The rise of a marine generalist predator and the fall of beta diversity
Bibliographic record
Abstract
Determining the importance of physical and biological drivers in shaping biodiversity in diverse ecosystems remains a global challenge. Advancements have been made towards this end in large marine ecosystems with several studies suggesting environmental forcing as the primary driver. However, both empirical and theoretical studies point to additional drivers of changes in diversity involving trophic interactions and, in particular, predation. Moreover, a more integrated but less common approach to the assessment of biodiversity changes involves analyses of spatial β diversity, whereas most studies to date assess only changes in species richness (α diversity). Recent research has established that when cod, a dominant generalist predator, was overfished and collapsed in a northwest Atlantic food web, spatial β diversity increased; that is, the spatial structure of the fish assemblage became increasingly heterogeneous. If cod were to recover, would this situation be reversible, given the inherent complexity and non-linear dynamics that typify such systems? A dramatic increase of cod in an ecologically similar large marine ecosystem may provide an answer. Here we show that spatial β diversity of fish assemblages in the Barents Sea decreased with increasing cod abundance, while decadal scale changes in temperature did not play a significant role. These findings indicate a reversibility of the fish assemblage structure in response to changing levels of an apex predator and highlight the frequently overlooked importance of trophic interactions in determining large-scale biodiversity patterns. As increased cod abundance was largely driven by changes in fisheries management, our study also shows that management policies and practices, particularly those involving apex predators, can have a strong effect in shaping spatial diversity patterns, and one should not restrict the focus to effects of climate change alone.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".