Insights into elasmobranchs ecology and baselines for their conservation
Bibliographic record
Abstract
ABSTRACT Elasmobranchs are keystone species whose rapid decline has triggered ecological imbalances across marine ecosystems. Yet, the absence of robust historical baselines and habitat-wide perspectives continues to hinder effective conservation. This thesis addresses these gaps by integrating ecological surveys, environmental DNA (eDNA) metabarcoding, sedimentary ancient eDNA (sedaDNA), and digital PCR (dPCR) assays to reconstruct biodiversity trajectories and investigate ecological baselines for elasmobranchs across two contrasting tropical systems: the Red Sea and the Bahamas. In the Red Sea, where fishing pressure has historically been unmanaged, ecological surveys and integrated expedition data documented previously unreported depth ranges, behaviors, and shed light on species habitat use, while sedaDNA reconstructions revealed a century-scale decline in fish and elasmobranch diversity. These declines coincided with intensified coastal urbanization and fisheries expansion. Functional guild analysis highlighted the erosion of herbivorous and coral-dependent fishes, while species-specific dPCR assays confirmed the long-term decline of key shark species. In contrast, the Bahamas provided evidence of stable or increasing elasmobranch populations, reflecting the effectiveness of proactive management, including a 1993 ban on destructive fishing practices and the 2011 establishment of a national shark sanctuary. This comparison illustrates the decisive role of early policy interventions in preventing irreversible biodiversity losses. Methodological advances were another key contribution of this thesis. By optimizing eDNA metabarcoding protocols (sampling medium, replication, primer sets) and coupling eDNA metabarcoding with dPCR assays, we improved the accuracy of biodiversity detection and extended the temporal scope of reconstructions. Although absolute population abundances cannot be quantified with the methodologies currently available, molecular approaches provide unique access to sedimentary archives, allowing reconstruction of relative declines or recoveries and extending baselines prior to the advent of systematic fisheries records. Together, the findings advance our ability to meet the Kunming–Montreal Global Biodiversity Framework’s Goals A and B and Targets 4 and 5, by grounding conservation in reconstructed historical baselines and applying cutting-edge molecular tools. This thesis demonstrates that elasmobranch conservation success depends on timely action, robust baselines, and the integration of molecular techniques with standard survey methods that can be helpful in reconstructing past ecological baselines and tracking ongoing biodiversity trajectories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".