers of threatened, endemic, or evolutionary distinct species may intersect with threats and conservation actions such as Marine Protected Areas (MPAs) or sustainable fisheries management. Here, I explore patterns of biodiversity, threat, and finally the conservation actions that, if implemented, could improve the status for the world’s threatened marine species. First, I evaluated the contribution of MPAs and governance ability in protecting the world’s threatened marine biodiversity. I found that 74 of the 338 threatened marine species in the database are neither adequately protected by MPAs nor found in countries with higher governance scores. Second, I focused on Class Chondrichthyes as a case study to evaluate the relationships between national landings trajectories and intrinsic ecosystem sensitivity and extrinsic drivers and threats. I found that global decline in Chondrichthyes landings was associated with overfishing, particularly in small tropical diverse ecosystems, rather than with management improvements. Third, I evaluated the degree to which MPAs protected imperilled endemic Chondrichthyan species. I found that only 12 of 99 imperilled endemics have at least 10% of their range overlapping with one or more strictly protected, no-take MPAs. However, over half of the threatened endemic Chondrichthyans can be protected given strategic MPA creation and fisheries management implementation in just 12 countries. Finally, to consider the conservation of a representation of unique assemblages, I delineated the unique shark and ray zoogeographic and phylogenetic regions. Globally, there are 41 zoogeographic and 12 phylogenetic shark and at least 50 and 28 ray regions, respectively. I suggest these regions be the focus for evaluating whether MPAs are ecologically representative. In conclusion, I incorporated biodiversity gradients, MPAs, fisheries management, and socio-economics to inform and improve conservation outcomes for threatened marine biodiversity.
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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".