Quantifying the effects of fragmentation of connectivity networks of deep‐sea vulnerable marine ecosystems
Bibliographic record
Abstract
Abstract Aim Protection of vulnerable marine ecosystems (VMEs) in the high seas has focussed on identifying concentrations of indicator species and prohibiting the operation of bottom‐contact fishing gears where those occur in significant concentrations. Most such species have planktonic larvae and depend on dispersal networks for inter‐generational persistence. Yet, connectivity amongst patches of VME has seldom been considered when spatial management measures are introduced. Here, the relative importance of individual patches for the maintenance of their connectivity networks is evaluated, and a prioritization scheme for management action is proposed. Effective conservation measures should maintain approximately natural network configurations whenever possible. Location Grand Bank and Flemish Cap, Northwest Atlantic Ocean. Methods 3‐D Lagrangian particle tracking was used to model larval dispersal connections between known patches of each of seven groups of benthic invertebrate taxa, previously recognized as indicators of VME. Connectivity networks were constructed and the effects of habitat loss simulated by systematic removal of whole patches, to determine the importance of each patch to connectivity within its respective network. Results The various patches differed widely in their contributions to network connectivity. Each taxon group had both some patches that, if removed from the network, would result in a major decline in connectedness but also several which could be lost with negligible consequences for the remainder. Main Conclusions While protecting each patch of VME has conservation value, the wide variation in connectedness shows that some patches are much more critical than others to the long‐term persistence of the taxa, providing a foundation for prioritization of conservation actions.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".